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Course Outline
- Section 1: Introduction to Big Data & NoSQL
- Overview of NoSQL technologies
- Understanding the CAP theorem
- Determining appropriate use cases for NoSQL
- Concepts of columnar storage
- The broader NoSQL ecosystem
- Section 2: Fundamentals of Cassandra
- System design and architectural components
- Structure of nodes, clusters, and datacenters
- Core concepts: keyspaces, tables, rows, and columns
- Mechanisms for partitioning, replication, and token assignment
- Quorum mechanisms and consistency levels
- Practical Labs: Engaging with Cassandra via CQLSH
- Section 3: Data Modeling – Part 1
- Introduction to CQL syntax
- Supported CQL data types
- Procedures for creating keyspaces and tables
- Strategies for selecting columns and data types
- Defining optimal primary keys
- Internal data layout for rows and columns
- Implementing Time to Live (TTL) features
- Constructing queries using CQL
- Executing update operations in CQL
- Working with collections (lists, maps, and sets)
- Practical Labs: Various CQL data modeling exercises; experimenting with query patterns and supported data types
- Section 4: Data Modeling – Part 2
- Implementation and utilization of secondary indexes
- Structure of composite keys (partition and clustering keys)
- Handling time-series data
- Best practices for time-series data modeling
- Using counters
- Lightweight Transactions (LWT)
- Practical Labs: Creating and utilizing indexes; modeling time-series data scenarios
- Section 5: Cassandra Internals
- Deep dive into Cassandra's internal design
- Key components: SSTables, memtables, and the commit log
- Section 6: Administration & Operations
- Criteria for hardware selection
- Comparing different Cassandra distributions
- Communication protocols between Cassandra nodes
- Processes for writing and reading data to/from the storage engine
- Management of data directories
- Anti-entropy operations
- Mechanisms of Cassandra compaction
- Selection and implementation of compaction strategies
- Operational best practices (including compaction and garbage collection)
- Setting up a test Cassandra instance with a low memory footprint
- Essential troubleshooting tools and diagnostic tips
- Practical Lab: Installing Cassandra and executing performance benchmarks
Requirements
- Proficiency with the Linux operating system, including command-line navigation and file editing using vi or nano
- For in-person sessions, a laptop or desktop equipped with at least 8 GB of RAM is recommended
- For remote sessions, a fully configured Cassandra lab environment will be provided; participants only need access to 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.
