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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.
This is a Rare Course and it can be take up to 3 weeks to arrange the training.
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4-Hour Sessions
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♱ Excluding VAT/GST
Classroom Training price is on request
♱ Excluding VAT/GST
Classroom Training price is on request
To ensure a productive and successful learning experience in the HDP Apache Hive course, participants should have the following minimum prerequisites:
Note that while these are the minimum prerequisites, the course is designed to accommodate learners with various levels of expertise. Prior exposure to the mentioned areas will facilitate a smoother learning curve, but the course content will provide comprehensive coverage of the necessary topics.
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.