Learning Apache Cassandra [Video]

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Learning Apache Cassandra [Video]

Tomasz Lelek

Build an efficient, scalable, fault-tolerant, and highly-available data layer into your applications by managing large amount of data using Apache Cassandra

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Video Details

ISBN 139781787126190
Course Length3 hours 04 minutes

Video Description

Cassandra is a NoSQL database with decentralized, fault-tolerant, scalable, and low-cost features, making it a core component of cloud computing systems. The more recent versions have greatly improved the security features, making it suitable for use in enterprise systems.

In this tutorial, you’ll see how Cassandra overcomes the challenges that relational databases face during high scalability demand. You will become familiar with the Cassandra terminologies, components, and their roles. Then you will learn how to create a multi-node Cassandra structure, understand the roles and responsibilities of Cassandra components, and see the data flow during database operations that demand speed, accuracy, and durability.

You will then see how Cassandra stores data onto files on the disk, how to optimize those files to improve performance, and how to monitor the Cassandra database performance using logs and metrics.

We’ll demonstrate the factors that could affect the performance SLAs of the Cassandra database. Next, you will learn how to optimize the data model to provide performance guarantees and consistent performance SLA over time. You’ll also learn how to build the data model on Cassandra and integrate the database with your application.

In the later sections, you’ll connect with Cassandra from Spark to read and write data. You’ll integrate Cassandra with Spark and learn how to process live streaming data with Spark and persist the data in Cassandra for consumption through the downstream system.

By the end of the course, you’ll be able to build powerful, scalable Cassandra database layers for your applications. You’ll design rich schemes to capture the relationships between different data types and master the advanced features available in Cassandra.

Style and Approach

This course is a comprehensive tutorial that teaches the basic and not so basic concepts of Apache Cassandra using step-by-step instructions. 

Table of Contents

Introduction to Cassandra
The Course Overview
What Is Apache Cassandra?
Key Space, Table Schema, Partition Key, and Clustering Key
Start a Single Node Cassandra Database
Introduction to Cqlsh Command Line Client
Loading and Reading Data
Cassandra Distributed Architecture
Node and Ring Structure
Replication and Consistency Model
Racks and Datacenters
CAP Theorem
Gossip
Read Repair, Hinted Handoff
Diagnostics
Understanding Files in the Data Directory
Use Nodetool to Examine Performance Statistics
System and Output Logs
JMX to Monitor Metrics
Choosing the Appropriate Compaction Strategy
Data Modelling Principles
Primary Key and Cluster Ordering
Denormalization and Design for the Read Performance
Optimizing for BlindWrites
Data Modelling in Cassandra
Collection Types
Static Columns
Indexes, Materialized Views
Data Aggregation
compareAndSet
Counter Type
Optimization of Data
The Impact of Frequent Updates and Delete
Wide Rows and Primary Key Considerations
Load Testing with CQL Stress
Logged and Unlogged Batching
Integrating Cassandra Database with Your Application
A Maven Project Using the Java Driver
Connection Information for the Driver
Basic Statements
Using Prepared Statements
Understanding Errors
Overview of Apache Spark
A What Is Apache Spark and Spark Architecture
Get Started with Spark
Working with Spark’s Data Structures – RDD, Data Frame, and Dataset
Setting Up the Spark Connector
Connecting Spark with Cassandra
Writing Data to Cassandra from Spark
Reading Data from Cassandra Using Spark RDD
Join, Aggregate Data Using Spark Data Frame API and Spark SQL
Cassandra Aware Partitioning in Spark
Integrate Cassandra with Spark Streaming
Use Cases for Near Real Time Stream Processing Using Spark Streaming
Advanced Stream Receiver Using Kafka Connectors
Stateless and Stateful Transformations
Persistence of Live Stream on to Cassandra

What You Will Learn

  • Scale up the relational databases and how no SQL databases like Cassandra overcome them
  • Understand the architecture of Apache Cassandra and how the data are stored
  • Use the different components of Cassandra; the read path, write path, fault tolerance, replication, consistency model, anti-entropy model as per what your application needs
  • Start a Cassandra multi-node cluster and understand the role of each critical piece of the distributed system and their interplay
  • Learn the principles and methodologies for data modelling in Cassandra
  • Integrate the database with your application
  • Migrate existing data from relational databases
  • Learn how to process live streaming data with Spark and persist the data on to Cassandra for consumption through a downstream system

Authors

Table of Contents

Introduction to Cassandra
The Course Overview
What Is Apache Cassandra?
Key Space, Table Schema, Partition Key, and Clustering Key
Start a Single Node Cassandra Database
Introduction to Cqlsh Command Line Client
Loading and Reading Data
Cassandra Distributed Architecture
Node and Ring Structure
Replication and Consistency Model
Racks and Datacenters
CAP Theorem
Gossip
Read Repair, Hinted Handoff
Diagnostics
Understanding Files in the Data Directory
Use Nodetool to Examine Performance Statistics
System and Output Logs
JMX to Monitor Metrics
Choosing the Appropriate Compaction Strategy
Data Modelling Principles
Primary Key and Cluster Ordering
Denormalization and Design for the Read Performance
Optimizing for BlindWrites
Data Modelling in Cassandra
Collection Types
Static Columns
Indexes, Materialized Views
Data Aggregation
compareAndSet
Counter Type
Optimization of Data
The Impact of Frequent Updates and Delete
Wide Rows and Primary Key Considerations
Load Testing with CQL Stress
Logged and Unlogged Batching
Integrating Cassandra Database with Your Application
A Maven Project Using the Java Driver
Connection Information for the Driver
Basic Statements
Using Prepared Statements
Understanding Errors
Overview of Apache Spark
A What Is Apache Spark and Spark Architecture
Get Started with Spark
Working with Spark’s Data Structures – RDD, Data Frame, and Dataset
Setting Up the Spark Connector
Connecting Spark with Cassandra
Writing Data to Cassandra from Spark
Reading Data from Cassandra Using Spark RDD
Join, Aggregate Data Using Spark Data Frame API and Spark SQL
Cassandra Aware Partitioning in Spark
Integrate Cassandra with Spark Streaming
Use Cases for Near Real Time Stream Processing Using Spark Streaming
Advanced Stream Receiver Using Kafka Connectors
Stateless and Stateful Transformations
Persistence of Live Stream on to Cassandra

Video Details

ISBN 139781787126190
Course Length3 hours 04 minutes
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