The Apache Cassandra Training is a 32-hour hands-on course focused on building expertise in distributed NoSQL database design and scalable architecture using Cassandra. Known for its high availability and fault tolerance, Apache Cassandra is widely used in modern, data-intensive applications that demand real-time responsiveness and horizontal scalability. This course dives into Cassandra’s peer-to-peer architecture, data modeling techniques, replication strategies, and tunable consistency. Learners will also gain practical experience in setting up Cassandra clusters, writing CQL (Cassandra Query Language), and optimizing performance for production workloads. Ideal for database engineers, backend developers, and data architects, this training provides the essential knowledge to design, deploy, and manage highly scalable NoSQL solutions using Cassandra.
Apache Kafka is an open-source distributed streaming platform that was originally developed by engineers at LinkedIn and later donated to the Apache Software Foundation. The platform is designed to handle large amounts of data in real-time and is used by many companies worldwide, including Uber, Netflix, and Airbnb. Apache Kafka also includes a number of advanced features that make it a powerful tool for real-time data processing. For example, the platform supports stream processing, which allows developers to perform continuous transformations and analysis of data as it flows through the system. Apache Kafka also supports message partitioning, which allows large data streams to be split across multiple brokers for increased performance. Apache Kafka has become one of the most popular open- source projects in the world, with a large and active community of developers contributing to its development and maintenance. The platform has been widely adopted in industries such as finance, healthcare, and e-commerce, and is used by many companies to power their mission- critical data processing and analytics pipelines.
The Apache Storm course is a comprehensive 3–4 day hands-on program designed to equip learners with the skills needed to build and manage real-time data processing systems. Participants will explore Storm’s architecture, including topologies, spouts, and bolts, while learning how to design scalable streaming workflows. The training covers fault tolerance, message reliability, parallelism, and distributed stream processing concepts essential for handling real-time data at scale. Through practical labs and real-world scenarios, learners gain the ability to process continuous data streams efficiently and integrate Storm into modern Big Data ecosystems.
The Apache Beam Training is a focused 3-day program designed to equip data engineers and developers with the skills to build unified batch and stream data pipelines using Apache Beam. This course blends practical labs with real-world case studies, enabling you to design and execute scalable data processing workflows that work seamlessly across multiple execution engines like Apache Flink, Google Cloud Dataflow, and Spark. By the end of the training, you’ll be ready to handle diverse data workloads efficiently and consistently using the powerful abstractions provided by Apache Beam.
The Comprehensive HBase course is a 3–4 day hands-on program designed to teach learners how to effectively work with HBase, the NoSQL database built on top of Hadoop. Participants will explore HBase architecture, tables, column families, regions, and region servers while mastering CRUD operations, data modeling, and performance optimization techniques. The training focuses on practical exercises for managing large-scale, real-time datasets, ensuring high availability, and scaling HBase clusters. By the end of the course, learners will be able to design efficient NoSQL solutions suitable for enterprise-level Big Data applications.
