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Course Outline

  • Section 1: Introduction to Big Data / NoSQL
    • NoSQL overview
    • CAP theorem
    • When NoSQL is the right choice
    • Columnar storage
    • NoSQL ecosystem
  • Section 2 : Cassandra Basics
    • Design and architecture
    • Cassandra nodes, clusters, and datacenters
    • Keyspaces, tables, rows, and columns
    • Partitioning, replication, and tokens
    • Quorum and consistency levels
    • Labs : interacting with Cassandra using CQLSH
  • Section 3: Data Modeling – part 1
    • Introduction to CQL
    • CQL Data types
    • Creating keyspaces and tables
    • Selecting columns and data types
    • Defining primary keys
    • Data layout for rows and columns
    • Time to live (TTL)
    • Querying with CQL
    • Updating data with CQL
    • Collections (list, map, set)
    • Labs : various data modeling exercises using CQL; experimenting with queries and supported data types
  • Section 4: Data Modeling – part 2
    • Creating and using secondary indexes
    • Composite keys (partition keys and clustering keys)
    • Time series data
    • Best practices for time series data
    • Counters
    • Lightweight transactions (LWT)
    • Labs : creating and using indexes; modeling time series data
  • Section 5 : Data Modeling Labs : Group design session
    • Multiple use cases from various domains are presented
    • Students work in groups to develop designs and models
    • Discuss various designs and analyze decisions
    • Lab : implement one of the scenarios
  • Section 6: Cassandra drivers
    • Introduction to the Java driver
    • CRUD (Create, Read, Update, Delete) operations using the Java client
    • Asynchronous queries
    • Labs : using the Java API for Cassandra
  • Section 7 : Cassandra Internals
    • Understanding Cassandra's underlying design
    • SSTables, memtables, and commit log
    • Read and write paths
    • Caching mechanisms
    • Virtual nodes (vnodes)
  • Section 8: Administration
    • Hardware selection
    • Cassandra distributions
    • Cassandra best practices (compaction, garbage collection)
    • Troubleshooting tools and tips
    • Lab : students install Cassandra and run benchmarks
  • Section 9: Bonus Lab (time permitting)
    • Implement a music service like Pandora or Spotify on Cassandra

Requirements

  • Familiarity with the Java programming language
  • Comfortable using the Linux environment (navigating the command line, editing files with vi / nano)
 21 Hours

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