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Course Outline
- Section 1: Introduction to Big Data / NoSQL
- Overview of NoSQL technologies
- Explanation of the CAP theorem
- Determining when NoSQL is the appropriate solution
- Understanding columnar storage
- Surveying the NoSQL ecosystem
- Section 2 : Cassandra Basics
- System design and architectural components
- Understanding Cassandra nodes, clusters, and datacenters
- Structure of keyspaces, tables, rows, and columns
- Concepts of partitioning, replication, and token distribution
- Quorum requirements and consistency levels
- Labs: Interacting with Cassandra via CQLSH
- Section 3: Data Modeling – Part 1
- Introduction to CQL
- Exploration of CQL data types
- Creation of keyspaces and tables
- Selection of appropriate columns and types
- Determining primary keys
- Data layout strategies for rows and columns
- Implementation of Time to Live (TTL)
- Executing queries with CQL
- Performing data updates in CQL
- Managing collections (lists, maps, and sets)
- Labs: Engaging in various data modeling exercises using CQL, focusing on query experimentation and supported data types
- Section 4: Data Modeling – Part 2
- Creation and utilization of secondary indexes
- Working with composite keys (partition keys and clustering keys)
- Handling time-series data
- Best practices for structuring time-series data
- Implementation of counters
- Use of Lightweight Transactions (LWT)
- Labs: Creating and applying indexes; modeling time-series data scenarios
- Section 5 : Cassandra Internals
- Understanding the internal design of Cassandra
- Components: sstables, memtables, and the commit log
- Section 6: Administration
- Hardware selection criteria
- Comparison of Cassandra distributions
- Communication between Cassandra nodes
- Processes for writing to and reading from the storage engine
- Management of data directories
- Anti-entropy operations
- Cassandra compaction mechanisms
- Selecting and implementing compaction strategies
- Cassandra best practices, including compaction and garbage collection
- Setting up a low-memory footprint Cassandra test instance
- Troubleshooting tools and diagnostic tips
- Lab: Installing Cassandra and executing performance benchmarks
Requirements
- Familiarity with the Linux environment, including command-line navigation and file editing using tools like vi or nano
- For on-site sessions, a laptop or desktop computer equipped with 8 GB of RAM
- For remote sessions, a functional Cassandra lab environment will be provided, requiring only a web browser from the participant
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.
