Cassandra Certification Training

Learning Objective:

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.

Topics:

• 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

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.

Topics:

• 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

Learning Objective:

Learn to start the cluster, examine its performance, make an adjustment, and test.

Topics:

• 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

Learning Objective:

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.

Topics:

• 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.

Learning Objective:

Learn to scale your cluster horizontally, as well as to remove and replace failed nodes.

Topics:

• 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.

Learning Objective:

Learn about the wide variety of options available for monitoring and logging for Apache Cassandra, which will help in identifying issues proactively.

Topics:

• 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.

Learning Objective:

Learn the correct use cases and database selection. Discover the DataStax Java driver, its behaviors and configurations, and how it interacts with Apache Cassandra.

Topics:

• 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

Hands-on:

• Select Driver
• Appropriate connection properties
• Handling simple and complex result sets in Java
• Loading data without overwhelming your nodes.

Learning Objective:

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.

Topics:

• 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.