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From Beginner to Pro: Master HQL, ETL, Optimization, Partitioning & Big Data Analysis on Hadoop.
What you’ll learn
- Master Apache Hive architecture and how it queries data stored in HDFS and S3.
- Write advanced HQL (Hive Query Language) queries for data analysis and ETL processing.
- Optimize query performance using Partitioning
- Bucketing
- and Map-Side Joins.
- Build and manage scalable data warehouses to support Big Data analytics projects.
Requirements
- Basic knowledge of SQL (Structured Query Language) is recommended.
Description
Unlock the power of Big Data Analytics using the skills you already have.
Welcome to the ultimate guide to Apache Hive, the industry-standard data warehouse software that facilitates reading, writing, and managing large datasets residing in distributed storage using SQL.
In today’s data-driven world, companies like Facebook, Netflix, and Amazon handle petabytes of data daily. They don’t rely on traditional databases for this; they use the Hadoop ecosystem. However, writing MapReduce jobs in Java is complex and time-consuming. Enter Apache Hive. Hive allows you to project structure onto this data and query it using a language you likely already know: HQL (Hive Query Language).
Why take this course? This course is designed to bridge the gap between a traditional SQL Developer and a modern Big Data Engineer. We strip away the complexity of the Hadoop ecosystem and focus on practical, real-world applications of Hive. You don’t need to be a Java expert to process Big Data—you just need to master Hive.
What will you learn? We will start from the ground up, covering the architecture and installation, before moving into advanced data manipulation and optimization techniques. Key topics include:
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Hive Architecture: deeply understand the Metastore, Driver, and Execution Engine.
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Data Definition (DDL): Master the differences between Internal vs. External tables and how to manage schema evolution.
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Advanced HQL: complex joins, windowing functions, and views.
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Performance Tuning: This is where you become a pro. We dive deep into Partitioning and Bucketing to make your queries lightning-fast.
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Storage Optimization: Learn to work with industry-standard file formats like ORC and Parquet and apply compression techniques like Snappy.
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UDFs (User Defined Functions): How to extend Hive capabilities when built-in functions aren’t enough.
Who is this for? If you are a Data Analyst, DBA, or Backend Developer looking to pivot into the high-paying field of Big Data Engineering, this course is your blueprint.
By the end of this course, you will be confident in building scalable data warehouses and executing complex ETL pipelines on massive datasets.
Enroll today and transform your career with Big Data skills!
