Overview
As big data sweeps the world there has been a growing need for databases that provide scalable and flexible storage solutions. NoSQLs are the answer to this and among the most popular NoSQLs is Cassandra helping organizations handle complex web applications and data proliferation. This course will help you master Apache Cassandra and take up industry challenges with success.
The NoSQL market is expected to reach $4.2 Billion globally by 2020. Cassandra holds a major share of this market due to its open source nature, peer to peer architecture, elastic scalability, high availability and performance and fault tolerance. This course will introduce you to the fundamentals of Cassandra, and with hands-on exercises you will go from learning about the basics to the more advanced concepts such as Cassandra Data models, Architecture, configuration, read and write data and integration with Hadoop.
Enrol now and make most use of the popularity of this technology to make a high profile career.
What You Will Learn:
How and where to use Cassandra and the core concepts that drive this database
Learn how to use the fault-tolerant and high availability feature of Cassandra
Understand the Apache Cassandra architecture
Understand complex inner workings such as gossip protocol, read repairs and Merkle trees
Identify requirements and create a Cassandra data model by applying data modelling techniques
Integrate Cassandra with Hadoop and use tools like Pig and Hive
Prerequisite
- Basic knowledge of Linux command line
- Basic knowledge of Java
- Basic knowledge of database or data-warehouse concepts
Who Should Attend
- Engineers working with large volume websites
- Those expected to design fault tolerant database systems
- Students and individuals who wish to learn more about Cassandra
Knowledgehut Experience
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Learn through Doing
Learn theory backed by practical case studies, exercises and coding practice that will make you ready for the industry.
Mentored by Industry Leaders
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Advance from the Basics
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Code Reviews by Professionals
Get reviews and feedback on your final projects from professional developers.
Curriculum
Learning Objective:
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
Learning Objective:
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
Hands-on:
- Installation and configuration
- Starting up and shutting down Cassandra
Learning Objective:
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
Hands-on:
Run Command- Line Client Interface. Read and write data.
Learning Objective:
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.
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.
- 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 Objective:
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
Learning Objective:
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
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.
- 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.
- 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.
- 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.
- 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.
- 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.
Project
Project
You will build Cassandra applications.
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The Course
The average pay for a Cassandra professional is $121,806/year with salaries going upto $202,000 annually. This goes to show the popularity of Cassandra among organizations seeking to hire NoSQL experts. This course is your opportunity to learn and master Cassandra and become one among these high earning Cassandra professionals.
After completing our course, you will be able to:
- Get a solid understanding of the concepts of Cassandra and know how to use it
- Learn to harness the capabilities of this database to run high volumes of data
- Learn how to scale-up or scale-down the architecture and perform read and write operations
- Design and model a Cassandra application
- Integrate Cassandra with Hadoop and learn how to analyse data
- Understand how to ensure authentication and authorization in Cassandra
By the end of this course you would have worked on projects and hands-on tutorials to gain a complete and thorough understanding of Cassandra.
There are no restrictions but participants would benefit if they have basic programming knowledge.
Yes, KnowledgeHut offers this training online.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Cassandra experts who have years of industry experience.
Finance Related
Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details: http://www.knowledgehut.com/refund
KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.
The Remote Experience
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
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- A modern web browser such as FireFox, Chrome
- Internet Connection