What you'll learn
Overview of NoSQL databases and Cassandra's role.
Understanding the architecture and distributed nature of Cassandra.
Designing effective data models for optimal performance.
Mastering the CQL syntax for creating, updating, and querying data.
Setting up and configuring Cassandra clusters.
Ensuring high availability and fault tolerance.
Strategies for optimizing read and write operations.
Connecting Cassandra with popular data processing frameworks.
Best practices for leveraging Cassandra in production environments.
Requirements
Familiarity with fundamental concepts of databases, including tables, queries, and basic data modeling principles.
A basic understanding of NoSQL database concepts can be beneficial but is not mandatory.
Proficiency in a programming language, such as Java, Python, or C++, is recommended. Knowledge of data types, variables, and basic programming constructs will be helpful.
Comfort with using the command line interface and basic Linux commands, as Apache Cassandra is often managed through the command line.
A grasp of distributed system concepts will aid in comprehending the architecture and functioning of Apache Cassandra.
An understanding of data engineering principles, data pipelines, and data processing can enhance the appreciation of how Apache Cassandra fits into the broader data ecosystem.
Students should have the ability to set up a development environment, including installing and configuring software on their machines.
Description
Elevate your expertise in data engineering with our comprehensive "Mastering Apache Cassandra: Essential Skills for Data Engineers" course. Designed for both beginners and experienced professionals, this hands-on training program delves deep into the intricacies of Apache Cassandra, a leading NoSQL database, equipping you with essential skills for managing and processing large-scale distributed data.Key Learning Objectives:Foundational Understanding: Gain a solid grasp of Apache Cassandra's architecture, distributed nature, and its pivotal role in modern data ecosystems.Effective Data Modeling: Master the art of designing data models that optimize performance, considering denormalization strategies and schema design trade-offs.Cassandra Query Language (CQL) Proficiency: Acquire expertise in CQL syntax for seamless data manipulation, covering basic operations, advanced features, and optimization techniques.Cluster Configuration and Deployment: Learn to set up and configure Cassandra clusters with best practices for deployment, scaling, and ensuring high availability.Performance Tuning and Optimization: Identify and resolve performance bottlenecks, implementing strategies to optimize both read and write operations.Scaling and High Availability Strategies: Explore horizontal scaling techniques, add nodes to clusters, and implement robust strategies for high availability and fault tolerance.Data Consistency and Replication: Understand consistency levels and configure data replication to ensure durability and reliability in distributed environments.Monitoring and Troubleshooting: Implement effective monitoring solutions and develop troubleshooting skills to address common challenges in Cassandra deployment.Integration with Data Processing Frameworks: Connect Cassandra seamlessly with popular data processing frameworks, and integrate it into existing data pipelines for comprehensive data solutions.Real-world Use Cases and Best Practices: Apply your knowledge to real-world scenarios and explore best practices for deploying and leveraging Apache Cassandra in production environments.Don't miss this opportunity to unlock the full potential of Apache Cassandra and propel your career in data engineering. Enroll now and embark on a journey towards mastering the essential skills needed for success in the dynamic world of distributed data management.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 What is Apache Cassandra?
Lecture 3 What is No-SQL
Lecture 4 Can Relational Database work for Bigdata
Lecture 5 Tips to Improve Your Course Taking Experience
Section 2: Apache Cassandra Installation
Lecture 6 Installation Steps on Linux machines (Ubuntu)
Section 3: Cassandra Query Language (CQL)
Lecture 7 Data Types (Part 1 Hands On)
Lecture 8 Data Types (Part 2 Hands On)
Lecture 9 Data Types (Part 3 Hands On)
Lecture 10 User-Defined Types
Lecture 11 Data Definition (DDL) - Create Keyspace[Hands On]
Lecture 12 Data Definition (DDL) - USE Keyspace[Hands On]
Lecture 13 Data Definition (DDL) - ALTER Keyspace[Hands On]
Lecture 14 Data Definition (DDL) - DROP Keyspace[Hands On]
Lecture 15 Data Definition (DDL) - CREATE Table[Hands On]
Lecture 16 Data Definition (DDL) - ALTER Table[Hands On]
Lecture 17 Data Definition (DDL) - DROP Table[Hands On]
Lecture 18 Data Definition (DDL) - TRUNCATE Table[Hands On]
Lecture 19 Data Manipulation (DML) - SELECT Statement[Hands On]
Lecture 20 Data Manipulation (DML) - INSERT Statement[Hands On]
Lecture 21 Data Manipulation (DML) - UPDATE Statement[Hands On]
Lecture 22 Data Manipulation (DML) - DELETE Statement[Hands On]
Lecture 23 Data Manipulation (DML) - BATCH Statement[Hands On]
Lecture 24 Arithmetic Operators[Hands On]
Lecture 25 Secondary Indexes[Hands On]
Lecture 26 Functions
Lecture 27 Scalar Functions
Lecture 28 Aggregate Functions
Section 4: Apache Cassandra Architecture
IT beginners,Students and Enthusiasts,Data Science beginners,System Architects,Data Architects,Software Developers,Database Administrators,Data Engineers


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