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Course Outline
- Section 1: Introduction to Big Data / NoSQL
- NoSQL overview
- CAP theorem
- When to use NoSQL
- Columnar storage
- NoSQL ecosystem
- Section 2: Cassandra Basics
- Design and architecture
- Cassandra nodes, clusters, and datacenters
- Keyspaces, tables, rows, and columns
- Partitioning, replication, and tokens
- Quorum 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
- Selecting columns and data types
- Selecting primary keys
- Data layout for rows and columns
- Time to live (TTL)
- Querying with CQL
- CQL updates
- Collections (list, map, set)
- Labs: various data modeling exercises using CQL; experimenting with queries and supported data types
- Section 4: Data Modeling – Part 2
- Creating and using secondary indexes
- Composite keys (partition keys and clustering keys)
- Time series data
- Best practices for time series data
- Counters
- Lightweight transactions (LWT)
- Labs: creating and using indexes; modeling time series data
- Section 5: Data Modeling Labs: Group Design Session
- Multiple use cases from various domains are presented
- Students collaborate in groups to design models
- Discussion and analysis of various design decisions
- Lab: implement one of the scenarios
- Section 6: Cassandra Drivers
- Introduction to the Java driver
- CRUD (Create, Read, Update, Delete) operations using the Java client
- Asynchronous queries
- Labs: using the Java API for Cassandra
- Section 7: Cassandra Internals
- Understanding Cassandra's underlying design
- SSTables, memtables, and commit log
- Read path / write path
- Caching
- vnodes
- Section 8: Administration
- Hardware selection
- Cassandra distributions
- Installing Cassandra
- Running benchmarks
- Tools for monitoring performance and node activities
- DataStax OpsCenter
- Diagnosing Cassandra performance issues
- Investigating node crashes
- Understanding data repair, deletion, and replication
- Other troubleshooting tools and tips
- Cassandra best practices (compaction, garbage collection)
- Section 9: Bonus Lab (time permitting)
- Implement a music service similar to Pandora or Spotify on Cassandra
Requirements
- Familiarity with the Java programming language
- Comfortable using the Linux environment (navigating the command line, editing files with vi or nano)
Lab Environment:
A functional Cassandra environment will be provided for all students. Access requires only an SSH client and a web browser to connect to the cluster.
Zero Install: There is no need for students to install Cassandra on their personal machines.
21 Hours
Testimonials (1)
It was informative.
