The Comprehensive Pig course is a 2–3 day hands-on program designed to teach learners large-scale data processing using Apache Pig and Pig Latin. Participants will explore Pig’s architecture, data transformation techniques, optimization strategies, and integration with Hadoop ecosystems. The training covers scripting, workflow automation, User Defined Function (UDF) creation, and performance tuning for efficient processing of structured and unstructured datasets. Through practical exercises and real-world scenarios, learners will gain the skills to handle complex data pipelines and leverage Pig for scalable, high-performance data analytics.
Apache Flink is an open-source stream processing framework that allows you to process massive amounts of data in real-time. It is designed to run batch processing, real-time processing, and stream processing applications with low-latency and high-throughput. The Flink course teaches students how to use Flink to build data processing pipelines and real-time applications. By taking a course in Flink, students can acquire the skills and knowledge necessary to build scalable and efficient real-time data processing applications. They can also learn how to effectively use Flink to handle complex data processing tasks. It supports a wide range of data sources and sinks, including Hadoop Distributed File System (HDFS), Apache Kafka, and Amazon S3.
Hadoop is an open-source framework used for distributed storage and processing of big data. The course of Hadoop is designed to provide students with a comprehensive understanding of the architecture and components of the Hadoop ecosystem, and how to use it effectively to manage, process and analyze large volumes of data. Students have the opportunity to work on real-world projects and case studies, providing them with practical experience using Hadoop in a professional setting. They also have access to a network of Hadoop experts and fellow students, providing them with ongoing support and resources as they continue to learn and use Hadoop. The course of Hadoop is an essential program for anyone involved in big data processing and analysis, including data scientists, data engineers, and software developers. With a comprehensive curriculum covering the architecture and components of the Hadoop ecosystem, the technical aspects of Hadoop, and how to use it for data processing, analysis, and visualization, students are equipped with the skills needed to succeed in a variety of roles in the field of big data.
The HDCD Spark Data Engineering course is a 32-hour in-depth program designed for professionals aiming to master big data processing and engineering. Participants will gain hands-on experience with Hadoop Distributed File System (HDFS), data ingestion, and distributed processing using Apache Spark. The course equips learners with the skills to manage, transform, and analyze large-scale datasets efficiently, enabling them to implement scalable data pipelines and support data-driven decision-making in enterprise environments.
The Kafka Elastic program is a focused, 16–20 hour training designed to equip professionals with end-to-end knowledge of real-time data streaming using Apache Kafka and its seamless integration with Elasticsearch. Through a combination of instructor-led sessions, hands-on labs, and guided use cases, learners will gain practical experience in building scalable data pipelines, managing stream processing, and enabling powerful analytics and monitoring. The course starts with Kafka fundamentals, covering key concepts such as producers, consumers, brokers, topics, partitions, and message delivery guarantees. From there, learners explore real-time streaming workflows and how to architect them for high availability and fault tolerance. The second half of the course focuses on Elasticsearch integration—how to index Kafka data, create dashboards, monitor system performance, and support enterprise-grade analytics. Ideal for engineers, data teams, and IT professionals, this course empowers participants to drive real-time decision-making in their organizations.
