the 3 t's - using hadoop to modernize with faster access to data and value
Post on 13-Sep-2014
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DESCRIPTION
Near real-time, big data analytics is a reality via a new data pattern that avoids the latency and overhead of legacy ETL--the 3 T's of Hadoop: Transfer, Transform, and Translate. Transfer: Once a Hadoop infrastructure is in place, a mandate is needed to immediately and continuously transfer all enterprise data, from external and internal sources and through different existing systems, into Hadoop. Previously, enterprise data was isolated, disconnected and monolithically segmented. Through this T, various source data are consolidated and centralized in Hadoop almost as they are generated in near real-time. Transform: Most of the enterprise data, when flowing into Hadoop, is transactional in nature. Analytics requires data be transformed from record-based OLTP form to column-based OLAP. This T is not the same T in ETL as we need to retain the granularity in the data feeds. The key is to transform in-place within Hadoop, without further data movement from Hadoop to other legacy systems. Translate: We pre-compute or provide on-the-fly views of analytical data, exposed for consumption. We facilitate analysis and reporting, for both scheduled and ad hoc needs, to be interactive with the data for analysts and end users, integrated in and on top of Hadoop.TRANSCRIPT
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The 3 Ts of Hadoop
Wuheng LuoAnkur Gupta
06.2013
The 3 Ts of Hadoop
3-Stage Circular Process of Enterprise Big Data
What is the 3Ts?
3Ts = Transfer, Transform, and Translate
A new enterprise big data pattern to bring disruptive change to conventional ETL To leverage Hadoop for streamlining data processes To move toward real-time analytics
The 3Ts Goal
To simplify enterprise data processing, reduce latency to turn enterprise data from raw form to products of discovery so as to better support business decisions.
The 3Ts One Liners
TransferOnce the Hadoop system is in place, a mandate is needed to immediately and continuously capture and deliver all enterprise data, from all data sources, through all data systems, to Hadoop, and store the data under HDFS.
TransformWhen source data is in, clean, standardize, and convert the data through dimensional modeling. Data transformation should be performed in-place within Hadoop, without moving the data out again for integration reasons.
TranslateFinish the data flow cycle by turning analytical data aggregated in Hadoop to data products of business wisdom. Use batch and streaming tools built on top of Hadoop to Interact with data scientists and end users.
Hadoop as Enterprise Data Hub
“Data Hub” is not a new concept, but:
Conventional Data Hub Hadoop Enterprise Data Hub
RDBMS or EDW based Hadoop ecosystem based
No consistent architectural style:ODS, MDM, messaging or publish-subscribe, etc.
3-phased architecture to cover full enterprise data flow cycle from data source to data products
Heavily reply on ETL 3Ts-driven
Intermediate, partial, siloed True center of enterprise data
… …
TRANSFER
Sourcing Data into Hadoop
Intent
Capture continuously all enterprise data at earliest touch points possible, deliver the data from all sources, through all source data systems, to Hadoop, and store the data under HDFS.
TRANSFER
Motivation
To gain distinctive competing capability, enterprises need to build an integrated data infrastructure as the foundation for big data analytics. Use Hadoop as THE centralized enterprise data repository, and make it the grand destination for all enterprise source data.
TRANSFER
(3 Ts’) Transfer vs. (ETL’s) Extract
Traditional ETL - Extract Hadoop - Transfer
Bottom-up Top-down
Task/project specific Enterprise-wide mandate
Passive Proactive
Data is not available when needed Data is ready when needed
Same datasets are moved around again and again, with no value added
Move the data once, and use it many times, each time with value increased
TRANSFER
Consequences
Before After
Isolated, disconnected in various siloed data/file systems
Consolidated and centralized in Hadoop
Monolithically segmented Heterogeneous, diverse, huge
Separated and partial Federated and holistic
TRANSFER
Implementation
Always do a data gap analysis first
Fork the ingestion in both batch and streaming if needed
Have a delivery plan for the data feed
Synchronize data changes between source system and Hadoop
TRANSFORM
Integrating Data within Hadoop
Intent
Keep the data flow beyond the ingest phase by transforming the data from dirty to clean, from raw to standardized, and from transactional to analytical, all within Hadoop.
TRANSFORM
Motivation
As the latency or speed from raw data to business insight becomes the focal point of enterprise data analytics, use Hadoop as data integration platform to perform in-place data transformation.
TRANSFORM
Implementation
Partition enterprise-wide standardized data and job-specific analytical data in HDFS, and retain history.
Use dimensional modeling to transform and standardize, make dimensional data as the atomic unit of enterprise data.
Identify all enterprise data entities, and add finest grain attributes to each entity as dimensional data.
Take a bottom-up approach, also think about data usage across the enterprise, not specific task bound.
TRANSFORM
(3 Ts’) Transform vs. (ETL’s) Transform
Transform in ETL / ELT Transform in 3 Ts
in vitro, outside Hadoop in vivo, within Hadoop
Use Hadoop as rental space Use Hadoop as integration platform
Non-value adding data movement in between data storage and transformation
Data is transformed while flowing from one partition to another under HDFS
High latency Low latency
Network bottleneck Data locality
TRANSLATE
Making Data Products out of Hadoop
Intent
Turn analytical data into data products of business wisdom using home-made or commercial tools of analytics built on top of Hadoop. Business decisions supported by data products will help generate more new data, thus a new round of enterprise data flow cycle…
TRANSLATE
Motivation
Low-latency big data analytics requires right platform/tools
Use Hadoop as the platform of choice for enterprise data analytics because of its openness and flexibility
Choose analytical tools that are flexible, agile, interactive and user friendly
TRANSLATE
Implementation
Big data analytics takes a team effort Include statisticians, data scientists and developers Utilize both generic and Hadoop specific technologies Consider both batch and streaming based approaches Provide access to pre-computed view and on-the-fly query Use both home-made and Hadoop-based commercial tools Use web-based, mobile friendly UI Visualize
The 3 Ts of Hadoop
Continuous Iteration of Enterprise Data Flow
Thank You!
For further informationemail:
visit:[email protected]
MetaScale is a subsidiary of Sears Holdings Corporation