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Duration 14 hours
Course Outline
- Section 1: Introduction to Big Data / NoSQL
- Overview of NoSQL
- The CAP theorem
- Scenarios suitable for NoSQL
- Columnar storage mechanisms
- The broader NoSQL ecosystem
- Section 2: Cassandra Basics
- System design and architectural layout
- Management of Cassandra nodes, clusters, and datacenters
- Structure of keyspaces, tables, rows, and columns
- Partitioning strategies, replication factors, and token distribution
- Quorum mechanisms and consistency levels
- Laboratories: Interaction with Cassandra via CQLSH
- Section 3: Data Modeling – Part 1
- Foundations of CQL
- Supported CQL data types
- Procedure for creating keyspaces and tables
- Selection of appropriate columns and types
- Determining primary key structures
- Data arrangement for rows and columns
- Time to live (TTL) configuration
- Executing queries via CQL
- Performing CQL updates
- Working with collections (list / map / set)
- Laboratories: Diverse data modeling tasks using CQL; experimentation with queries and compatible data types
- Section 4: Data Modeling – Part 2
- Implementation and utilization of secondary indexes
- Composite keys (partition keys and clustering keys)
- Handling time-series data
- Optimal practices for time-series data management
- Usage of counters
- Lightweight transactions (LWT)
- Laboratories: Construction and application of indexes; modeling time-series data
- Section 5: Cassandra Internals
- Analyzing the internal design of Cassandra
- Components such as sstables, memtables, and the commit log
- Section 6: Administration
- Criteria for hardware selection
- Overview of Cassandra distributions
- Communication protocols between Cassandra nodes
- Data writing and reading operations to/from the storage engine
- Configuration of data directories
- Anti-entropy processes
- Mechanisms of Cassandra Compaction
- Selection and deployment of compaction strategies
- Best practices in Cassandra (covering compaction, garbage collection, etc.)
- Setup of a low-memory Cassandra test instance
- Diagnostic tools and troubleshooting advice
- Laboratory: Installation of Cassandra and execution of performance benchmarks
Requirements
- Familiarity with Linux environments (specifically command-line navigation and file editing using vi / nano)
- For in-person training, a laptop or desktop equipped with 8 GB of RAM
- For remote training, a functional Cassandra laboratory environment will be supplied, requiring only a web browser on your end
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.
