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Duration 14 hours
Course Outline
- Section 1: Introduction to Big Data and NoSQL
- Overview of NoSQL databases
- Understanding the CAP theorem
- Determining when NoSQL is the appropriate choice
- Columnar storage concepts
- The broader NoSQL ecosystem
- Section 2: Cassandra Fundamentals
- System design and architecture
- Understanding nodes, clusters, and datacenters
- Keyspaces, tables, rows, and columns
- Partitioning, replication, and token management
- Quorum mechanisms and consistency levels
- Labs: Interacting with Cassandra via CQLSH
- Section 3: Data Modeling – Part 1
- Introduction to CQL
- Supported CQL data types
- Creating keyspaces and tables
- Selecting appropriate columns and data types
- Determining primary key structures
- Understanding data layout for rows and columns
- Implementing Time to Live (TTL)
- Executing queries with CQL
- Performing data updates in CQL
- Working with collections (lists, maps, and sets)
- Labs: Engaging in various data modeling exercises with CQL; experimenting with query formulations and supported data types
- Section 4: Data Modeling – Part 2
- Creating and leveraging secondary indexes
- Using composite keys (partition keys and clustering keys)
- Handling time series data
- Best practices for time series implementations
- Utilizing counters
- Lightweight transactions (LWT)
- Labs: Building and utilizing indexes; modeling time series data scenarios
- Section 5: Inside Cassandra
- Understanding the internal design and mechanics
- Reviewing sstables, memtables, and the commit log
- Section 6: Administration and Operations
- Selecting appropriate hardware
- Overview of available Cassandra distributions
- Node-to-node communication protocols
- Data read and write processes within the storage engine
- Managing 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 practical tips
- Lab: Installing Cassandra and executing performance benchmarks
Requirements
- Familiarity with the Linux environment, including navigating the command line and editing files using 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; participants only need a 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.
