BigData SQL Hive

BigData SQL Hive is an in-depth course designed to provide you with a comprehensive understanding of Apache Hive, a powerful tool for querying and managing large datasets stored in Hadoop. Whether you're a data engineer, analyst, or developer, this course will equip you with the skills to leverage Hive's full potential in a big data context.

Course Overview:

1. What is Hive / Architecture

• Begin with an introduction to Apache Hive, understanding its role in the Hadoop ecosystem as a data warehouse infrastructure. Learn about Hive’s architecture, including its interaction with HDFS, the metastore, and execution engines.

2. Hive Authorization Options

• Explore the different authorization options available in Hive, including SQL Standard Authorization and Ranger, and understand how to manage data access and security within a Hive environment.

3. Transactions

• Dive into Hive’s transaction management capabilities, including ACID (Atomicity, Consistency, Isolation, Durability) properties. Learn how to enable transactions, manage transactional tables, and execute insert, update, and delete operations.

4. Data Types

• Gain a thorough understanding of the data types supported by Hive, including primitive types (like integers, strings, and dates) and complex types (like arrays, maps, and structs). This module will help you structure your data effectively for efficient querying.

5. “Select” Queries

• Master the use of “SELECT” queries in Hive. Learn how to retrieve data using various SQL techniques, including filtering, sorting, and grouping data, as well as how to join tables effectively to obtain the results you need.

6. DML / Export / Import

• Understand the Data Manipulation Language (DML) commands in Hive for inserting, updating, and deleting data. Learn how to export and import data between Hive and external systems, ensuring smooth data flow across different platforms.

7. Hive UDFs Types

• Explore the different types of User-Defined Functions (UDFs) in Hive, including simple, aggregate, and table functions. Learn how to create and use UDFs to extend Hive’s capabilities and perform custom computations on your data.

8. Indexes

• Delve into the indexing options in Hive, including how to create, manage, and use indexes to improve query performance. Understand when and how to apply indexes to optimize data retrieval.

9. Windowing and Analytics Functions

• Learn how to use windowing and analytical functions in Hive to perform complex calculations, such as running totals, moving averages, and ranking, directly within your queries.

10. Performance Tuning

• Optimize your Hive queries and data management with performance-tuning techniques. This includes understanding how to configure Hive for optimal performance, manage resources, and troubleshoot common issues.

By the end of this course, participants will:

• Have a deep understanding of Hive’s architecture and its role in the Hadoop ecosystem.

• Be able to manage Hive transactions and data securely, using advanced authorization and ACID properties.

• Master complex SQL queries in Hive, including the use of UDFs, indexes, and analytical functions.

• Optimize Hive performance for large-scale data processing, ensuring efficient data retrieval and management.

This course combines theory and practical exercises, ensuring a balanced approach that allows you to apply the concepts learned in real-world scenarios. You will gain hands-on experience with Hive’s features, preparing you to tackle big data challenges confidently.

Upon completion of the "BigData SQL Hive" course, trainees will be able to:

• Effectively manage and query large datasets using Apache Hive.
• Implement and optimize complex SQL queries, including the use of UDFs and analytical functions.
• Securely manage data in Hive using advanced authorization options and transactions.
• Tune Hive’s performance to handle large-scale data efficiently.

• What is Hive / Architecture
• Hive Authorization Options
• Transactions
• Data Types
• “Select” Queries
• DML / Export / Import
• Hive UDFs types
• Indexes
• Windowing and Analytics Functions
• Performance tuning