Learning Apache Cassandra

Learning Apache Cassandra, available at $34.99, has an average rating of 3.9, with 47 lectures, 10 quizzes, based on 22 reviews, and has 170 subscribers.

You will learn about 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 This course is ideal for individuals who are This course is for anyone who wants to learn more about Apache Cassandra from the ground up and get a solid understanding of its workings. It is particularly useful for This course is for anyone who wants to learn more about Apache Cassandra from the ground up and get a solid understanding of its workings.

Enroll now: Learning Apache Cassandra

Summary

Title: Learning Apache Cassandra

Price: $34.99

Average Rating: 3.9

Number of Lectures: 47

Number of Quizzes: 10

Number of Published Lectures: 47

Number of Published Quizzes: 10

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: $109.99

Quality Status: approved

Status: Live

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

Who Should Attend

This course is for anyone who wants to learn more about Apache Cassandra from the ground up and get a solid understanding of its workings.

Target Audiences

This course is for anyone who wants to learn more about Apache Cassandra from the ground up and get a solid understanding of its workings.

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.

About the Author

Tomasz Lelek is a Software Engineer and Co-Founder of InitLearn. He mostly does programming in Java and Scala. He dedicates his time and effort to get better at everything. He is currently diving into Big Data technologies. Tomasz is very passionate about everything associated with software development.

He has been a speaker at a few conferences in Poland-Confitura and JDD, and at the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference. He was also a speaker for an international event in Dhaka. He is very enthusiastic and loves to share his knowledge.

Course Curriculum

Chapter 1: Introduction to Cassandra

Lecture 1: The Course Overview

Lecture 2: What Is Apache Cassandra?

Lecture 3: Key Space, Table Schema, Partition Key, and Clustering Key

Lecture 4: Start a Single Node Cassandra Database

Lecture 5: Introduction to Cqlsh Command Line Client

Lecture 6: Loading and Reading Data

Chapter 2: Cassandra Distributed Architecture

Lecture 1: Node and Ring Structure

Lecture 2: Replication and Consistency Model

Lecture 3: Racks and Datacenters

Lecture 4: CAP Theorem

Lecture 5: Gossip

Lecture 6: Read Repair, Hinted Handoff

Chapter 3: Diagnostics

Lecture 1: Understanding Files in the Data Directory

Lecture 2: Use Nodetool to Examine Performance Statistics

Lecture 3: System and Output Logs

Lecture 4: JMX to Monitor Metrics

Lecture 5: Choosing the Appropriate Compaction Strategy

Chapter 4: Data Modelling Principles

Lecture 1: Primary Key and Cluster Ordering

Lecture 2: Denormalization and Design for the Read Performance

Lecture 3: Optimizing for BlindWrites

Chapter 5: Data Modelling in Cassandra

Lecture 1: Collection Types

Lecture 2: Static Columns

Lecture 3: Indexes, Materialized Views

Lecture 4: Data Aggregation

Lecture 5: compareAndSet

Lecture 6: Counter Type

Chapter 6: Optimization of Data

Lecture 1: The Impact of Frequent Updates and Delete

Lecture 2: Wide Rows and Primary Key Considerations

Lecture 3: Load Testing with CQL Stress

Lecture 4: Logged and Unlogged Batching

Chapter 7: Integrating Cassandra Database with Your Application

Lecture 1: A Maven Project Using the Java Driver

Lecture 2: Connection Information for the Driver

Lecture 3: Basic Statements

Lecture 4: Using Prepared Statements

Lecture 5: Understanding Errors

Chapter 8: Overview of Apache Spark

Lecture 1: What Is Apache Spark and Spark Architecture

Lecture 2: Get Started with Spark

Lecture 3: Working with Spark’s Data Structures – RDD, Data Frame, and Dataset

Lecture 4: Setting Up the Spark Connector

Chapter 9: Connecting Spark with Cassandra

Lecture 1: Writing Data to Cassandra from Spark

Lecture 2: Reading Data from Cassandra Using Spark RDD

Lecture 3: Join, Aggregate Data Using Spark Data Frame API and Spark SQL

Lecture 4: Cassandra Aware Partitioning in Spark

Chapter 10: Integrate Cassandra with Spark Streaming

Lecture 1: Use Cases for Near Real Time Stream Processing Using Spark Streaming

Lecture 2: Advanced Stream Receiver Using Kafka Connectors

Lecture 3: Stateless and Stateful Transformations

Lecture 4: Persistence of Live Stream on to Cassandra

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 1 votes

3 stars: 4 votes

4 stars: 6 votes

5 stars: 8 votes

Frequently Asked Questions

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