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Duration 14 hours
Course Outline
- Section 1: Introduction to Big Data / NoSQL
- Overview of NoSQL databases
- CAP theorem
- Criteria for NoSQL appropriateness
- Columnar storage mechanisms
- The NoSQL technology ecosystem
- Section 2 : Cassandra Basics
- System design and architectural principles
- Cassandra nodes, clusters, and datacenters
- Keyspaces, tables, rows, and columns
- Partitioning strategies, replication factors, and token distribution
- Quorum mechanisms and consistency levels
- Labs : Interacting with Cassandra via CQLSH
- Section 3: Data Modeling – Part 1
- Introduction to Cassandra Query Language (CQL)
- CQL data types
- Creation of keyspaces and tables
- Selection of column attributes and data types
- Determination of primary key structures
- Data layout optimization for rows and columns
- Implementation of Time to Live (TTL) policies
- Execution of CQL queries
- Processing CQL update operations
- Utilization of collections (lists, maps, and sets)
- Labs : Various data modeling exercises using CQL; experimentation with queries and supported data types
- Section 4: Data Modeling – Part 2
- Creation and implementation of secondary indexes
- Composite key structures (partition keys and clustering keys)
- Management of time series data
- Best practices for time series data structures
- Use of counters
- Lightweight Transactions (LWT)
- Labs : Creation and utilization of indexes; modeling time series data
- Section 5 : Cassandra Internals
- Analysis of Cassandra’s underlying design architecture
- Management of SSTables, Memtables, and commit logs
- Section 6: Administration
- Hardware selection criteria
- Cassandra distribution options
- Cassandra node communication protocols
- Writing and reading data to and from the storage engine
- Configuration of data directories
- Anti-entropy processes
- Cassandra compaction mechanisms
- Selection and implementation of compaction strategies
- Cassandra operational best practices (compaction, garbage collection)
- Deployment of test Cassandra instances with low memory footprints
- Utilization of troubleshooting tools and diagnostic tips
- Lab : Students install Cassandra and execute benchmark tests
Requirements
- Proficiency in Linux environments (command line navigation, file editing with vi or nano)
- For on-site courses, a laptop or desktop computer equipped with 8 GB of RAM
- For remote courses, a functional Cassandra lab environment will be provided, requiring only a compatible web browser
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.
