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We're here to help you find itHDP Apache Hive Course Overview
The HDP Apache Hive course is a comprehensive program designed to equip learners with in-depth knowledge of Apache Hive, a data warehouse software project that facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Through an array of modules, the course offers a blend of theoretical understanding and practical skills, from the basics of information architecture in Module 1 to advanced performance tuning in Module 8.
Learners will explore the Apache Hive Architecture, understand various file formats, and delve into advanced programming techniques. The course also covers integration with other big data components like Apache HBase, Phoenix, Druid, Sqoop, and Spark, ensuring a holistic grasp of the data ecosystem. Modules on security with Apache Ranger and Atlas, as well as performance enhancement with LLAP, are also included.
By the end of the course, participants will be well-versed in Apache Hive, capable of optimizing enterprise data warehouses, and will have gained practical exposure to Hive's integration with various tools, enhancing their big data skill set and opening up opportunities in data engineering and analytics.
Purchase This Course
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
The HDP Apache Hive course is tailored for IT professionals aiming to master data warehousing and query optimization using Hive.
Gain expertise in Apache Hive with a comprehensive course covering optimization, architecture, programming, performance tuning, security, data governance, integration with Hadoop ecosystem components, and real-time processing with LLAP.