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Course Outline

  • Section 1: Introduction to Big Data / NoSQL
    • Overview of NoSQL
    • The CAP theorem
    • When NoSQL is appropriate
    • 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 Datatypes
    • Creating keyspaces and tables
    • Selecting columns and types
    • Selecting primary keys
    • Data layout for rows and columns
    • Time to live (TTL)
    • Querying with CQL
    • CQL updates
    • 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: Cassandra Internals
    • Understanding Cassandra’s underlying design
    • SSTables, memtables, and commit log
  • Section 6: Administration
    • Hardware selection
    • Cassandra distributions
    • Cassandra node communication
    • Writing and reading data to/from the storage engine
    • Data directories
    • Anti-entropy operations
    • Cassandra compaction
    • Choosing and implementing compaction strategies
    • Cassandra best practices (compaction, garbage collection)
    • Creating a test Cassandra instance with a low memory footprint
    • Troubleshooting tools and tips
    • Lab: Students install Cassandra and run benchmarks

Requirements

  • Familiarity with the Linux environment (including command-line navigation and file editing with vi or nano)
  • For on-site courses: a laptop or desktop with at least 8 GB of RAM
  • For remote courses: a functional Cassandra lab environment will be provided; participants only need a web browser
 14 Hours

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