Big Data 2.0: Hadoop as part of a Near-Real-Time Integrated Data Era
by Hadoop_Summit on Jul 09, 2013
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A new era of big data is coming, an era we would call ?Big Data 2.0,? with characteristics including: 1. The lines between data and metadata, storage and processing logic become further blurred 2. ...
A new era of big data is coming, an era we would call ?Big Data 2.0,? with characteristics including: 1. The lines between data and metadata, storage and processing logic become further blurred 2. Data integration pattern is shifting from ETL (extract, transform and load) to the 3 T?s in Hadoop (transfer, transform and translate) 3. Batch-oriented data pipeline is challenged, even surpassed by stream-based data flow 4. In-memory big data processing emerges as a new promising trend 5. Latency from raw data to business intelligence is dramatically shortened toward real-time or near real-time 6. Hadoop and other No-SQL solutions are further integrated into the same environment 7. Mapping and conversion between relational/row-based and column-based data becomes end-user friendly 8. More ad hoc, interactive, query-based analytics outgrow pure MapReduce 9. Hadoop evolves from data server-centric to client rich 10. Hadoop becomes the centerpiece of enterprise data systems, with roles of database, data warehouse, and data center storage, all in one, as integrated platform and solutions This vision of Big Data 2.0 is based on Sears? research, development and production experience, and best practice in enterprise data solutions, which indicate that Hadoop is ready for its prime time in this new era.
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