Introduction to Cassandra
Learning Objectives: Get introduced to Apache Cassandra and some of its design considerations and components and learn about various use cases of Cassandra.
- Differences between NoSQL and RDBMS
- Replication in RDBMS
- Key Challenges with RDBMS
- Schema
- Advantage & Limitation
- Key Characteristics of No SQL Data Base
- Advantages of Cassandra
- Where and when to use it?
- Brewers CAP Theorem
- Cassandra Key Features
- Distributed and Decentralised
- Elastic Scalability
- High Availability and Fault Tolerance
- Tuneable Consistency
- Strict Consistency
- Casual Consistency
- Weak (Eventual Consistency)
- Column Orientation
- Column Orientation
- Introduction to Cassandra
- USE Cases for Cassandra
Getting Started with Cassandra
Learning Objectives: Install and configure Cassandra. Build your own local, single-node cluster. Learn about CCM with some basic commands with Cassandra's nodetool.
- Installation
- Configuration
- Starting Cassandra
- Cassandra Cluster Manager
- Introduction to the data model
- Shutting down Cassandra
- Installation and configuration
- Starting up and shutting down Cassandra
Cassandra Data Model
Learning Objectives: Learn to run Command-Line Client Interface, connect to a server. Also, learn about the relational data model, design differences between RDBMS and Cassandra
- Installation
- Running the Command-Line Client Interface
- Basic CLI Commands, Help
- Connecting to a Server, Describing the Environment
- Creating and Keyspace and Column Family
- Writing and Reading Data
- The Relational Data Model
- Cluster
- Keyspaces
- What is Column oriented database
- Column Families
- Column Family Options
- Columns
- Wide Rows
- Skinny Rows
- Column Sorting
- Super Columns
- Composite Keys
- Design Differences between RDBMS and CASSANDRA
- Query Language
- Referential Integrity
- Secondary Indexes
- Sorting, DeNormalisation
- Design Patterns
- Materialized Views
Steps in Configuration
Learning Objectives: Learn to configure a data model.
- Token calculation
- Configuration overview
- Node tool
- Validators
- Comparators
- Expiring column
Hands-on:
- Configure a data model using Token calculation, node tool, validators, comparators.
Cassandra Architecture
Learning Objectives: Learn about the concepts that influenced Cassandra's design and use. Understand Brewer's CAP theorem data-distribution and partitioning; Cassandra's read and write paths; how data is stored on-disk; inner workings of components such as the snitch, tombstones, and failure-detection; and the delivered security features.
- Cassandra's ring architecture
- Cassandra's write path
- Cassandra's read path
- On-disk storage
- Additional components of Cassandra
Hands-on:
Problems that Cassandra was designed to solve
- Cassandra's read and write paths
- The role that horizontal scaling plays
- How data is stored on-disk
- How Cassandra handles failure scenarios
Cassandra Query Language (CQL)
Learning Objectives: Learn about CQL, its syntax and usage and evolution as a language and comparing some of its capabilities to the well-known SQL of the relational database world.
- Overview of Cassandra Data Modeling
- cqlsh
- Getting started with CQL
Hands-on:
Build primary keys that facilitate high-performing data models at scale
- Use CQL syntax and solve different types of problems using it
Configuring a Cluster
Learning Objectives: Learn to start the cluster, examine its performance, make an adjustment, and test.
- Evaluating instance requirement
- Operating systems optimization
- Configuring the JVM
- Configuring Cassandra
Hands-on:
Sizing hardware and computer resources for Cassandra deployments
- Operating system optimizations
- Configuring the JVM
- Configuring Cassandra
Performance Tuning
Learning Objectives: Learn about Cassandra-Stress and how to establish a performance baseline for a specific data model. Evaluate factors that can influence write performance. Understand read performance, and the different configuration properties that can help Apache Cassandra perform well during read-heavy and mixed workloads.
- Cassandra Stress
- Write performance
- Read performance
- Other performance considerations
Hands-on:
Using the Cassandra-Stress tool discover opportunities for improvement
- Looking into situations to apply different table-compaction strategies
- Examining Apache Cassandra's cache and compression options
- Improving upon the efficiency of the JVM
- Optimizing network settings and configuration to avoid performance bottlenecks.
Managing a Cluster
Learning Objectives: Learn to scale your cluster horizontally, as well as to remove and replace failed nodes.
- Add/Remove Nodes
- Scaling Up
- Scaling Down
- Backing up and restoring data
- Maintenance
Hands-on:
Adding and removing nodes
- Working with logical data centers
- Backups
- Techniques for ensuring data consistency.
Monitoring
Learning Objectives: Learn about the wide variety of options available for monitoring and logging for Apache Cassandra, which will help in identifying issues proactively.
- JMX interface
- Node tool utility
- Metric stack
- Log stack
- Troubleshooting
Hands-on:
- Understand different monitoring and logging tools, and how they provide more insight for problem solving on your cluster.
- Make decisions using reliable out-of-the-box applications from the open source community, including installing, configuring, analyzing, and setting up alerting.
Application Development
- Common mistakes made at the application and data model levels
- Driver selection
- Appropriate connection properties
- Handling simple and complex result sets in Java
- Loading data without overwhelming your nodes
Learning Objectives: Learn the correct use cases and database selection. Discover the DataStax Java driver, its behaviors and configurations, and how it interacts with Apache Cassandra.
Hands-on:
- Select Driver
- Appropriate connection properties
- Handling simple and complex result sets in Java
- Loading data without overwhelming your nodes.
Integrating Cassandra with Apache Spark
Learning Objectives: Learn about Spark architecture, which stands on top among other sets of available tools; it offers ease of installation and a huge community, as well as backing up on Hadoop for data warehousing. Get to know the different ways of installation, along with a custom all-in-one Docker image, which has Apache Cassandra, a monitoring stack, and Spark including PySpark, SparkR, and Jupyter with their dependencies.
- Spark (architecture, installation, and configuration)
- PySpark
- SparkR
- Read, transform, and write
- The Jupyter web interface
Hands-on:
- Read, transform, and write
- Work with Jupyter web interface.
