1. HBase Presented by Chintamani Siddeshwar Swathi Selvavinayakam http://www.slideshare.net/amansk/hbase-hadoop-day-seattle-4987041

2. HBase Open source BigTable HDFS as underlying DFS ZooKeeper as lock service Tight integration with Hadoop MapReduce

3. Why HBase ? Scales out to thousands of nodes Access granularity is a row – read/write to a single row is atomic Designed for workloads consisting of simple operations on individual items Provides efficient access to random rows Allows dynamic repartitioning of data

4. Data Model Sparse Distributed multi dimensional persistent Sorted map (row, column, timestamp) -> cell Column = Column Family : Column Qualifier

6. System Structure

7. Other Features Compression In memory column families Multiple masters Rolling restart Bloom filters Efficient bulk loads Source and sink for Hive, Pig, Cascading

8. Use Cases Mozilla Yahoo! Twitter Facebook Adobe

9. HBase v/s RDBMS Column Oriented Flexible schema, add columns on the fly Good with sparse tables No query language De-normalize your data No transactions Row Oriented ( mostly) Fixed schema Not optimized for sparse tables SQL Normalize as you can Transactional

10. Related Chapters Big Data Data Modelling

11. References http://ofps.oreilly.com/titles/9781449396107/intro.html http://wiki.apache.org/hadoop/Hbase/DataModel http://www.slideshare.net/amansk/hbase-hadoop-day- seattle-4987041 http://www.slideshare.net/amansk/hbase-hadoop-day- seattle-4987041 http://static.googleusercontent.com/external_content/untrust ed_dlcp/labs.google.com/en/us/papers/bigtable-osdi06.pdf http://static.googleusercontent.com/external_content/untrust ed_dlcp/labs.google.com/en/us/papers/bigtable-osdi06.pdf