Mastering Apache HBASE with Hands-on

Learn how Hadoop MapReduce schedules and executes distributed data processing on commodity clusters using Java, with map and reduce as core stages.

Harnesses open-source, distributed noSQL architecture built on a DFS to enable fast reads and writes on large tables. Scale linearly by adding machines and replicating data.

Learn the building blocks of HBase, including tables, rows, column families, qualifiers, and cells, and how regions split and scale, with an API for managing tables and storage options.

Master column families in HBase, where each family groups columns and stores them together in a storage file, shaping data layout, with versions, timestamps, and qualifiers.

Explore how column families are stored on disk in HBase, with personal data like employee name and address and demographic data like date of birth and gender organized as key-value files.

Learn how to use ddl commands in hbase to modify column family properties, including max versions and max file size, and to add or delete column families.

Discover how HFile stores data in HBase, using blocks and indexes to enable fast reads, with immutable data, delete markers, compression, and a log-backed write path.

Learn how hbase filters narrow results using get and scan operations, reducing network bandwidth by applying custom filters to subset data, with shell commands to explore options.