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.
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
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Learn theory backed by practical case studies, exercises and coding practice that will make you ready for the industry.
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Get introduced to Apache Cassandra and some of its design considerations and components and learn about various use cases of Cassandra.
Install and configure Cassandra. Build your own local, single-node cluster. Learn about CCM with some basic commands with Cassandra's nodetool.
Learn to run Command-Line Client Interface, connect to a server. Also, learn about the relational data model, design differences between RDBMS and Cassandra.
Run Command- Line Client Interface. Read and write data.
Learn to configure a data model.
Configure a data model using Token calculation, node tool, validators, comparators.
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.
Problems that Cassandra was designed to solve
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.
Build primary keys that facilitate high-performing data models at scale
Learn to start the cluster, examine its performance, make an adjustment, and test.
Sizing hardware and computer resources for Cassandra deployments
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.
Using the Cassandra-Stress tool discover opportunities for improvement
Learn to scale your cluster horizontally, as well as to remove and replace failed nodes.
Adding and removing nodes
Learn about the wide variety of options available for monitoring and logging for Apache Cassandra, which will help in identifying issues proactively.
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.
Learn the correct use cases and database selection. Discover the DataStax Java driver, its behaviors and configurations, and how it interacts with Apache Cassandra.
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.
You will build Cassandra applications.
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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:
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.
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: https://www.knowledgehut.com/refund
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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