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Course Outline
- Section 1: Introduction to Big Data / NoSQL
- Overview of NoSQL databases
- Explanation of the CAP theorem
- Scenarios where NoSQL is appropriate
- Understanding columnar storage
- The NoSQL ecosystem
- Section 2: Cassandra Basics
- Design and architecture overview
- Components: Cassandra nodes, clusters, and datacenters
- Core concepts: keyspaces, tables, rows, and columns
- Partitioning, replication, and token management
- 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 appropriate columns and types
- Defining primary keys
- Structuring data layout for rows and columns
- Understanding Time to Live (TTL)
- Querying data with CQL
- Updating records via CQL
- Using 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 utilizing secondary indexes
- Composite keys (partition keys and clustering keys)
- Handling time series data
- Best practices for time series data
- Working with counters
- Lightweight transactions (LWT)
- Labs: Creating and using indexes; modeling time series data
- Section 5: Cassandra Internals
- Understanding Cassandra’s internal design
- Core components: SSTables, memtables, and commit log
- Section 6: Administration
- Hardware selection guidelines
- Cassandra distributions
- Node communication in Cassandra
- Writing and reading data to/from the storage engine
- Data directory management
- Anti-entropy operations
- Cassandra compaction mechanisms
- Selecting and implementing compaction strategies
- Cassandra best practices (compaction, garbage collection)
- Setting up 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 / 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
Testimonials (2)
Extensive knowledge of NoSQL environments, not only Cassandra (ex: HADOOP)
Stefan Marcoci - Videotron ltee
Course - Cassandra Administration
The 1:1 style meant the training was tailored to my individual needs.
