dbta best practices going hybrid data management

8/8/2019 DBTA Best Practices Going Hybrid Data Management

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Best Practices Series

Hortonworks PAGE 14 GOING HYBRID:

THE NEXT ERA OF

DATA MANAGEMENT

MemSQL PAGE 16

A HYBRID APPROACH

TO DATA PROCESSING

DenodoTechnologies PAGE 17 DATA VIRTUALIZATION:

THE FOUNDATION FOR A

SUCCESSFUL HYBRID DATA

ARCHITECTURE

Splice Machine PAGE 18 POWERING REAL-TIME

APPLICATIONS

AND OFFLOADING

OPERATIONAL REPORTS

WITH AN OPERATIONAL

DATA LAKE

GridGain Systems PAGE 19 ACCELERATE BUSINESS

INSIGHTS BY MANAGING

HYBRID DATA IN MEMORY

GOINGHYBRID Te Next Era ofData Management


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12 FEBRUARY/MARCH 2015 | DBTA

FOR POWERING TODAY’S ENTERPRISES, no

one single solution does it all. Rather,

organizations rely on varied—and ofteneclectic—mixes of databases, platforms,

systems, and frameworks. The spotlight

may currently be on Hadoop and all-flash

storage systems as the stars of the big

data show, but there are many other cast

members as well. A well-functioning

data environment requires an entire

ensemble of approaches that include,

but aren’t necessarily limited to, Hadoop

and all-flash storage. These consist of

relational database management systems,

enterprise data warehouses, in-memorysystems, disk and tape storage systems,

NoSQL databases, and cloud-based data

environments. Bringing all these elements

together into hybrid approaches may

potentially deliver faster, better, and morescalable approaches to data management.

Workloads and applications may vary

on a day-to-day basis. A typical hybrid

architecture may consist of a relational

database management system running

a transactional system, with data sent

to an in-memory database supporting

analytics platform. Or, there may be an

open source framework such as Hadoop

at the back end to manage and create

files with big data that would be too

expensive to send through the extract,transform, and load processes of the

enterprise data warehouse. A hybrid data

architecture may also consist of remote

databases that capture data from outside

the walls of the enterprise, which is thensent to an enterprise data warehouse

at the secondary level to support data

transformation and governance. Such

an environment may also have NoSQL

databases at the front end to support data

access and analysis.

The key is that business requirements

are constantly changing, and the data

infrastructure has to be flexible enough

to evolve with these requirements.

Enterprises need to be able to scale to new

configurations, or even swap out existingsolutions for newer technologies. An open,

hybrid architecture enables such agility.

Best Practices Series

GOINGHYBRID Te Next Era of Data Management


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13 FEBRUARY/MARCH2015 | DBTA

There are compelling benefits to

deploying hybrid data environments,especially in terms of speed, flexibility,

and costs. That’s because hybrid data

environments can be built on enterprise-

wide service layers that can dynamically

scale against back-end on-premises,

virtualized, or cloud-based resources,

while employing in-memory, clustered,

and parallel processing resources. As

business needs evolve, DBAs and data

managers need to be able to provision

and stand up databases and supporting

infrastructures that can quickly supportsuch growth. Hybrid environments

provide an array of choices to enable

rapid implementations. At the same

time, businesses need to avoid the costs

involved in investing in high-end systems

that may need to be scaled back, or may

not be suitable for their requirements 3 or

4 years down the road.

Here are eight ways hybrid approaches

can be effectively deployed to support

data management:

WORK WITH BUSINESSREQUIREMENTS

Systems, and even architectures, are

in a great state of flux, as enterprises

wrestle with ever-shifting requirements

while also attempting to stay competitive

with digital strategies. There are many

options to address opportunities and

problems, and every enterprise and

department of an enterprise has its own

business requirements, budgets, existing

technologies and approaches, andavailable skills. That’s why as enterprises

seek to transition to digital, they need

to move in deliberate and well-planned

steps, as new approaches take root

alongside existing legacy systems and

processes. Business requirements vary,

plus budgets and priorities may vary.

LOOK TO THE NEW BREEDOF RELATIONAL DATABASES

To meet varying demands, a new

generation of hybrid databases is nowemerging in the market. These data

platforms are typically relational database

systems with a range of new capabilities,

offering the option to move to eitherin-memory or traditional disk-based

storage. These databases are optimal when

enterprises require a high-performance,

relatively small footprint without the

expense and resources needed for moving

data back and forth between disks—

often seen as a latency factor in many

traditional database settings.

INCORPORATE AND INTEGRATEDIVERSE DATABASES INTO

A HYBRID ARCHITECTUREThere are a wide range of database

types—from relational database

management systems to NoSQL to

in-memory cloud databases—now in

today’s environments. Each serves specific

purposes, but the information they

handle needs to be available across the

enterprise. At the same time, each format

brings its own advantages in terms of cost

and ease of use. Play on the strengths of

each, but bring them together.

SUPPORT HYBRIDSTORAGE SYSTEMS

There are many new options on the

table for storage, including traditional

hard disk drives and tape, flash memory,

solid state drives (SSDs), and cloud-

based storage. The costs vary for these

various modes, requiring assessment of

the business requirements of each. Some

forms, such as physical hard disk drives

and tape, are lower cost but take more

time to access, so therefore may servebetter for data that is more infrequently

accessed, or in back-end archival roles.

More costly but faster and better-

performing forms of storage such as

SSDs may serve caching or short-term

storage requirements.

SUPPORT ANY ANDALL DATA TYPES

Data from various sources – both

existing and being added on a regular

basis—will be in a variety of formats, bethey unstructured or semi-structured,

including ASCII, binary, and proprietary

formats. A hybrid data architecture is well

equipped to handle the variety—expectedand unexpected—that the business may

be bringing in.

ACHIEVE QUICKIMPLEMENTATION

Hybrid data storage and warehouse

appliances can be quickly implemented

into existing infrastructures at relatively

low costs and with small footprints.

Appliances offer high-capacity

alternatives for mixed application

workloads and virtualized environments.

ENABLE DATA AS ASERVICE (DAAS)

The challenge is to provide a data

environment in which the entire

organization can benefit, enabling all

parties—no matter how distributed they

are—to acquire, transform, move, clean,

stage, model, govern, deliver, explore,

collect, move, replicate, share, analyze,

catalog, publish, search, back up, and

archive the data they are working with.Ultimately, this ends up as a data as a

service layer that provides for all these

requirements, while ensuring control,

security, privacy, reliability, and scalability

—along with a great user experience on

the front end.

BUILD A SKILLS REPERTOIREWhile there’s always a strong case to

be made for specialization, particularly

in database technologies, enterprises are

fast requiring a broad range of skill setsto power their data environments. As

hybrid environments and architectures

increasingly become the norm, there

will be critical demand for data

managers capable of addressing multiple

environments, or at least being able to

acquire help on an as-needed basis. This

is part of the ongoing evolution of the

jobs of data managers, who see their roles

evolving from collectors and installers of

data, to high-level consultative roles to the

business, serving as brokers and curators.

—Joe McKendrick


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Sponsored Content 14 FEBRUARY/MARCH 2015 | DBTA

®

GOING HYBRIDTHE NEXT ERA OF DATA MANAGEMENT

AS WE STEP INTO AN AGE where data is a

competitive advantage, our concepts of

data management need to be revised from

the days of the enterprise data warehouse.

In the big data age, a hybrid model of

data management can ensure continued

success and build long lasting value for

the enterprise. Hortonworks and Red Hat,

two pioneers in the open source space,

have worked closely together to build

agile, enterprise-grade big data solutions

for the enterprise of the future.

WHAT IS APACHE HADOOP?

Shortly after enterprise IT adopted

large scale systems to manage data, the

Enterprise Data Warehouse (EDW)

emerged as the logical home of all

enterprise data. Today, virtually every

company has a Data Warehouse that serves

to model and capture the essence of the

business from their operational systems.

The explosion of new types of data in

recent years—from inputs such as theweb and connected devices, or just sheer

volumes of records—has put tremendous

pressure on the EDW. Organizations

are also seeking to capitalize business

opportunities as they ingest real time event

data streams.

In the meantime, Apache Hadoop

has emerged as a great way to parallelize

analytics on large data sets running on

commodity hardware. As a result, an

increasing number of organizations have

resorted to a hybrid model using ApacheHadoop to help cost-effectively manage

the enormous increase in data while still

maintaining the integrity of the data in

the EDW. By adopting this new hybrid

model, organizations are beginning to

deploy new analytic applications that

could not exist before, either because it

was too costly to scale their EDW, or it

was not technically possible in the existing

IT infrastructure model.

RED HAT AND HORTONWORKS

—THE VISION

Red Hat and Hortonworks, two opensource leaders, bring Apache Hadoop to

the enterprise. Working together, they

are building on their common, open

source approach to developing software

that addresses the growing big data

requirements of the enterprise. With

an enterprise Hadoop platform that is

tightly integrated with open hybrid cloud

technologies (including OpenStack, Red

Hat Storage, JBoss, Red Hat Enterprise

Linux, and OpenJDK), Hortonworks

and Red Hat deliver infrastructure andapplication development solutions that

enable the next generation of big data

applications through IT optimization and

advanced analytics applications.

Companies are now able to move high

volumes of existing data into Hadoop,

offload processing workloads, and enrich

their data architecture with additional

types of data to create new business value.

Additionally, a new, ultra-competitive

breed of businesses is now emerging.

These organizations are able to takeadvantage of immense volumes and

varieties of data to create competitive

differentiation—as an example, by

building a single, 360-degree view of

their customers and leveraging advanced

predictive analytics in Apache Hadoop.

Red Hat and Hortonworks are committed

to helping enterprises mine and monetize

their data for deeper business insights.

Learn more about the collaboration at

hortonworks.com/partner/redhat.

HYBRID DEPLOYMENT

SCENARIOSHortonworks and Red Hat provide

many choices of infrastructure to deploy

Hortonworks Data Platform (HDP): on

premise, cloud, and virtualized. Further,

our customers have a choice of deploying

on Linux and Windows operating

systems. We believe you should not be

limited to just one option, but have the

option to choose the best combination

of infrastructure and operating system

based on the usage scenario. In a hybrid

deployment model, you should have all of these options. Our customers come

to us asking to meet the requirements

for their organizations for the following

scenarios:

Cluster Backup

IT Operations teams expect Hadoop

to provide robust, enterprise-level

capabilities, like other systems in the data

center, and business continuity through

replication across on-premises and

cloud-based storages targets is a criticalrequirement. In HDP 2.2, Hortonworks

helped extend the capabilities of Apache

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15 Sponsored Content FEBRUARY/MARCH2015 | DBTA

Falcon to establish an automated policyfor cloud backup to Red Hat Storage.

In addition, Red Hat engineers have

worked closely with the HDP Engineering

Team to build a plugin for Red Hat

storage. This new plugin allows customers

to run Map Reduce applications directly

on top on GlusterFS rather than have to

deal with expensive and cumbersome data

movement to and from HDFS (Hadoop

Distributed File System). In addition, the

Red Hat Storage option offers customers a

POSIX-compatible environment withouta single point of failure.

Development

In enterprise shops, development

environments are normally separated

from production systems. And

development environments are typically

smaller in scale, spun up and down

on a regular basis and are constantly

changing. Today, many organizations

are relying on a cloud-based option

for their development teams. It allowsIT to manage multiple development

environments more easily and also to

spin up temporary environments to

a full or a short-term development

requirement. As a hybrid option, you

need to be able to port not just data, but

the Hadoop applications as well. Red Hat

and Hortonworks are working closely

together on joint R&D efforts to simplify

instantiation of analytic applications on

HDP leveraging the Platform as a Service

(PaaS) capabilities of Red Hat OpenShift.

Burst

Data Science continues to grow in

interest within all of the organizations

we see adopting Apache Hadoop. With

YARN acting as the data operating

system for Apache Hadoop within a

production cluster, new advanced analytic

applications, whether short or long-

running in nature, are able to spin up

application containers in a distributed

fashion, on Hadoop Worker nodes thatare ideal and have the right profiles and

available resourcing for hosting each of

these unique workloads. Data Scienceteams are also able to comfortably spin

up temporary clusters (on premise or

in the cloud) to perform discovery-

type exploration, develop and test new

models or even run advanced machine-

learning algorithms without the worry

of impacting other IT systems. Data

Scientists can seamlessly incorporate data

and application logic from their existing

production Hadoop environments.

OpenStack OpenStack is an open-source

cloud platform typically deployed as

an Infrastructure as a Service (IaaS)

solution. It has gained much popularity

for its affordability, scalability and

flexibility. Hortonworks and Red Hat

have collaborated to bring Hadoop

to OpenStack via the Sahara project.

The goal of Sahara is to eliminate the

complexity of setting up and maintaining

Hadoop clusters, and to lower the TCO

of big data analytics. Deploying HDP onOpenStack will provide IT shops with

the deployment flexibility and speed

needed to meet today’s rapidly changing

business needs.

Portability is the key to making each

of these deployments models successful.

You need to be able to not only move data

back and forth, but to also synchronize

data sets. Hortonworks continues to

focus on providing an Enterprise Ready

Hadoop distribution and invest in thisarea. Apache Falcon, Sqoop, Kafka

and Flume are delivered with HDP2.2

to support data management and

governance.

Further, and even more complex,

is the consistency of the “bits” across

environments. The same version of

the entire Hadoop stack must be

deployed in these environments, or else

you risk a job execution failing as it is

migrated from one to the next. This

portability is a critical requirement forhybrid deployment of Hadoop. With

Hortonworks providing Apache Ambari

for Hadoop deployment, configuration,management and monitoring, and the

above-mentioned joint Red Hat and

Hortonworks engineering collaboration,

these challenges can be met.

HORTONWORKS ENABLES THE

MOST CHOICE IN THE INDUSTRY

Agility is a key business imperative for

CEOs and CIOs alike. Agile businesses

run on agile technology, which in turn

is made possible through choice. The

collaboration of two open source leaderstranslates into more choice for customers

looking to build big data systems today

that will also evolve to meet the demands

of the enterprise tomorrow. Enabling Red

Hat and Hortonworks partner integration

is key to everyone’s success and a key part

of our joint strategy.

CONCLUSION

Hybrid is more than just a good idea.

It’s the way forward. As traditional lines

blur (IT vs. Business, Cloud vs. On-Premise,Big Data vs. EDW), it is important that

enterprises are prepared to juggle the

demands of traditional data systems with

a modern data architecture. Monetizing

all types of data has emerged as the new

battleground, and a hybrid model for data

management ensures that tomorrow’s

enterprises are set up for success.

HORTONWORKS For more information, visit

www.hortonworks.com

®

Call to action:Learn more at

http://hortonworks.com/labs/redhat/

and go over the tutorials.

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A Hybrid Approach to Data ProcessingIn-memory computing and untapped business opportunities

are leading organizations to hybrid transactionaland analytical data processing.

THE CHALLENGE WITH DATA

MANAGEMENT TODAY

Traditionally, processing of

transactional and analytical data occurs

in separate databases which leads to

data silos. The ability to run business

operations and gain insights from data

is restricted by the time it takes to move

data from a transactional database to a

data warehouse.

Typically, the process goes like this:• An online transaction processing

(OLTP) database ingests and stores

incoming data.

• The process we begrudgingly know

as ETL (extract, transform, load)

transfers data from the OLTP database

to a data warehouse.

• Stale data is then available to run

queries against and, hopefully, garner

insights to either increase revenue or

reduce costs.

This is a problem because, for mostorganizations, the highest value data is

also the most recent.

A NEW, HYBRID APPROACH

TO DATA PROCESSING

Thanks to innovations in in-memory

computing coupled with distributed

system architectures, the antiquated OLTP

to OLAP approach to data management

is being turned on its head by what

Gartner has coined Hybrid Transactional/

Analytical Processing, or HTAP for short.

Defining Hybrid Transactional/

Analytical Processing

Hybrid Transactional/Analytical

Processing (HTAP) describes the

capability of a single database that

can perform both online transaction

processing (OLTP) and online analytical

processing (OLAP) for real-time

operational intelligence processing.

Market Forces Driving HTAPPowerful market forces must be in

motion in order for any transformative

change to take place, especially when that

change is connected to data management.

The major market forces spurring the

transition from OLTP/OLAP to HTAP

include the following:

Lowering Cost of RAM—Over

the past decade, the price of RAM has

steadily dropped, and is now at the point

where value gained from storing data in

memory far outweighs the costs.

Untapped Business Opportunities—HTAP gives businesses an accurate

representation of their most recent data.

With this visibility, businesses can extract

revenue from incoming data sources, and

mitigate costs by monitoring application

performance in real-time.

Data Everywhere—Movements like

mobile computing and the Internet of

Things have brought us to an age of

interconnectivity where data rules. For

business to thrive in this era, the ability

to collect, store, and analyze data in real-time is an absolute must.

HTAP Solves for Real-Time

Data Processing

HTAP opens new doors for

organizations to make sound decisions

from incoming data without the

restrictions of latency. As data workloads

grow from terabytes to petabytes, HTAP

will enable businesses to scale accordingly.

As a result, organizations will be able to

extract value from data that, with legacysystems, was unthinkable.

HTAP Use Cases

We are in the early days of HTAP, and

it is not always clear how it can be applied

in the real world. As a rule of thumb,

any organization that handles large

volumes of data will benefit from HTAP.

To provide a bit more context, we’ve

compiled the following applications of

HTAP in use today.

Application Monitoring —Whenmillions of users reach mobile or web-

based applications simultaneously, it

is critical that systems run without any

hiccups. HTAP allows teams of system

administrators and analysts to monitor

the health of applications in real-time to

spot anomalies and save on costs incurred

from poor performance.

Internet of Things—Applications

built for the Internet of Things (IoT) run

on huge amounts of sensor data. HTAP

easily processes IoT scale data workloads,

as it is designed to handle extreme dataingestion while concurrently making

analytics available in real-time.

Real-Time Bidding —Ad Tech

companies struggle to implement complex

real-time bidding features due of the

sheer volume of data processing required.

HTAP delivers the processing power that’s

necessary to serve display, social, mobile

and video advertising at scale.

Market Conditions—Financial

organizations must be able to respond

to market volatility in an instant. Anydelay is money out of their pocket. HTAP

makes it possible for financial institutions

to respond to fluctuating market

conditions as they happen.

In each of these use cases, the ability to

react to large data sets in a short amount

of time provides incredible value and,

with HTAP, is entirely possible.

WE BUILT MEMSQL FOR HTAP

MemSQL is built for Hybrid

Transactional and Analytical Processing.It allows data reliant businesses to handle

large amounts of incoming data with ease,

make sound decisions in real-time, and

to manage messy, real-world, data models

without having to give up the power and

familiarity of SQL.

MEMSQL

www.memsql.com

Download a 30-day FREE trial

memsql.com/download

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i t i e s

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7/9


Data Virtualization: The Foundation fora Successful Hybrid Data Architecture

IN THE ERA of of IoT, Cloud, Mobile, and

Social, data is being generated at a pacenot seen before and has become the fuel

that drives successful businesses.

Traditional data management

solutions—based on rigid information

models—are not flexible enough to deal

with the processing of today´s data and,

as a result, hybrid databases, Big Data and

NoSQL technologies have emerged.

For data architects this represents

a new situation where a hybrid data

architecture is needed. The single-

repository centralized data warehouseapproach is no longer suitable as data

architects need to deploy multiple

repositories to store and process the

new data types.

Doing so without a sound information

architecture imposes new challenges:

• Additional information silos, with the

subsequent risk of a spaghetti-style

point-to-point architecture.

• Data is stored with different

granularities in each repository, in

different formats and accessed usingdifferent protocols.

• Data model mismatches between the

data in enterprise systems and that in

the new repositories.

• Applications find it difficult to access

and consume the information that is

spread across many silos.

As a result of this, IT finds it difficult

to cope with today´s business demands.

DATA VIRTUALIZATION ENABLESA HYBRID DATA ARCHITECTURE

Data Virtualization is a technology

that provides a data abstraction layer

over multiple distributed heterogeneous

repositories–hiding the complexity

underneath in terms of potential data model

mismatches and different information

granularity and access heterogeneity.

The Data Virtualization engine lies

between the information repositories and

the consuming application layer representing

a unified point of bi-directional access tothe information. It offers the following

architectural advantages:

1. Abstraction: Hides the complexity

of the underlying data sources and

exposes a unified data model that can be

consumed by the application layer. This

information model is typically based on

the well-known relational model and canbe accessed using SQL.

2. Decoupling: A change in the

underlying infrastructure is buffered by

this data virtualization layer, protecting

the consuming applications from the

changes.

3. Unified point of access: The data

virtualization layer is the ideal place

to enforce your data governance and

security rules.

4. Reuse: The data virtualization

approach fosters the deployment of dataservices that can be reused across the

whole organization.

BEST PRACTICES FOR A SOUND

HYBRID DATA ARCHITECTURE

1. Introduce data virtualization at the

beginning of a project to avoid the risk of

creating a point-to-point architecture that

will be very difficult to manage in the future.

2. Define the information model to be

exposed to the consuming applications in

this layer. As a best practice, use a canonicalmodel that represents the key business

entities that your applications require.

3. Enable access to the new

repositories (Big Data, NoSQL, etc.)

through this layer avoiding a direct access

from the consuming applications to them.

4. Apply the needed transformations

at this layer to import the informationmodels from the new sources. Advanced

data virtualization engines, such as Denodo,

allow importing hierarchical data, key-

value, etc. seamlessly thanks to its Extended

Relational Model while preserving the

native data model in the source.

5. Build reusable data services that

expose the information in multiple

formats (SQL, SOAP, REST).

6. Finally, tune the model in terms

of performance to meet your SLAs.

Advanced data virtualization platformsapply sophisticated query optimization

techniques to make the most of Big

Data and NoSQL platforms computing

capabilities.

A hybrid data architecture based on

data virtualization makes it easy to add a

new repository, offering the agility that IT

needs to properly scale and react to ever

increasing business demands.

DENODO TECHNOLOGIESis the leader in Data Virtualization.

www.denodo.com

http://www.denodo.com/http://www.denodo.com/https://www.dropbox.com/s/41m39fc3gcergyz/dbta_hybrid_bp_graphic_20150128-01-01.tif?dl=0http://www.denodo.com/


8/9


COMPANIES ARE INCREASINGLY EVALUATING

big data technologies to handle massive

data growth. For many, though, the path

to conquering big data is riddled with

challenges—both technical and resource-

driven. They want to leverage big data,

but they don’t know where to start.

A common starting point is

implementing an operational data lake,

which is a hybrid approach to upgrading

obsolete operational data stores (ODSs)

that are inherently expensive to scale. Topower real-time applications and offload

operational reports, an operational data

lake requires a hybrid of two technologies

to make it happen: an RDBMS for

transactional, real-time updates and a

scale-out architecture from Hadoop.

OPERATIONAL DATA

LAKE STRUCTURES THE

UNSTRUCTURED

While Hadoop is a great platform

for unstructured data, it traditionallyhas not been conducive to structured,

relational data. Hadoop uses read-only

flat files, which can make it very difficult

to replicate the cross-table schema in

structured data.

A data lake is operationalized via a

Hadoop RDBMS (see Figure 1 above),

where Hadoop handles the scale out,

and the RDBMS functionality supports

structured data and reliable real-time

updates. With this setup, the operational

data lake is never overwhelmed like atraditional ODS. It’s nearly bottomless

or limitless in its scalability—companies

can continue to add as much data as they

want because expansion costs so little.

With the data lake, users can extract

structured metadata from unstructured

data on a regular basis and store it in the

operational data lake for quick and easy

querying, thus enabling better real-time

data analysis. And, just as importantly,

because all data is in a single location, the

operational data lake enables easy queriesacross structured and unstructured data

simultaneously.

Finally, unlike native Hadoop, an

operational data lake can handle CRUD

(create, read, update, delete) operations

in a highly concurrent fashion. The

system can handle truly structured data

in real time, while using transactions to

ensure that updates are completed in a

reliable manner.

In the following section, a case study

is presented to illustrate the power of the

operational data lake in the enterprise.

The case study specifically demonstrateshow a Hadoop RDBMS, such as Splice

Machine, can bring significant business

value to digital marketers using an

operational data lake as a unified

customer profile.

SPLICING TOGETHER A

SOLUTION: A CASE STUDY

Marketing services company Harte

Hanks needed to power its campaign

management and BI applications to

deliver 360-degree customer views toits client base, but found that its queries

were slowing to a crawl, taking half an

hour to complete in some cases. Given the

company’s prediction that its data would

grow by 30% to 50%, query performance

would only get worse.

Harte Hanks replaced its Oracle

RAC databases with Splice Machine, a

Hadoop RDBMS, thereby experiencing

a 3-to-7 fold increase in query speeds

at a cost that is 75% less than its Oracle

implementation.Splice Machine allows Harte Hanks

to seamlessly support their OLTP and

OLAP processes all previously powered by

Oracle RAC:

• IBM Unica for campaign management

• IBM Cognos for business intelligence

• Harte Hanks Trillium for data quality

• Ab Initio for ETL

With this operational data lake

powered by Splice Machine, Harte Hanks

can now provide real-time campaign

management more cost-effectively, easily

scaling out to hundreds of terabytes byadding commodity servers.

CONCLUSION

Creating a Hadoop-based operational

data lake to support core applications

and services involves selecting scale-

out technologies that can effectively

encompass the best of all worlds. A

Hadoop RDBMS like Splice Machine

brings together the scalability of Hadoop,

the ubiquity of industry-standard SQL,

and the transactional integrity of a fullyACID-compliant RDBMS.

An operational data lake can be an

excellent way of implementing a hybrid

architecture approach to not only ride

the wave of big data, but also ensure that

businesses face smooth sailing in the

future.

SPLICE MACHINE

To learn more about how Splice

Machine can power an operationaldata lake for your enterprise, visit

www.splicemachine.com.

Powering Real-Time Applications andOfoading Operational ReportsWith an Operational Data Lake

Figure 1.OperationalData LakeArchitecture

http://www.splicemachine.com/


9/9


ORGANIZATIONS LOOK TO DATA to provideanswers, but most are ingesting it at a

volume, speed and variety that creates

the even bigger challenge of making

sense of it. Due to the tradition of

keeping operational and analytical data

separate in legacy environments, the

increasing need for processing structured,

unstructured and semi-structured data,

and the convergence of both volatile

and non-volatile storage underneath

these workloads, data management

is threatening to become increasinglyburdened by complex architectures and

unsolvable pain points rather than a

source of insight. And “Big Data” quickly

becomes just that—a huge pile of data

creating a big headache.

HYBRID TRANSACTIONAL AND

ANALYTICAL PROCESSING STILL

EARLY, BUT GAINING TRACTION

Historically, two types of data

processing environments have developed

due to different characteristics ofanalytics (OLAP) and transactional

(OLTP) workloads, and the reluctance of

performing analytical processing on live

transactional data. OLAP often requires ad

hoc exploratory capabilities and doesn’t

have strong SLAs, while OLTP almost

always demands strong performance and

SLA’s for data consistency.

However, increasing demand for real-

time analytics, which allows instantaneous

business intelligence and decision making,

is forcing many enterprises to rethink thefundamental premises behind OLAP

and OLTP. Surging innovations in the

area of In-Memory Computing provide

the technological underpinning for the

new software infrastructure for emerging

hybrid transactional and analytical

processing (HTAP) workloads. With the

performance and scalability benefits of

In-Memory Computing, Big Data can be

effectively stored and processed in DRAM,

and both analytical and transactional

workloads can be effectively executedwithout a need for two different systems

or ETL data movement processes.

The GridGain In-Memory Data Fabricprovides a unique platform for high

performance data processing of analytical

and transactional workloads without a

need for costly ETL processes from silo-ed

installations. It combines state-of-the-art

transactional processing capabilities with

all key analytical processing features in

one data layer, sharing the same ultra-

high performance characteristic (high

throughput, low latency) of in-memory

processing.

DIVERSE DATA SOURCES

Another interesting aspect of hybrid

data management is the fact that no longer

is there a single data source that serves the

application or a set of applications. The

typical modern composite application

relies on multiple dedicated data sources

such as traditional RDBMS for OLTP,

NoSQL for OLAP and Hadoop for data

warehousing. One of the key challenges of

hybrid data management is the ability to

effectively query and manage data across adiverse set of data sources, while providing

a unified and consistent view on all data to

the applications.

The GridGain In-Memory Data Fabric

provides a data access and processing layer

that takes a holistic view of in-memory

processing as a layer on top of any existing

data source—instead of requiring a costly

replacement of any one of them. GridGain’s

approach allows to ingest new and

traditional data sources without ripping

and replacing existing databases, whileoffering high performance processing of

diverse data sets in a hybrid environment.

DATA PERSISTENCE WITHOUT

PERFORMANCE PENALT Y

Just as Flash technology is quickly

taking the place of spinning disks as

the default storage for many traditional

workloads, RAM—especially emerging

non-volatile DIMM (NVDIMM)

technology —promises long sought-after

data persistence for high-performance,hyper-scale applications. NVDIMM

makes a normal DDR4 memory persistent

and enables dramatic performanceoptimizations for in-memory-based

applications—transactional, analytical

and hybrid (HTAP).

Unlike NAND-based storage which

is always accessed as a block device,

DRAM-based NVDIMM is purely byte-

addressable memory that’s absolutely

identical to a normal DRAM. In fact,

from the application’s standpoint there is

no difference between accessing normal

DRAM or NVDIMM. Most transactional

systems assume a tiered memory hierarchyof volatile memory for processing, and

persistent disk storage (HDD, SSD) for

durability of the data. With NVDIMM,

these systems gain fast and granular

access to persistent storage without the

performance penalty of involving disk-

based storage.

As a leading provider of open source

and commercial in-memory technology,

GridGain Systems is on the forefront of

innovating in the areas of hybrid volatile/

non-volatile memory environments,with the goal to support low-latency

write-though operations for real-time

applications that cannot afford to lose data.

THE PROMISE

Modern in-memory technology

provides the most logical and

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data with the speed and scale necessary to

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dbta best practices going hybrid data management

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