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HIVE

HIVE

Duration
45 Hours

Course Description

        Apache Hive is a data warehouse system built on top of Apache Hadoop, providing a way to query and analyze large datasets using a SQL-like language called HiveQL. It allows users to project structure onto unstructured data and query it without needing in-depth knowledge of Java or MapReduce. Hive is designed for data warehousing tasks like data summarization, ad-hoc queries, and analysis of huge datasets. 

Course Outline For HIVE

1. Introduction to Hive and the Hadoop ecosystem

  • Understanding Big Data: Challenges and opportunities, comparing real-time and batch processing.
  • Hadoop Fundamentals: Overview of Hadoop, HDFS (Hadoop Distributed File System) for storing large datasets, and MapReduce for processing them.
  • Introduction to Apache Hive: What Hive is, why it's important for managing and querying large datasets, and its use cases, often compared to traditional databases.
  • Hive Architecture: Components like the Metastore, Driver, Execution Engine, and how they interact within the Hadoop environment.
  • Installing and Configuring Hive: Setting up the Hive environment, including prerequisites and installation steps. 

2. HiveQL and data manipulation

  • Hive Data Types: Understanding the different data types supported by Hive.
  • Data Definition Language (DDL): Creating and managing databases, tables (managed and external), partitions, and views.
  • Data Manipulation Language (DML): Loading, inserting, updating, and deleting data within Hive tables.
  • Hive Query Language (HiveQL): Writing SQL-like queries for filtering, sorting, grouping, and joining data.
  • Built-in Functions and Operators: Utilizing Hive's built-in functions and operators to perform various data transformations and analysis. 

3. Data management and optimization

  • Partitioning and Bucketing: Techniques for organizing data into manageable segments to improve query performance and data retrieval efficiency.
  • Hive File Formats: Working with different file formats such as TextFile, SequenceFile, RCFile, ORC, Parquet, and Avro.
  • SerDes (Serializer/Deserializer): Understanding how SerDes handle data formats and their impact on storage and retrieval.
  • Query Optimization: Strategies for optimizing Hive queries for improved performance, including using indexes and understanding explain plans.
  • Hive Scripting: Writing Hive scripts to automate tasks and streamline data processing workflows. 

4. Integration with the Hadoop ecosystem

  • Hive and MapReduce: Understanding how Hive queries are converted to MapReduce jobs for execution.
  • Hive and Spark: Integrating Hive with Apache Spark for faster and more efficient data processing.
  • Hive and HBase: Using HBase as a data source for real-time processing and interactive queries with Hive.
  • Other Integrations: Depending on the course, you might explore integration with tools like Apache Pig, Apache Sqoop, Apache Kafka, Apache Tez, and Apache Drill. 

5. Security and advanced topics (optional)

  • Security in Hive: Concepts like authentication, authorization, and data encryption within Hive.
  • Hive LLAP: Learning about Live Long and Process (LLAP) for faster query retrieval.
  • Real-time Use Cases and Projects: Applying Hive skills to solve real-world data problems and building projects like a data warehouse for e-commerce.
  • Troubleshooting: Addressing common issues in Hive, such as infrastructure problems or user-related errors. 
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