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Course Outline
- Section 1: Introduction to Big Data / NoSQL
- NoSQL overview
- CAP theorem
- When is NoSQL appropriate
- Columnar storage
- NoSQL ecosystem
- Section 2 : Cassandra Basics
- Design and architecture
- Cassandra nodes, clusters, datacenters
- Keyspaces, tables, rows and columns
- Partitioning, replication, tokens
- Quorum and consistency levels
- Labs : interacting with Cassandra using CQLSH
- Section 3: Data Modeling – part 1
- Introduction to CQL
- CQL Datatypes
- Creating keyspaces & tables
- Choosing columns and types
- Choosing 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 work in groups to propose designs and models
- Discuss various designs and analyze decisions
- Lab : implement one of the scenarios
- Section 6: Cassandra drivers
- Introduction to Java driver
- CRUD (Create / Read / Update, Delete) operations using Java client
- Asynchronous queries
- Labs : using Java API for Cassandra
- Section 7 : Cassandra Internals
- Understanding Cassandra design under the hood
- SSTables, memtables, 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 a node crash
- 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 like Pandora / Spotify on Cassandra
Requirements
- Familiarity with the Java programming language
- Proficiency in the Linux environment (navigating the command line, editing files with vi or nano)
Lab environment:
A functional Cassandra environment will be provided for students. Access to the cluster requires an SSH client and a web browser.
Zero Install : Students do not need to install Cassandra on their own machines!
21 Hours
Testimonials (1)
It was informative.
