Big Data Engineer (Hadoop)

Hadoop Course

The Hadoop Course Basic to Advance provides learners with a strong foundation in big data processing using the Hadoop ecosystem. Participants learn Hadoop architecture, distributed computing concepts, cluster components, data storage, MapReduce fundamentals, installation, configuration, and resource management. The course emphasizes practical learning through real-world datasets, enabling learners to understand how large-scale data is processed efficiently across distributed environments and preparing them for enterprise-level big data engineering.

HDFS Course

The HDFS Course Basic to Advance focuses on mastering the Hadoop Distributed File System for reliable and scalable data storage. Learners explore HDFS architecture, NameNode and DataNode operations, block management, replication, fault tolerance, file permissions, storage optimization, balancing, snapshots, security, and performance tuning. Through hands-on exercises, the course develops practical skills in managing distributed storage systems for large-scale enterprise data environments.

Apache Hive Course

The Apache Hive Course Basic to Advance equips learners with advanced data warehousing and SQL-based analytics capabilities within the Hadoop ecosystem. Participants learn Hive architecture, HiveQL, partitions, bucketing, external tables, user-defined functions, query optimization, metadata management, and integration with Hadoop components. The course combines practical projects with real-world scenarios to build scalable analytical solutions for big data processing.

Apache Pig Course

The Apache Pig Course Basic to Advance teaches learners how to simplify large-scale data processing using Pig Latin scripting. Participants explore data loading, transformations, filtering, grouping, joins, user-defined functions, optimization techniques, debugging, and integration with Hadoop. Through practical business use cases, the course develops efficient data transformation and ETL skills for handling complex big data workloads.

YARN Course

The YARN Course Basic to Advance provides comprehensive training in Hadoop cluster resource management and job scheduling. Learners master YARN architecture, ResourceManager, NodeManager, application lifecycle, scheduling policies, queue management, monitoring, scalability, fault tolerance, and performance optimization. The course prepares participants to efficiently manage distributed computing resources within enterprise Hadoop environments.

Apache Spark Course

The Apache Spark Course Basic to Advance develops expertise in high-performance distributed data processing using Apache Spark. Participants learn advanced DataFrames, Spark SQL, RDD optimization, caching, partitioning, streaming, machine learning integration, performance tuning, and large-scale ETL development. Through project-based learning, the course enables learners to build fast, scalable, and fault-tolerant big data processing applications.

Sqoop Course

The Sqoop Course Basic to Advance equips learners with the skills to transfer data efficiently between relational databases and Hadoop ecosystems. Participants learn import and export operations, incremental loading, parallel processing, connectors, job automation, performance tuning, security, and integration with Hive and HDFS. The course focuses on developing reliable data migration workflows for enterprise data engineering applications.

Oozie Course

The Oozie Course Basic to Advance focuses on workflow scheduling and job orchestration within Hadoop environments. Learners explore workflow creation, coordinators, bundles, job dependencies, scheduling, error handling, notifications, monitoring, integration with Hive, Pig, Sqoop, and Spark, and workflow optimization. Through real-world projects, the course prepares participants to automate and manage complex big data pipelines efficiently across enterprise data platforms.