EDW Webinar: A Sneak Peek at Enterprise Data Innovations

Tony ShawCEO, DATAVERSITY

Mod

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www.enterprisedataworld.com

#EDW14om

Copyright © 2014, EnterpriseWeb LLCEnterpriseWeb is a Registered Trademark of EnterpriseWeb LLC.

All rights reserved.

MDM: Master Data Management or Massive Data Mistake?

Jason BloombergChief EvangelistEnterpriseWeb

The Myth of the Master Data Model

• “If only we had a single data model, a single view of the truth, then all our data problems would be solved…”

Copyright © 2014, EnterpriseWeb LLC 2

What is MDM?

Master data management (MDM) comprises the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference (Wikipedia)

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Massive Data Mistake?

• Consistency?– Assumes a static organization

• Single point of reference?– Whose point of

reference, anyway?– What if that point

of reference doesn’t correspond to your line of business?

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Millions Down the Drain

• Multi-million dollar MDM efforts• Far more difficult than predicted• Illusory vision of a perfectly organized

data world


Not the Way People Work

• Not the way that business - or people - understand and use information

• Traditional technology approaches will never solve the Master Data Management problem

• Every department, in fact, every interaction, has its own business context

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Shoehorns don’t work!

• Business context is inherently dynamic• Information is inherently context-sensitive

& always in flux

• Expecting any organization to shoehorn their information into some techie ideal of a master data model is woefully unrealistic

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No More Static!

• Static data models & static APIs never support agility– Only good for fixed and

standardized purposes– Don’t respond to changing

or varied business context

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No More Shoehorns!

• Instead of shoehorning data into an unrealistic technical ideal, let’s build better technology that truly supports the business


Logic Mixed/Schemaless Data

SchemaSchema Schema

SmartAlex™

Dealing with Real-Time Context

Process metadata, data, and logic for each interaction in real time – across the entire distributed system


EnterpriseWeb: Paradigm Shift

Manage data that support dynamic business context in a

Cloud-friendly, enterprise-class environment


EnterpriseWeb®

Dynamic Business Context• Unified data architecture • Seamlessly correlates system-

wide activity for real-time 360o

views of anything• Dynamic constraint satisfaction

• Supports integrated operations– Linked processes & cross-

process governance

• Solves MDM context problem!

Context “A” Context “C”Context “B”

Systemwide Context

Resolution


Copyright © 2014, EnterpriseWeb LLCEnterpriseWeb is a Registered Trademark of EnterpriseWeb LLC.

All rights reserved.

Jason Bloomberg

Chief Evangelist, EnterpriseWeb

[email protected]

@theebizwizard

Moving Hadoop Beyond Batch Analytics to Power Real-Time Apps

Hadoop – Not Just for Data Scientists Anymore

Distributed File System

Java MapReduce Programs

Read-Only

Batch Analytics

Distributed RDBMS

SQL-99 Queries

Real-Time Updates with ACID transactionsReal-Time Apps and Analytics

1

Presenter

Presentation Notes

To date, most Big Data and Hadoop use cases have focused on ETL and batch analytics. However, there is a new class of Big Data use cases emerging - powering real-time applications and analytics. What do we mean by real-time? We mean real-time, interactive queries on data updated in real-time. This is usually the realm of traditional databases such as Oracle and MySQL, but they lack the ability to scale-out on commodity hardware, which is the hallmark of Big Data technologies. Now RDBMS technology is being married to scale-out technology of Hadoop. This enables a whole new class of real-time Big Data use cases.

TheHadoopRDBMS

Standard ANSI SQLHorizontal Scale-OutReal-Time Updates

ACID TransactionsPowers OLAP and OLTPSeamless BI Integration

Splice Machine

Splice Machine Proprietary and Confidential2

Presenter

Presentation Notes

The only Hadoop RDBMS, Splice Machine is designed to scale real-time applications using commodity hardware without application rewrites. Splice Machine replaces traditional RDBMSs such as Oracle and MySQL that are experiencing scaling or cost issues. As a transactional SQL-on-Hadoop RDBMS, Splice Machine helps customers power real-time, Big Data analytics and applications.

How We Do It

3

Presenter

Presentation Notes

Splice Machine marries two proven technology stacks: Apache Derby and HBase/Hadoop. Splice Machine integrates these technology stacks by replacing the storage engine in Apache Derby with HBase. Splice Machine retains the Apache Derby parser, but it redesigned the planner, optimizer, and executor to leverage the distributed HBase computation engine. HBase co-processors are used to embed Splice Machine in each distributed HBase region (i.e., data shard). This enables Splice Machine to achieve massive parallelization by pushing the computation down to each distributed data shard.

Case Study: Digital Marketing

Powers Unica app and CognosScale-out with commodity serversMade queries 3x-7x faster

Achieved over 10x price/perfimprovement

Replaced Oracle RAC DB Initial Results

Clients Consumers

Unica

Real-Time Personalization

Real-Time Actions

Cross-Channel Campaigns

Oracle

4

Presenter

Presentation Notes

Splice Machine has helped customers in digital marketing, ad tech, and telecom industries. For instance, one of our customers is a marketing services provider that uses IBM Unica software to execute real-time campaigns for major retail brands such as Sephora and Petsmart. They were experiencing cost and scaling issues with one of their 20TB Oracle RAC databases. When they replaced their Oracle RAC database with a Hadoop-based RDBMS, they saw queries speed up by 3x-7x, while costs dropped by about 4x. This lead to an overall price/performance of over 10x.

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Technology

EDW Webinar: A Sneak Peek at Enterprise Data Innovations