The Comprehensive Hive course is a 3-day hands-on program designed to help learners master Apache Hive for large-scale data warehousing and analytics. Participants will explore HiveQL, data modeling, partitions, bucketing, indexing, and optimization techniques for handling massive datasets efficiently. The training emphasizes practical exercises in querying, managing, and optimizing Hive tables, while integrating with Hadoop ecosystems for scalable data processing. By the end of the course, learners will have the skills to design, optimize, and maintain data warehouses for real-world business analytics.
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.
