hana end to end overview

8/11/2019 HANA End to End Overview

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Special Report: Implementing SAP HANA, an End-to-EndPerspectiveIn this exclusive special report, get an in-depth, step-by-step look at the aspects of implementing BIsolutions on SAP HANA. Gain insight into how ETL integrates with SAP HANA and how SAPBusinessObjects BI 4.0 analyzes and visualizes the data stored in SAP HANA.

Key Concept

SAP HANA modelingis a process whereby a developer converts raw columnar tables into business-centric

logical views, such as dimensions and measures. The result lets business consumers find their data elements,

group by business elements, and filter and sort data. There are seven components behind SAP HANA

modeling, each with its own function.

When you take a moment to think back to all the technical innovations that have occurred during the last 30

years, several thoughts come to mind. There was the invention of the Nintendo game console. In todays

standards, it is not a technical wonder but it did lead to the birth of a new market that paved the way for all the

amazing game consoles and personal gaming devices that exist today. There was the invention of the Internet,

which helped to essentially change the way we humans shop, communicate, share information, and

collaborate. There was the invention of the smartphone, a device that put the power of the Internet in the palm

of our hand in virtually every city in the world.

Just imagine for a second what life would be like if companies such as AOL, Apple, and Nintendo lacked the

ability to develop these products and bring these technical wonders to market. Technical innovation is

something that we have all come to expect, but how does one recognize when innovation will lead tofundamental change?

For those of us that have been working in the business intelligence (BI) arena for the past decade, the

limitations of interacting with large quantities of data at speeds that were acceptable to business users has

been a real challenge. The relational database technologies that had been a core component of our strategies

were reaching a point of diminishing return. No real innovation was being introduced by the main database

vendorsor at least innovation that offered major performance change. In large part, that was due to their

need to support legacy solutions while attempting to provide perceived enhancements. Their strategy for

innovation was slow and continually centered around the use of inefficient and increasingly expensive magneticstorage arrays.

In the meantime, SAP was struggling to find a solution to help its customers solve the ever decreasing

performance issues associated with managing large volumes of SAP application data. In 2008, SAP began

working on a pilot project to prove that the basic mechanisms and processes of a database could be re-

developed, leveraging RAM and multi-core CPUs in a way that would revolutionize the capabilities of BI,


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analytics, and complex data processing. Based on first impressions and more than a year of experience

working with SAP HANA, we believe SAP has developed an innovative data platform that will lead to

revolutionary changes in BI and beyond.

SAP HANA is a merger of software, hardware, and creative ideas that afforded SAP the opportunity to rethinkthe database platform. Because SAP had the opportunity to develop this technology from the ground up,

without the constraints of the legacy relational database management system (RDBMS) vendors, innovation

was an inevitable result of its efforts. Hardware had evolved to a state where RAM could be addressed in

terabytes and CPU cores could be numbered in the hundreds, all within a single blade chaise or server rack.

When you combine this with SAP HANAs ability to compress data in-memory, organizations had a viable

solution for managing 40 to 120 terabytes of data on a platform that could produce query results so quickly that

many questioned if what they were seeing was a hoax.

Will SAP HANA lead to fundamental change? In some regards we are already seeing other database vendors

update their solutions to be more like SAP HANA. For organizations that have already adopted SAP HANA,

there is no question that it has changed the capabilities of analytics and data processing. Only time will be the

true judge of SAP HANA, but all indications are that SAP has developed a solution that will lead BI into the next

generation.

When organizations look to develop solutions on SAP HANA, there are three ways you can categorize the

available solutions:

The first way that organizations can use SAP HANA, while leveraging their investments in traditional

SAP BI solutions, involves moving their BW environment, based on a legacy database, to SAPNetWeaver BW powered by SAP HANA.

The second broad category of solutions can be characterized as rapid solutions based on a specificindustry, business process, or line of business.

The final category and the main focus of this report pertains to the ways organizations can use theSAP HANA database by moving data from multiple sources, in either batch or real time, into the SAPHANA in-memory database. In general terms, we label this final solution SAP HANA standalone.

For organizations that have years of experience and knowledge invested in the SAP NetWeaver BW platform,

SAP NetWeaver BW powered by SAP HANA will prove to be the most straightforward and cost-effective SAP

HANA-based solution available. Organizations will experience very few process or procedure changes with this

solution. This is due to the fact that primarily only the underlying relational database that powers SAP

NetWeaver BW 7.3 will change.

However, there are specific optimizations whereby DataStore objects (DSOs) and InfoCubes can be converted

to in-memory optimized versions. Under the covers, there are also several optimizations within the code that

effectively push down processes that were previously handled at the application layer to the SAP HANA


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database. The net result is a substantial reduction in database storage requirements and query response

times.

The SAP Web site (http://www.sap.com/solutions/technology/in-memory-computing-

platform/hana/overview/index.epx)has a long list of prebuilt or rapid accelerated solutions designedspecifically to use SAP HANA. Each solution is tailored for a specific business process or line of business or

industry. The list includes, but is not limited to, SAP CO-PA Accelerator, SAP Finance and Controlling

Accelerator, SAP Smart Meter Analytics, and SAP Sales Pipeline Analysis. As of October 2012, you can find

just over 20 solutions available, but you should expect to see the list grow as SAP and its partners find

innovative ways to use SAP HANA.

The final category of solutions centers on SAP HANA standalone in-memory database. Those that have blazed

the trail with traditional Enterprise Information Management (EIM) solutions will find the most comfort with this

category. The solution includes the use of SAP BusinessObjects 4.0, SAP Data Services 4.0, and SAP HANA.

SAP Data Services 4.0 provides all the features needed to support enterprise level data management. SAP

Data Services is a proven tool for managing all aspects of EIM. It is used by thousands of companies to extract,

cleanse, translate, model, and load data into data warehouses and data marts. With the release of version 4.x,

it is tightly integrated with SAP HANA while maintaining support for almost every popular legacy RDBMS and

business application on the market. In short, it is an excellent tool for extracting data from both SAP and non-

SAP based sources. SAP HANA will serve as the engine for storing, aggregating, calculating, filtering, and

forecasting the data loaded into its columnar or row store in-memory tables. BusinessObjects 4.0 provides the

tools needed to analyze and visualize the data stored in SAP HANA. It includes a Swiss army knife of tools that

all have well defined mechanisms to connect to the data on SAP HANA.

As you continue to read this special report, we will walk you through all the aspects of implementing BI

solutions on SAP HANA standalone using SAP Data Services to manage and load data, SAP Information

Steward to profile and research data issues, SAP HANA to develop and manage multi-dimensional models,

and the SAP BusinessObjects suite of tools to create mobile analytics, reports, and dashboards.

For those reading this special report with little or no experience using SAP BusinessObjects or SAP Data

Services, we hope to provide insight into how companies and Decision First Technologies have implementedsuccessful solutions for over a decade using SAP BusinessObjects EIM and analytic best practices. For those

looking to find more information on creating multi-dimensional models in SAP HANA, this special report will

also provide you with valuable insight into that world.

Managing SAP HANA with a Proper Data Model
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SAP HANA provides such a powerful in-memory data platform that much more information is available at

speeds never seen before. This is why managing information appropriately is more important than ever before.

SAP HANA in a standalone configuration is truly a blank slate. There are no tables, no models, no views, and

no data. You must not only get your data into SAP HANA but also plan and design the structures and strategy

to house your data. In this portion of the special report, we focus on managing data effectively using proper

data modeling techniques, profiling and examining data with SAP Information Steward, and finally loading data

into SAP HANA using SAP Data Services.

Start with a Good Data Model for SAP HANA

Data modeling in SAP HANA is quite similar to traditional data modeling with some subtle differences. Data

must be modeled into efficient structures that take fu ll advantage of SAP HANAs in-memory structure and

analytic modeling capabilities before presenting the data to reporting tools such as SAP BI BusinessObjects

4.0. In certain cases this deviates from traditional data modeling techniques.

Traditionally, star schemas have been used as the backbone of BI design, and this approach also works well

as a baseline data model for SAP HANA. With a traditional RDBMS, your data is modeled into a star schema

consisting of fact tables with measures and dimension tables with attributes to describe the data. Notice in

Figure 1that the Fact_Sales table has measures of units sold with foreign keys to the dimension tables:

Dim_Date, Dim_Product, and Dim_Store. Data structured in this manner performs quickly and efficiently when

joined in queries and presented to reporting tools.

Figure 1

Typical star schema example with one fact table and multiple dimensions


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This is certainly a good starting structure for an SAP HANA data model, with a couple of exceptions. SAP

HANA stores data either in rows or in a columnar format, so this degree of normalization is not always

necessary or even beneficial for certain types of queries. Both in our lab at Decision First Technologies or at

clients, we have seen better performance in some situations with SAP HANA by denormalizing or flattening

data in certain fact tables when this flattened data is stored in column store tables.

When data is stored in a columnar table, the repeating data has a greater likelihood to be only stored once

using run-length encoding. With this method, the values are sorted and the repeating values have a greater

likelihood of being sorted together as run-length encoding counts the number of consecutive column elements

with the same values. If the values are the same values, only one instance is stored.

This is achieved by actually storing column data using two columns: one for the values as they appear in the

table and another for a count of the use of those values. This encoding method yields good compression and

the query response times are often better querying this type of structure with repeating data stored in a

columnar table over data stored in relational row tables. For example, in our tests both on client sites and in the

Decision First lab, we have seen anywhere from six times to 16 times compression over traditional RDBMS

structures, and the performance has been no less than incredible.

Another reason to stray from the traditional normalized approach over star schemas in SAP HANA for BI

applications is due to join cost. Specifically, the join cost of including range-based operations from the two

relational tables in the row engine is expensive due to the intermediate data being transferred from a columnar

engine to a row engine. These types of analysis are not available in the columnar engine, so they must occur in

the row engine. You then get a performance cost for joining the data that is referred to as join cost.

This repositioning of data at query runtime from the columnar engine to the row engine makes these types of

operations much more costly from a performance standpoint. Take the star schema example in Figure 1. This

is optimized for RDBMS structures, which work fine in SAP HANA. However, the cost in performance of joining

the two tables DIM_Date and Fact_Sales when running the following query is much greater when the heavy

lifting is not performed by the column engine.

These are the kinds of decisions you must consider when modeling data for storage in SAP HANA. In some

cases it makes sense to move from a traditional star schema modeling technique toward columnar modeling byusing columnar functions available in SAP HANA. Take the example in Figure 2showing a typical star schema

join between a sales fact table and a date dimension table.


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Figure 2

Typical star schema join on sales and date

If the query were revised to use the SAP HANA EXTRACT function as shown in Figure 3, which is natively

supported in the columnar engine in SAP HANA, you could avoid the join cost altogether by using a lightning

fast EXTRACT function to derive the necessary date values in real time rather than joining.

Figure 3

Using the columnar engine function EXTRACT to increase sales and date join performance

The query results come faster by eliminating a whole transfer step, with the processing occurring at the more

efficient column engine in-memory using a built-in native SAP HANA function. This type of thinking is what

fosters a discussion and a change in modeling data. This leads to the final topic to consider when modeling

your data in SAP HANA: Cardinality.

Simply put, cardinality refers to the uniqueness of data in a column or attribute of a table. There are three types

of cardinality: high, normal, and low. Most columns that have high cardinality are unique in their content. For

example, IDs are primary keys that are unique and have high cardinality. However, state values repeat in an

address table (Figure 4).


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Figure 4

Examples of high, normal, and low data cardinality

All new records in the Address table receive a new AddressID. This makes AddressID completely unique. Low

cardinality is essentially the opposite and this refers to columns containing values that almost completely

repeat. State data provides good examples of low cardinality, and are typically carved off, or normalized, into

separate tables as the foreign key column StateProvinceID in the Address table shows in Figure 4. Normal

cardinality refers to columns with values that are somewhat uncommon. Take shipping address values that

relate to SalesOrderHeader records. Sales orders will most likely be shipped multiple times to the same

address for the same customers, so there will likely be some repetition of these values in the

SalesOrderHeader table.

This is why in a traditional data model, the structure looks as it does in Figure 4. The address records would

exist in a normalized structure with an Address table with a foreign key to SalesOrderHeader. Both low and

normal cardinality conform to this modeling technique for traditional RDBMS databases, but this is entirely the

wrong approach for loading data into SAP HANA.


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Again, you must consider the join cost of reassembling the information at query runtime versus a more natural

structure for a columnar engine. A more efficient data model for SAP HANA is shown in Figure 5. It merges

both Address and State information with SalesOrderHeader and with SalesOrderDetail data to create one table

in SAP HANA.

Figure 5

A merged sales table containing both address and state data in SAP HANA

One thing to notice in Figure 5, aside from the denormalized data, is the use of float Column Store Data Type

for all the amount fields. Normally, decimal data types would be used for their precision, but float data types

accommodate a behavior that is unique to SAP HANA. SAP HANA requires the data type of the base column

values to be able to cover or support the maximum value in size and precision of the data as it is rendered in

aggregate operations.

This is especially important as the values of the datasets grow in size. For example a decimal (19, 4) data type

at the individual record level in a table is fine, but as the aggregation of a recordset grows, the growth produces

overflow errors that a decimal (19, 4) does not cover. So, you guard against this unique behavior by using

floats for commonly calculated values, such as amount fields in base tables.


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This fact table is a poor choice for a traditional data model, as a traditional approach dictates multiple

structures, and the join cost in a traditional RDBMS is helped greatly by providing indexes at all join points.

However, in SAP HANA, the compression achieved by the column storage structure as described before

performs better than taking the time to join the separate tables in the row engine. The compression achieved by

a Column Store table negates the gains of a traditional normalized structure.

We have discussed numerous examples of ways to model data and are almost ready to load the data and

create these structures in SAP HANA using SAP Data Services 4.0. However, by not profiling the source data

first, you may miss aspects of the data that could compromise the quality of your data. The last thing that you

want in SAP HANA is really fast bad data, so you can ensure quality with data profiling in SAP Information

Stewards Data Insight.

Profiling Data with SAP Information Stewards Data Insight

SAP Information Stewards Data Insight is a tool for quickly ascertaining a grand amount of information from

both data source tables and target tables. There are many profiling capabilities including columns, addresses,

dependency, redundancy, and uniqueness. Data Insight also has the capability to measure and record data

quality over time by creating scorecards that are fully configurable to measure quality aspects that are

important to each individual companys business. It is important to note that Data Insight is only one application

in SAP Information Steward. For the scope of this special report, we limit our focus to the profiling capabilities

of Data Insight.

Upon logging into SAP Information Steward, you land at the main application screen with the Data Insight

application tab in focus, as seen in Figure 6. For the purpose of this special report, we have created both a

project called HANA_Source and a connection to the source SQL Server database within this project.

Figure 6


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Data Insight application on SAP Information Stewards main screen

With regards to profile tables in this project, you click the project to launch the Workspace home screen, which

is where you set up and run the profiling tasks against the tables. In our example stated earlier for SAP HANA,

we are loading both customer and address data with our sales data, so we need to take care and ensure that

addresses are good, verified United States Postal Services (USPS) addresses and that customer and address

data all have good quality before loading it into SAP HANA.

To set up the column profile task, select the tables Address and Contact in the Workspace Home application

tab. Select Columns for the profiling task from the pull-down menu as shown in Figure 7. After clicking

Columns, you are prompted to click Save and Run Now. This executes the profiling job on the SAP Information

Steward server, and the profile job runs the profile against the database tables. This is really all that you need

to do to engage a column profile task.

Figure 7

Select the tables to profile in the Workspace Home and Columns from the pull-down menu

This takes care of column profiling, so now we now turn our focus to address data. SAP Information Steward

has the unique capability to run address profiling tasks using USPS validated directories. It gives you

information about your address data quickly with just a few clicks and field settings. You can determine if an

address in a record is a valid, deliverable address, if an address in a record is correctable using the Data

Quality Management transforms in SAP Data Services, or if an address in a record is invalid and uncorrectable.

A correctable address means that according to the profile result, SAP Data Services has enough information


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available in the input record to a data quality job to adequately fix the address to ensure that it is deliverable by

the USPS. All this is done with no coding using SAP Information Steward. Before this tool, that task was

impossible.

To perform the address profile, select the Address table and Addresses from the Profile pull-down menu asrepresented in Figure 7. This launches the Define Addresses Task window as shown in Figure 8. Using this

screen, you assign or map the fields from your database table that correspond to the field mappings shown in

the Define Address Task screen. In our example table for the Address1 field in SAP Information Steward, we

have an AddressLine1 field. For Address2 we have AddressLine2 in the database. Locality1-3 in SAP

Information Steward refers to the city information and Region refers to state information, so those map to City

and PostalCode fields, respectively. PostalCode is the Zip code field and a PostalCode field maps to this

information. Upon filling out this form, you again click the Save and Run Now button to submit the address

profiling task.

Figure 8


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Map address attributes and click Save and Run Now

After the tasks finish in Information Steward, you have a lot of information about your source tables for the Data

Services job. It helps fix data quality issues in your code before the data is presented to the data model that

you have set out to establish in SAP HANA. Lets consider the results of the column profile in Figure 9.

Figure 9

Results of the Data Insight Column Profile task


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You can see from the results of this column profile task in Figure 9that you have some work to do on the data

before loading it into SAP HANA. There are some issues with names. It appears that some have been entered

in upper case as indicated in the Value column by Xxxxxx and some in lower case as indicated in the Value

column as xxxxxfor example, the record of gomez. You need to standardize all of the names on proper or

mixed case as well as run them through data cleansing transforms before loading them into SAP HANA.

Looking at the address profile results in Figure 10it appears that you should cleanse the addresses as there

are quite a few correctable addresses that the Address_Cleanse transforms in SAP Data Services can fix.

These are valuable repairs before you load the data for further presentation in SAP HANA. You are now ready

to begin building your code in SAP Data Services to both build tables and load data into the model youve

designed in SAP HANA.

Figure 10

Results of the Data Insight Address Profile task

Loading Data into SAP HANA using SAP Data Services 4.0

After seeing the trouble that can arise from faulty addresses and faulty names, you are ready to craft both the

FACT_Sales_Order_Detail table structure that was presented in the data modeling section of the special report

in Figure 5and to load data into that structure. SAP Data Services is the only certified solution to load third-

party data into SAP HANA, and this is our vehicle for data loads. You can quickly create both row- and column-

based tables in SAP HANA, thus both building and loading the model laid out in the examples above. To


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accomplish this, you first need to create Datastore connections to the source SQL Server database and the

target SAP HANA system.

Open the SAP Data Services Designer and browse to the Datastores tab in the Local Object Library on the

bottom left portion of the screen. Right-click the white space to bring up the pop-up menu shown in Figure 11.Click New on the pop-up menu to launch the Create New Datastore configuration screen.

Figure 11

Click New to create Datastore connections to both the SQL Server source and SAP HANA target

In the Create New Datastore screen, you specify the settings as shown in Figure 12. Notice the ODBC Admin

button on the screen. You need to create an ODBC connection to SAP HANA if you have not done so already.

This is a standard ODBC connection just like any other data source using Windows Data Sources (ODBC) in

the control panel in Windows. The only thing slightly different is that you use the SAP HANA ODBC driver

shipped with SAP HANA over a standard, Windows-supplied ODBC generic driver. This is similar to using an

IBM ODBC driver to set up an IBM DB2 connection much like other databases that are supported in SAP Data

Services as ODBC connections. The SAP HANA ODBC driver is installed on the machine hosting the SAP

Data Services job server.


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Figure 12

Specifying new Datastore connection settings

You now have your Datastores created and have established connections to the Microsoft SQL Server source

database and the SAP HANA target system. All the components in SAP Data Services are ready to create the

data flows necessary to build the FACT_SALES_ORDER_DETAIL table in SAP HANA.

However, it would not be wise to go directly from the source to the structure laid out in the data modeling

section of this special report. What if you choose to include other data sources in your well-modeled Sales

Order Header fact table in the future? By going straight from the source to SAP HANA, you are to use the

primary key from the source table as well as just taking the fields as they are in the source. Usually, this is not

desired in a reporting data structure.

Dimensionally modeled star schema data marts or data warehouses should be divorced from the source and

contain source-agnostic columns that represent business definitions and have source-agnostic primary and

foreign key structures. The way to achieve a divorced storage structure is to use a staging database and create

a surrogate (source-agnostic) primary key with a link back to the source primary key. To do this, you model a

staging layer in SQL Server into your Data Services process before moving data or creating structures in SAP

HANA. Follow these steps to model a staging layer.


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Step 1. Create Staging with Surrogate Keys

Staging serves two functions in your load to SAP HANA. First, it divorces the source-primary key structure with

the keys that you create while loading to SAP HANA. This allows you to easily integrate other data sources in

the future.

The second function of staging is to do all the manipulation or transformation of the data necessary to deal with

the issues that were found earlier in profiling using SAP Information Steward. To do this, you use SAP Data

Services to create a table called SALES_ORDER_DETAIL_STAGE. It has flattened or denormalized data from

the following tables in your source database: SalesOrderHeader, Address, StateProvince, and

SalesOrderHeader. The data in these tables will be merged into the target table to take advantage of the

unique columnar engine properties of SAP HANA. This type of data structure performs better and serves as a

proper foundation to properly exploit the analytic modeling capabilities of SAP HANA. The fully realized data

flow is depicted in Figure 13.

Figure 13

Create a SALES_ORDER_DETAIL_STAGE staging table

Whats inside the data flow components? The first thing that the data flow does is to join four disparate tables

from the source database in the query transform labeled Query in Figure 13. You can see in Figure 14how

the joins are accomplished in SAP Data Services in the FROM tab of the query transform.


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Figure 14

Join all tables together in the query transform of the data flow DF_SALES_DETAIL_SG_I

Take note in Figure 14that the native date fields from the source will be transformed in these data flows to

varchar() fields and the format of the field should be YYYYMMDD. This means a date field in the OrderDate

source table would look like 09/01/2012 11:59:59, but in the staging table or in SAP HANA, you want the date

field to look like 20120901. The reason for this is that an SAP system contains sophisticated built-in date

handling functionality that we explore in the next section (analytic modeling) of this special report. This

varchar() format is what is required to take advantage of that functionality.

One last thing that is happening in the query transform in Figure 14is that the first field, SALES_ORDER

DETAIL_ID, has a gen_row_num() function in the Mapping column of the query transform. This is the surrogate

key as the gen_row_num() function generates a row number for each record. The source table key

SalesOrderID will also be mapped to the target table so this staging table, SALES_ORDER_DETAIL_STAGE,

will contain both the surrogate key as well as the source primary key. This table provides the link of the ultimate

fact table in SAP HANA back to the source table.

Eventually, when you wish to add more sources to the fact table in SAP HANA, you just map the attributes

appropriately to this staging table and add the new sources primary key column as a new column in thestaging table. The other fields signify the business terms, not a direct link to any source. Take, for example, the

OrderDate field. An OrderDate is an abstracted business concept now. It is no longer just a linked field to the

source. The OrderDate stands source independent and represents an OrderDate business concept outside of

just coming from this source. This concept is agnostic to the source and can be used independently to describe

any OrderDate from any source. A new source has a new order date field that is mapped to this OrderDate field

in the SALES_ORDER_DETAIL_STAGE table. Therefore, all the other attribute fields, such as OrderDate, are


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reused with the new source. It is the primary keyspresence, along with the surrogate key, that provides the

link back to any source table. This is the primary reason for taking the time to craft a staging layer for your load

to SAP HANA.

Another issue that arose in the data profiling is the validity of the addresses. You can use theUSARegulatory_AddressCleanse transform in your data flow DF_SALES_DETAIL_SG_I (as shown in Figure

13) to correct the addresses. The address cleansing transforms are found in the Local Object Library under the

Data Quality node as shown in Figure 15.

Figure 15

Where to find the USARegulatory_AddressCleanse transform

After placing the USARegulatory_AddressCleanse transform in the data flow, you configure both the input and

output fields within the transform. The input fields map to the existing address fields coming from the source

tables through the query transform. The address cleanse transform takes these field inputs and analyzes and

corrects the physically stored addresses using SAP-supplied postal address directory files updated by the

USPS. By using SAP Information Steward to quickly identify the address records to correct, you are able to use

the address cleansing capabilities of SAP Data Services to effectively cleanse your records in the staging

database.

Now that you have your staging table SALES_ORDER_DETAIL_STAGE correctly populated, this table can link

you back to the various sources that will be loaded over time. You are now ready to load the data to SAP

HANA.

Step 2. Move Data into SAP HANA and Create All Tables at Runtime in SAP DataServices


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You have performed most of the heavy lifting in the staging data movements, and the load to SAP HANA is

straightforward. You are essentially going to take your staging tables as a template, use the template table

functionality within SAP Data Services to quickly create table structures, and load the data into SAP HANA.

Template tables are handy tools. They take any recordset and craft a create table SQL statement against the

target database. As soon as you have the structure for the table exactly as you wish, you can select a template

table as the target for your data flow, as shown in Figure 16. The table structure will be created in SAP HANA

at data flow runtime. After executing the Job_HANA_Load SAP Data Services job to run your

DF_FACT_SALES_DETAIL data flow, you now have your table structure created in SAP HANA.

Figure 16Completed data flow in SAP Data Services to load the sales order detail into SAP HANA

The template table is a great way to quickly create the structure of the table in SAP HANA, but it may not

perform as well as bulk loading data using SAP HANAs bulk loader. This is particularly important if you are

loading a large table with millions of records. Smaller tables can stop at this point and use the template table to

create the table structure and load the data, but with a larger table, such as FACT_SALES_ORDER_DETAIL,

you probably want to explore the bulk loader options available from SAP HANA. To use the bulk loader

capabilities within SAP Data Services, import the table into SAP Data Services as a standard table. To do this,

right-click the template table in the Local Object Library that was created by running the job and data flow. Thenthe popup menu in Figure 17 appears.


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Figure 17

Import the table in Data Services to get standard table full functionality

After importing the table, you are free to set commit sizes or use the bulk loader by double-clicking the

FACT_SALES_ORDER_DETAIL target table. This brings up the target table editor screen, in which you can

specify many things about the load of the large FACT_SALES_ORDER_DETAIL table (Figure 18). Since you

know this table is large, use the Bulk Load Options tab to control the maximum bind array size. Set it to

1,000,000 rows. This is a practical starting value that we have used with good results in our Decision First lab.

The maximum bind array value acts like a commit size control in other target databases and batches the

records together into larger groups for performance in large loading operations.


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Figure 18

Use the target table editor to control the maximum bind array size

After carefully crafting your SAP Data Services job and data flows to load the FACT_SALES_ORDER_DETAIL

table in SAP HANA, the only thing left to do is execute the job. Navigate to the Project Area in Designer as

shown in the top left of corner of Figure 15. Right-click the job name, and select Execute Job from the pop-up

menu. With data extracted, cleansed, and loaded into a series of SAP HANA columnar tables, you can now

begin the process of developing multi-dimensional models or views based on those tables.

SAP HANA Modeling Process

SAP HANA modeling is a process whereby a developer converts the raw columnar tables into business-centric

logical views. Much like the process in which a legacy BusinessObjects customer would define a universe

based on relation tables, modeling within SAP HANA allows for columns of data to be defined as dimensions

and measures. The result presents the data in a format that is more business intuitive, granting consumers an

easy catalog to find their data elements, group by business elements, and filter and sort data.

There are seven main components to SAP HANA modeling. Each component has a specific purpose and

function. When these components are compiled together, the result provides a meaningful multi-dimensional

representation of the data. The main components of modeling are the following:

SAP HANA Studio

Schemas


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Packages

Attribute views

Analytic views

Calculation views

Analytic privileges

Lets look at each component inmore detail.

SAP HANA Studio

SAP HANA Studio (Figure 19) is a Java-based client tool that allows developers and administrators to create

models and manage the SAP HANA RDBMS. It is typically installed on a developers desktop and it is the basis

for developing rich, multi-dimensional models that are consumed by the various supported SAP

BusinessObjects 4.0 reporting tools. It also contains a subset of tools for the SAP HANA database

administrator (DBA). Developers use the interface to create packages, attribute views, analytic views, databaseviews, calculation views, and analytic privileges. DBAs use the interface to manage security, roles, backups,

tables, and views and to monitor the system.

Figure 19

SAP HANA Studio

Schemas

Schemas (Figure 20) are directly associated with user accounts created by the SAP HANA DBA and are used

to store row and columnar tables. There are also other objects that are stored in an SAP HANA schema,


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including views and procedures. For each user created by the DBA or default to the system, a schema space

exists that must be referenced when working with tables in SAP HANA. The term schema is not unique to SAP

HANA. Almost every RDBMS on the market incorporates this term per the schema modification standards set

by the American National Standards Institute.

Note

Schemas are secured in SAP HANA, so it is important that the developers account and _SYS_BIC (system

account for managing SAP HANA models) have been granted the SELECT rights before models can be

developed or activated in SAP HANA Studio.

Figure 20

Schemas

When you create a table using SQL syntax in the SAP HANA Studio, you must reference the schema in the

CREATE TABLE and DROP TABLE commands. The syntax of every table-related function always references

the schema name (Figure 21).

Figure 21

CREATE TABLE and DROP TABLE commands


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Packages

Packages are the first logic storage component of an SAP HANA model. Within a package you define one or

more attribute views, analytic views, calculation views, or analytic privileges. Packages can be created in a

hierarchical order for the purposes of security and logic ordering of components (Figure 22).

Figure 22

Package hierarchies

When you create your first package, you can give it a name, such as Sales. Subsequent packages can be

created using the . naming convention. In Figure 23, we created a sub

package named northamerica. Because we wanted this package to exist under the sales package, we named it

sales.northamerica. The dot or period in the name indicates that the package should be created as a child to

the parent package sales. Creating a hierarchical package structure is important for both organization of

modeling objects and for securing objects within packages.

Figure 23

Creating a package

Attribute Views


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Attribute views are the logical dimension and hierarchy containers within an SAP HANA model. SAP HANA

Studio allows you to create them by joining and filtering tables found in SAP HANA schemas. Attribute views

are not required for an SAP HANA model, but before you can create an analytic view containing hierarchies,

you must first create an attribute view. The end result of an attribute view appears to be a single logic table or

view of data.

Attribute views allow the developer to denormalize data by joining one or more tables, filtering one or more

tables, or by developing calculated attributes. Imagine you are developing a SQL View based on three tables

that will result in a record set that contains all the information about customers who placed a sales order. Within

this attribute view you likely join tables such as Customer, Address, and Account. You can also filter the

Customer table so that only active customer records are returned. The end result is a single, logical view of

these tables that returns all the relevant customer information in a single unique row (Figure 24).

Figure 24

Components of an attribute view

There are two main tabs within the interface that developers use to create an attribute view. The Data

Foundation tab is used to define the joins, keys, and filters needed to create a complete attribute view. The

Hierarchies tab is used to define hierarchies that are available to some of the SAP BusinessObjects reporting

tools.

The Data Foundation tab of the attribute view allows developers to denormalize a data set using joins, filters,

and calculated attributes. The joins are defined as inner, left outer, right outer, referential, or text. If the

developer right-clicks any column in a data foundation table, the user interface (UI) presents the option to


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create a filter. A filter at the foundation level is permanently applied to the results sets and should only be used

to remove records based on technical or business requirements.

On the right side of the Data Foundation tab are the output columns. These columns are added by right-clicking

a column within a table found on the Data Foundation tab. On the right-click menu, there is an option to Add asAttribute. Any value available on the output window is accessible anywhere the completed and activated

attribute view is used.

Another option available on the output windows is the derived column. You can derive attribute columns using

the calculated attribute option. This useful feature allows developers to derive columns to support various

reporting requirements (Figure 25). For example, you could concatenate the customers last and first name

separated by a column. You can also use the if() and now() function and CUSTOMER_EFFECTIVE_DATE

field to create a calculated column that flags customers that have more than five years of history with your

company.

Figure 25


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Calculated attribute

When you define an attribute view, you select one or more columns and establish the attribute key (Figure 26).

The attribute key is the basis for joining the attribute to an analytic foundation, which we discuss in more detail

later. Developers can find the option to add an attribute key by right-clicking the table in the data foundation

and selecting Add as a key attribute. It is important that the values for this column be truly unique in results. In

traditional data modeling, developers define a primary key that signifies that all records are unique based on

the column or columns defined as a primary. The same is true with an attribute view. When the attributes are

joined within an analytic view, each record must be unique to prevent the duplication of records and

subsequent over-aggregation of data.

Figure 26

Components of an attribute view

Within an attribute view, developers can create hierarchies that can be directly used by tools, such as SAP

BusinessObjects Analysis for Office and BusinessObjects Analysis for OLAP. Developers can find this option

by clicking the Hierarchies tab (Figure 27). In future releases of SAP BusinessObjects 4.0, these hierarchies

will also be accessible by SAP BusinessObjects Web Intelligence (also known as WebI) and possibly SAP

BusinessObjects Crystal Reports for Enterprise via direct binding to SAP HANA analytic views. Hierarchies add

a logic order to data ranging from a narrow to a broad category.


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Figure 27

Attribute hierarchies

Hierarchies are useful when reporting needs require expand and collapse functionality for displaying key

performance indicators and other measures. In Figure 28, you can see that the AccountNumber column

contains a + sign, which indicates that there are child objects available. In almost every line of business, you

will find hierarchies that are useful for analyzing measures or key figures.

Figure 28


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SAP BusinessObjects Analysis for OLAP

There are four main options available when creating an attribute view in SAP HANA Studio (Figure 29):

The standard attribute view type is just as the name implies. This is the type of attribute viewdevelopers choose when creating or deriving attributes based on existing tables stored in SAP HANA.

Time-based attributes are derived based on pre-loaded date and time tables maintained by the SAPHANA system. When you create a time-based attribute, you have the option to establish the calendartype, variant table, and granularity. Time-based attributes are handy because they eliminate the needfor an external tool to load and manage date and time tables.

Developers use the derived attribute type to create aliases of existing attribute views. They are handywhen your analytic foundation contains multiple foreign keys for various dates or times. For example, atypical sales_order_detail table likely contains three columns that represent the order_date, ship_date,and due_date. Each of the three columns contains a unique date that will be joined in that analyticfoundation to three different date-based attributes. If you attempt to join all three columns to the sametime-based attribute, you create a logic loop. The results of your model then only display transactionsin which the order_date, ship_date, and due_date all occur on the same day. To overcome this issue,you must create a derived attribute based on an existing date-based attribute for each expected date

key in your analytic foundation. Derived attributes are permanently fixed to their parent attribute. Everychange made to the parent automatically is reflected in each child-derived attribute and associatedanalytic view. Developers find them efficient when an attribute view alias is required.

The final option when creating an attribute view is the use of the copy from option. This is differentfrom the derived attributes in that a physical copy of an existing attribute view is created. The copy willhave no further association with its parent once the copy process is complete. This is typically usedwhen a developer wants to rename an existing attribute view without affecting the overall model.


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Figure 29

Attribute view options

Regardless of the type of attribute view you select, each attribute view is used within one or more analytic

views to complete a multi-dimensional model. Once you have completed the design of your attribute view, click

the save and activate icon to commit its definition to the metadata repository of SAP HANA (Figure 30).


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Figure 30

Save and activate your attribute view

Analytic Views

Analytic views are the heart of SAP HANAs multi-dimensional models. They bring together the attribute view

and are the basis for the measures or key figures that make up a multi-dimensional analytic model (Figure 31).

In almost every circumstance, the analytic view is defined using a transactional columnar table. Transactional

tables contain each record of activity within a line of business. They can range from sales transactions to a

customers calls to units shipped.


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Figure 31

Adding an attribute view to the data foundation

If you are using SAP Data Services to extract, transform, and load (ETL) data into SAP HANA, and also

following standard data modeling approaches, you will use fact tables as your analytic foundation. If you are

loading data without using an ETL processes, transaction tables might be more difficult to identify. With almost

every transaction table, there is a general set of characteristics that you can use to recognize these types of

tables. They typically contain dollar amounts or unit counts that occur over time or over a sequence of events.

In the examples used in this report, the SALES_ORDER_DETAIL table is a perfect example. It contains three

distinct dates and four columns that can be used as measures (Figure 32). Once joined with the attribute

views, users can subtotal these amounts over fiscal and calendar dates, months, years, or quarters or by

customers, states, regions, or countries.


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Figure 32

Transaction tables

When creating an analytic view, you must use a new or an existing package for storage and security. You

specify the analytic view name and choose from the Create New or Copy From options (Figure 33). Note that

you cannot change the name of an analytic view once it is saved and activated. However, developers can use

the Copy From option to create a new version with a different name.


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Figure 33

Creating an analytic view

There are two main tabs within an analytic view. The Data Foundation tab is the starting point for designing an

analytic view. It contains all the components needed to define the transaction or fact table. The Logic View

table is used to define the joins between the data foundation and existing attribute views.

On the right side, developers add one or more tables to the data foundation. Once the tables are added,

developers define private attributes and measures by right-clicking each column and selecting the appropriate

option (Figure 34).


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Figure 34

Analytic view on the Data Foundation tab

Private attributes are the columns used in joining to existing attribute views or for defining display attributes that

do not exist in an attribute view. In most cases they are used to define a join path, but they are present in the

output of any model and can be used for filtering, grouping, and sorting within analytic tools once the model is

complete. Developers can also define filters that will be applied to any results generated by the final model.

Developers typically filter the analytic view data foundation to eliminate records that should be excluded from

any calculation based on the final model. For example, a transaction table might contain multiple order statuses

and duplicate measure values for each status. From a business user point of view, only the final or confirmed

order status is necessary for reporting. Using an analytic view filter eliminates the status used in the workflow of

entering, verifying, and confirming an order and only presents calculations on the records representing the final

status of the order.

From a technical perspective, developers need to filter the order status to prevent the model from over-

aggregating the results. If an order has three statuses and subsequently three order-detail line records, only

one record can be included in the results without triplicating the values of the measure.

It is possible to include more than one table in the analytic view foundation. However, we caution against this

approach as it results in significant performance degradation when both tables contain millions of records. In

almost all cases, it is better to model the data into a single table using SAP Data Services as data is loaded into

SAP HANA. This not only simplifies the SAP HANA modeling tasks but also increases the query response

times of any model.


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The Data Foundation output includes all the columns that are available for use on the Logical View tab. They

consist of Attribute Views, Private Attributes, Calculated Attributes, Measures, Calculated Measures, Restricted

Measures, Variables, and Input Parameters. The output columns available in this view can be managed on

both the Data Foundation and Logical View tabs (Figure 35). However, items will not be visible until the joining

of the attribute view work has been completed on the Logical View tab (Figure 34).

Figure 35

Analytic view columns

The attribute view contains all the columns defined within attribute views that are joined to the foundation on

the Logical View tab. Until you have added and joined the attribute views to your foundation, this section

remains empty.

Private attributes are those that you select in the foundation for joining on the Logical View tab. They represent

columns that you can use for the display in the final model or with restricted measures. In any case, unless

hidden, these values are available in the final model and appear as though they are standard attribute views.


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Calculated attributes allow for the manipulation of any attribute using SAP HANA formulas and functions. In

most cases, we recommend that you design calculated attributes in the appropriate attribute view. However,

developers may sometimes find it necessary to concatenate, substring, or derive new output columns based on

multiple private attributes or attribute view columns within the analytic view.

Generally developers create them in the analytic view because the calculation spans multiple attribute views or

private attributes. This is difficult to accomplish in the attribute view because the values might exist in disparate

tables in the data model.

Measures are defined by right-clicking columns in the foundation that will be aggregated in the final results of

the model. SAP HANA analytic views only support the SUM, MAX, and MIN aggregation functions at this time.

To perform more complex aggregations, you need to develop a calculation view, which we discuss later in this

report.

Calculated measures are defined in the output section of the analytic view. They represent calculations that

involve static values or additional measures. For example, users might want to see the total value of an order

less the shipping costs. This can be accomplished in calculated measures simply by subtracting the shipping

costs from the sales order total. Developers can also define ratios and percentages at this level, but they must

consider the tools used to consume these values as summing a ration or averaging. Average might occur at the

reporting tool level.

Restricted measures are a feature of SAP HANA models that allow the developer to define conditional

aggregates. When defining restricted measures, the developer selects an existing attribute, defines anoperator, and indicates a value to which it must be equal. For example, developers can define a measure that

totals sales for 2003 and another that totals sales for 2004. When these values are aggregated and grouped on

country, users can see total sales for 2003 and 2004 for each country.

Variables allow the developer to define single value, interval, or range filters within the analytic view. Any query

that is executed against the published analytic view must satisfy any mandatory variables. This is a very useful

feature if the developer intends for the result set to be limited for a specific date range, attribute, or other

criteria. Note that most of the SAP BusinessObjects reporting tools do not recognize these variables at this

time. However, we have been told by SAP that this functionality will be fully supported in the next few servicepack releases. Variables are different from filters in that they are intended to be dynamic or changed based on

the values selected from the input parameters. Filters, on the other hand, are hard coded and must be re-coded

by developers when business requirements change.

Variables work hand in hand with input parameters. These placeholder values allow developers to enhance the

use of variables by allowing the executor of the query to insert a custom value upon execution. For example,


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each time the query is executed, the user interface requests that a beginning and ending fiscal year be entered

to limit the results. When developers define input parameters, they must indicate the name, database data

type, length, and scale. There is also an option to specify the default value of the input parameter if needed for

the users.

After the data foundation is defined, the second tab of the analytic view is named the logical view. The logical

view is the basis for defining the joins between the analytic foundation and existing attribute views (Figure 36).

Figure 36

Logical view

Developers add the existing attribute views either using the new analytic view wizard or by dragging them from

the navigator pane on the far left side of the SAP HANA Studio modeling perspective. Attribute views are joined

to the analytic foundation using the attribute key of the attribute view and the private attributes of the

foundation. The basic inner, left outer, and right outer join types are all supported. Each join is assumed to use

the equal operator, which limits the use of between, less than, or greater than joins.

There are also two additional join types of joins, referential and text. Referential joins are the default join type.

They offer better performance compared to inner joins assuming only a subset of attributes are queried in

relation to the overall number of attributes defined in an analytic view. They act as an inner join but they are not

enforced if attributes are not selected in a query. This is unlike the SAP HANA inner join, in which attributes

defined in the analytic foundation are enforced even when they are not selected in a query. In short, the

referential join helps to reduce the number of expensive join operations by eliminating joins that are not

relevant to any user defined query.


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However, the results of one query to the next might vary because the analytic foundation records will be

excluded or included based on the inner joining of the various attribute views selected in the query. They

should only be used if the referential integrity between the analytic foundation table and all its attribute views is

known to be sound. In database terms, a logical foreign key constraint should exist. In layman's terms, every

record in the analytic foundation table should have a matching record in the analytic views. If this is not the

case, a query by YEAR and SUM(SALES_DOLLARS) might return different results than a query on YEAR,

CUSTOMER and SUM(SALES_DOLLARS) when a sales transaction record exists in the foundation that has

no matching customer in the attribute view.

Text joins are used within attribute views. They are a special join type that allows developers to join two tables

when one contains characteristics and the other contains the characteristic in a specific language. Text joins

were developed specifically to work with SAP ERP tables and the SPARAS field to provide for automatic

translation of characteristics. Text joins act as an inner join, meaning that they will restrict the results based on

matching records. There is also a special dynamic language parameter. It is defined in the attribute view

foundation join definition that is automatically processed within to filter the text to a specific language based on

the locality of the user querying the attribute. In short, they are used to provide automatic multi-language

support in query results.

Based on the documentation, you can also establish the cardinality between tables to help the various SAP

HANA engines quickly and accurately execute the analytic view. We have never noticed any difference in

performance when changing the cardinality rules, but we have seen a model fail to activate if an attribute key is

not truly unique. When viewing the interface from the Logical View tab, the same output columns and their

various types are available. There is no real difference in the output when switching between the data

foundation and logic view. The only exception is that attribute views are only visible in either tab once they have

been added to the model on the Logical View tab.

Once developers have fully defined the model, they must save and activate the analytic view before it is

available within the SAP HANA metadata repository (Figure 37). To save and activate the model, developers

click the save and activate icon. Activation also validates that no rules have been violated within the design of

the model. Developers should pay close attention to the Job Log window, as it indicates if there are any failures

in the activation. If there are any failures, the font color changes to red, indicating that there was an issue in the

attempt to activate the model.


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Figure 37

Save and activate the analytic view

Developers can double-click an item in the Job Log to open the Job Details window (Figure 38). Within this

window, a detailed explanation is provided as to the issues that led to the activation failure. The same is true

when a model is validated without activation.

Figure 38

Job Log details

Calculation Views


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Calculation views are the basis for performing complex calculations, aggregations, and projections. It is difficult

to describe the full functionality of calculation views, but they are generally used to produce result sets that

span multiple analytic views. A more simple explanation might include the use to produce a distinct count or to

further filter and aggregate the analytic view for faster processing. Calculation views can be used to produce a

view of that data that spans multiple fact tables or contexts, similar to the way Web Intelligence and a universe

manage multiple queries.

In SAP BusinessObjects, the universe and Web Intelligence report engine overcome cross fact aggregation by

passing multiple independent SQL statements to the relational database and then merge the results as if they

were a single query within the report engine. SAP HANA approaches this differently in that calculation views

are used to merge data sets into a single logical view of the data. They incorporate a more set-based

philosophy in working with data than you see in a traditional database view or procedure. SAP HANA can

provide most of this functionality in a graphical UI (GUI) without the need to write hundreds of lines of SQL

code. With that said, calculation views can also be based on script logic if needed.

The calculation view UI is similar to that of the attribute view and analytic view. On the left side, developers can

create logic dataset workflows to guide SAP HANA in the processing of the data sets. The center window

contains details on only objects selected from the left-side window. The right-side window contains the output

column definitions for each items selected from the left side. Each item selected from the left side produces a

different view for both the center and right windows (Figure 39).

Figure 39

Calculation view overview

For the purposes of this special report, we do not go into great detail on all the facets of calculation views.

However, we do describe in general terms a solution in which calculation views are used to produce meaningful

results.


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Take, for example, an analytic view that produces customer sales orders and another that produces customer

product returns. The analytic view for each area would be capable of calculating results for not just products

and dates, but also for customers, sales reps, distribution centers, and other facets. For the purposes of this

solution you only need to use a few of those facets to produce the results.

Using a calculation view, you can develop a results set that compares the number of orders for a given product

and subsequently the number of returns for that same set of products. To develop this solution using a

calculation view you would start by adding both analytic views to the GUI. You then would project them to

include only the columns needed to satisfy the requirements. Projection is a process in SAP HANA in which

developers can reduce the amount of in-memory data blocks that are accessed by removing columns from an

analytic view that are not needed within the calculation view. In most cases, projecting the analytic view

increases the performance of the calculation view.

Once they are projected, you can aggregate the results of the sales analytic view to include the product, year,

month, total units shipped, and a null value place holder for products returned. Using the sales returns analytic

view, you can aggregate the results to produce product, return year, return month, total units returned, and

NULL place holder for units shipped (Figure 40). The purposes of the NULL value place holders are to facilitate

the subsequent UNION of the two results. When performing a UNION, both results sets must have the same

number of columns.

Figure 40

Setting a NULL column

Within the aggregation of each set, you create a calculated column and set it to a NULL value (Figure 41).


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Figure 41

Results of a projection and aggregation of two analytic views: products sold and products returned

Taking the results of each aggregation, you then can UNION the records sets. The results of the UNION

operation would only be temporarily managed by SAP HANA and never returned by the results set of the

calculation view. However, it is important to logically understand what is happening within the sequence of

calculations that produce the desired results (Figure 42).


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Figure 42

UNION of the record sets

Using the aggregate option within a calculation view, you can then aggregate the results again to produce a

single records set that displays the results as if they were stored together in the database (Figure 43).

Figure 43

Aggregation of UNION

The setup of such a calculation can be done completely using a GUI. Each object in the GUI represents a

different dataset-based operation that can project, aggregate, UNION, aggregate, and output a results set.

Within the SAP HANA Studio, this is represented as a series of set-based operations (Figure 44). From a

workflow standpoint, you are simply taking two datasets, aggregating each set, combining the two sets, and

then aggregating the combined sets to produce a single result set (Figure 45).


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Figure 44

An SAP HANA Studio calculation view workflow

Figure 45

Logic workflow of a calculation view


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Analytic Privileges

Analytic privileges allow developers to define automatic, row-based filters based on an SAP HANA user

account. In general, we refer to this as row-level security. Analytic privileges can either protect data or

automatically filter data for each SAP HANA logon. They are set up and stored with the same packages thatare used to manage attribute views, analytic views, and calculation views.

When defining an analytic privilege, the developer specifies one or more view objects to restrict. Once the

objects are selected, they must then define the attribute to restrict. The final step of the process requires that a

restriction be set up for that selected attribute. For example, an analytic privilege can set up to restrict the

results of a calculation view to only the country of Great Britain (Figure 46). Once the analytic privileges are

saved and activated, the DBA can then assign it to an individual user or a database role (Figure 47).

Figure 46

Creating an analytic privilege

Figure 47

Assigning the privilege to a user


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available, including information about which tools can connect directly to SAP HANA and which must connect

via other methods, such as the semantic layer.

Connecting to SAP HANA

In all of the conversations that we have had on analytics and visualizations built on top of SAP HANA, the most

frequently asked question weve heard is: How do we connect to SAP HANA?

Individuals always wonder if theres any special learning or knowledge that must be gained before they can

successfully connect to data sources with SAP HANA. For the most part, the most difficult part of connecting

SAP BusinessObjects to SAP HANA is the configuration of the ODBC or JDBC client drivers. Once you have

the connections set up properly, the SAP BusinessObjects 4.0 tools work essentially the same way that they

would with any relational source.

SAP HANA Database Client

To connect SAP HANA to SAP BusinessObjects 4.0, you begin with the SAP HANA JDBC and ODBC client.

This client is available in both 32-bit and 64-bit for a wide variety of operating systems. You can locate these

clients on the SAP Service Marketplace (Figure 49).

Figure 49

Finding SAP HANA clients on the SAP Service Marketplace

To find the download files, follow these steps.


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1. Log on to the SAP Service Marketplace (http://service.sap.com/support)

2. Choose the Software Downloads tab

3. Open the SAP Software Download Center in the left frame and click Support Packages and Patches

4. Click Browse our Download Catalog from the left frame

5. Click SAP In-Memory (SAP HANA) from the list in the center frame

6. Click SAP HANA Enterprise Edition and then SAP HANA Enterprise Edit 1.0

7. Click Comprised Software Component Versions

8. Click SAP HANA Client 1.00

9. Select the appropriate operating system (Figure 50)

Figure 50

Operating system choices for SAP HANA

10. Scroll to the bottom and download the version that matches your SAP HANA database (Figure 51)

Figure 51

Download options

Note
http://service.sap.com/supporthttp://service.sap.com/supporthttp://service.sap.com/supporthttp://service.sap.com/support


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All SAP HANA client patch installations allow for either an upgrade or a full installation. It is best to only install

the version that matches the SAP HANA database that will be used for connectivity to SAP BusinessObjects

4.0. With the SAP BusinessObjects 4.0 server services, you need the 64-bit client. With the SAP

BusinessObjects 4.0 client tools, you need the 32-bit version.

Once the correct installation has been downloaded, find a file with a .SAR extension. This is a special SAP

archive (much like a ZIP file) that you need to extract using a utility called SAPCAR.exe. You can find

SAPCAR.EXE in the SAP Service Marketplace. To download SAPCAR in the SAP Service Market Place, use

the following steps:

1. Choose the Software Downloads tab

2. Choose Support Packages and Patches from the left frame

3. Choose Browse our Download Catalog from the left frame

4. Choose Additional Components

5. Choose SAPCAR (Figure 52)

6. Select SAPCAR 7.10

7. Select the appropriate operating system where you will run the utility.

Figure 52

SAPCAR options


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SAPCAR does not require any installation to use. It is standalone executable. Simply download it and save it to

any folder. To use SAPCAR you must access it from the command line. For example, you can extract the SAP

HANA client .SAR archive using the following example:

SAPCARxvf IMDB_CLIENT100_XXXX.SAR (Figure 53).

Figure 53

Extract the downloaded SAR file

This extracts the SAP HANA client to a sub folder within the path in which you executed SAPCAR. Within the

newly extracted folder, look for hdbsetup.exe. This starts the installation of the SAP HANA client. If you are

upgrading your client, choose the Update SAP HANA Database Client option (Figure 54). If you are installing

for the first time or installing side-by-side, choose the Install New SAP HANA Database Client option. With the

appropriate install option selected, review and confirm the installable components and click the Install button

(Figure 55). Step 3 (Install Software) displays the progress of the installation. Once complete, Step 4 (Finish)

displays (Figure 56). Click the Finish button to close the installation wizard.


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Figure 54

Define the SAP HANA client install options


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Figure 56

Complete the installation

SAP HANA ODBC Data Source

Now that the database client is installed, the next step is to open the ODBC Data Source Administrator. For

SAP BusinessObjects 4.0 client tools, open the 32-bit ODBC data source at

c:\windows\syswow64\odbcad32.exe. For SAP BusinessObjects 4.0 server services, open the standard 64-bit

ODBC manager found in the control panel.

When the ODBC source is created on the SAP BusinessObjects 4.0 server, it must be created as a 64-bit

ODBC data source because the SAP BusinessObjects 4.0 server runs as a 64-bit application. A typical ODBC

Data Source Administrator looks something like the screen in Figure 57. Click the System DSN tab and click

the Add button to add a new data source.


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Figure 57

The ODBC Data Source Administrator


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Scan the list in Figure 58. Find the HDBODBC data source, select it, and click the Finish button to open the

SAP HDB properties page.

Figure 58

Select the HDBODBC data source

Enter a short name for the ODBC data source along with a description, the server name, and port number

(Figure 59). In the Server:Port field, enter :. The default port for most SAP

HANA database instances is 30015. This information can be determined by contacting your SAP HANA

administrator.

Once the information is entered, click the Connect button. A new window appears (Figure 60). Within the

window, enter a valid SAP HANA user and password and click the OK button to verify the connection details. A

new window appears to verify that your connection is set up properly (Figure 61). If your connection is

successful, click the OK button on each subsequent window until the ODBC data source administrator is closed

(Figure 57).


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Figure 59

Provide a name, description, server, and port for SAP HANA

Figure 60

Enter valid credentials for SAP HANA

Figure 61

Successful connection message


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If you receive an ODBC error (Figure 62), check with the SAP HANA administrator to verify that the connection

details are correct and that the SAP HANA system is available.

Figure 62

An error in creating the connection to SAP HANA

Once the ODBC data source is built on your workstation (Figure 63), ensure that the same data source is

created on the BusinessObjects Enterprise 4.0 servers to which you will publish your semantic layers,

analytics, and visualizations. Without this, analytics and visualizations created or published on the SAP

BusinessObjects 4.0 server will not connect to the SAP HANA data source. You are now ready to build a

semantic layer using the ODBC data source.


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Figure 63

A completed ODBC Data Source Administrator with SAP HANA

SAP BusinessObjects IDT

We now discuss the steps required to use the SAP HANA ODBC data source connection to create a semantic

layer with the SAP BusinessObjects IDT. This is not a step-by-step guide on how to create an IDT universe, but

rather an overview of how a developer would establish relational connections to SAP HANA using ODBC

drivers. Once an SAP HANA relational connection is created in the IDT, universe design processes are very

similar to those used with any relational source. There are a few exceptions to this statement, which we discuss

below.

Note

You can also choose to set up and use the SAP HANA Client JDBC driver. You can find directions forconfiguring the SAP HANA JDBC driver using the following SAP Note:

https://service.sap.com/sap/support/notes/1591695.

Developers need an IDT universe to support SAP HANA data access for SAP BusinessObjects Web

Intelligence, SAP Crystal Reports for Enterprise, and SAP BusinessObjects Dashboard Design. SAP

BusinessObjects Analysis for OLAP and SAP BusinessObjects Explorer do not use the universe layer to

connect to SAP HANA. It is also worthy of mention that both Crystal Reports 2011 and Crystal Reports for

Enterprise can now directly connect using ODBC. In addition legacy UNV Universes can connect using ODBC

to SAP HANA starting with SAP BusinessObjects 4.0 SP4.

There are two main methods or methodologies for creating an IDT universe. You can either connect directly to

the SAP HANA base columnar tables or you can connect the universe directly to the analytic views or

calculation views developed using the SAP HANA Studio. However, before developers can access the SAP

HANA tables or analytic view they have to create a connection to the data source within the IDT. Within the IDT

developers create a connection to SAP HANA within the standard Repository Resources window (Figure 64).

They follow the same process that is used for creating any typical relational connection.
https://service.sap.com/sap/support/notes/1591695https://service.sap.com/sap/support/notes/1591695https://service.sap.com/sap/support/notes/1591695


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Figure 64

Typical Repository Resources and connections

During the process of creating a relational connection to SAP HANA, the Database Middleware Driver selection

window appears. The SAP HANA ODBC and JDBC drivers are found under SAP>SAP HANA database 1.0 >

ODBC or JDBC (Figure 65). Outside of the location of the SAP HANA drivers, the process for creating the

connection to SAP HANA is the same as always.


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Figure 65

Select the ODBC drivers in the Database Middleware Driver Selection


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After selecting the ODBC driver option the developer clicks the Next button. The connection wizard then

requests the authentication options, server and port information, and user name and password required to

connect to an instance of SAP HANA (Figure 66). The server name is entered in using the standard

conventionfor example, sap-hana.org:30015. You can use the Test Connection button to validate

that everything from the ODBC to the IDT Universe connection information is in working order.

Figure 66

Configure the IDT Universe SAP HANA authentication and server

SAP HANA Tips for the IDT Data Foundation Layer

With the server side connection and the connection shortcut created in the local project, you now create a data

foundation layer. Again, the scope of this section of this report is to help developers understand the concepts

within IDT specific to SAP HANA. How a developer accomplishes basic tasks within IDT is beyond its scope.

As mentioned before, you can set up a data foundation for an SAP HANA universe in two ways:

You can directly use the SAP HANA models, both analytic views and calculation views, which youcreated in the SAP HANA Studio

You can use the base columnar tables that were loaded by SAP Data Services 4.0


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If the universe will be used directly to support analytical calculations in charts, graphs, tables, and other visual

components, it is best to use the analytic views or calculation views in the data foundation of your universe.

The analytic views already contain all the modeling work needed to facilitate these types of BI needs.

However, if your universe will support enterprise reporting, generate lists of records, or provide insight into datathat is not highly aggregated or measured, it is best to use the base tables to set up the data foundation.

There is no simple guide to help developers make the correct choice, but each query executed against an

analytic view must contain a group by and aggregate function. With this in mind, it would only really facilitate

analytic analysis of data.

It is also important to remember that you need analytic views to support SAP BusinessObjects Explorer or

Analysis for OLAP. Some organizations do not want to develop and support both SAP HANA analytic view

metadata and traditional universe metadata. When the base tables are used, there are no requirements to

execute a SUM() or group by statement to facilitate SAP HANA-based queries. However, it is wise to include

measures containing database level aggregates in either scenario.

When working with the IDT universe data foundation, developers can locate the SAP HANA analytic views or

calculation views by examining the _SYS_BIC schema. Within this schema, developers see a relational

representation of the multi-dimensional model stored in the SAP HANA metadata repository. Analytic views can

be located by their distinct icon and name. The analytic view icon has an orange cube and the fully qualified

name that includes the package, analytic view, and the term OLAP within its name (e.g., package/analytic

view/olap.) Simply add these table objects to the foundation and you are finished (Figure 67).


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Figure 67

A listing of schemas in the IDT


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Because all the required modeling was completed on SAP HANA, there is no need to set up any additional

items at this level. Future releases of SAP BusinessObjects will allow for direct binding to SAP HANA models.

This will render the need to set up universes on those models obsolete. However, because that functionality

does not yet exist, we provide details on how to use them in current versions of SAP BusinessObjects 4.0.

When working with the IDT universe data foundation, developers can locate the base columnar tables within

the schema to which the data was loaded using SAP Data Services 4.0. From a traditional BusinessObjects

universe design standpoint, the process for using the columnar tables is exactly the same as developing a

universe against any RDBMS. The developer adds the tables to the data foundation, joins them based on the

appropriate columns, filters them based on business or technical rules, or creates derived tables to facilitate

advanced calculati

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