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Duration 14 hours
Course Outline
- Section 1: Introduction to Big Data / NoSQL
- NoSQL overview
- CAP theorem
- Scenarios where NoSQL is appropriate
- Columnar storage concepts
- The NoSQL ecosystem
- Section 2: Cassandra Basics
- System design and architecture
- Cassandra nodes, clusters, and datacenters
- Keyspaces, tables, rows, and columns
- Partitioning, replication, and token allocation
- Quorum mechanisms and consistency levels
- Labs: Interacting with Cassandra using CQLSH
- Section 3: Data Modeling – Part 1
- Introduction to CQL
- CQL data types
- Creating keyspaces and tables
- Selection of columns and data types
- Determining primary keys
- Data layout for rows and columns
- Time to live (TTL) settings
- Querying data with CQL
- Executing CQL updates
- Managing collections (list, map, set)
- Labs: Various data modeling exercises using CQL; experimentation with queries and supported data types
- Section 4: Data Modeling – Part 2
- Creation and application of secondary indexes
- Composite keys (partition keys and clustering keys)
- Handling time series data
- Best practices for time series data management
- Utilizing counters
- Lightweight transactions (LWT)
- Labs: Creating and using indexes; modeling time series data
- Section 5: Cassandra Internals
- Understanding Cassandra's underlying design
- Roles of sstables, memtables, and commit logs
- Section 6: Administration
- Hardware selection criteria
- Available Cassandra distributions
- Communication between Cassandra nodes
- Writing and reading data to/from the storage engine
- Configuration of data directories
- Anti-entropy operations
- Cassandra compaction processes
- Selecting and implementing compaction strategies
- Cassandra best practices (compaction, garbage collection, etc.)
- Creating a test Cassandra instance with a low memory footprint
- Troubleshooting tools and techniques
- Lab: Installing Cassandra and running performance benchmarks
Requirements
- Proficiency in a Linux environment, including command-line navigation and file editing using vi or nano
- For on-site training, a laptop or desktop computer equipped with at least 8 GB of RAM
- For remote training, a functional Cassandra lab environment will be provided; only a web browser is required
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.
