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Apache Hive for Data Engineers (Hands On)

person icon Bigdata Engineer


Apache Hive for Data Engineers (Hands On)

Learn everything about Apache Hive a modern, data warehouse.

updated on icon Updated on Feb, 2024

language icon Language - English

person icon Bigdata Engineer

category icon Databases,Apache

Lectures -92

Resources -1

Duration -6 hours



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Course Description

The Apache Hive data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command-line tool and JDBC driver are provided to connect users to Hive.

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Hive! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Apache Hive!

Built on top of Apache Hadoop, Hive provides the following features:

  • Tools to enable easy access to data via SQL, thus enabling data warehousing tasks such as extract/transform/load (ETL), reporting, and data analysis.

  • A mechanism to impose structure on a variety of data formats

  • Access to files stored either directly in Apache HDFS™ or in other data storage systems such as Apache HBase™

  • Query execution via Apache Tez™, Apache Spark™, or MapReduce

  • Procedural language with HPL-SQL

  • Sub-second query retrieval via Hive LLAP, Apache YARN and Apache Slider.

Hive provides standard SQL functionality, including many of the later SQL:2003, SQL:2011, and SQL:2016 features for analytics.
Hive's SQL can also be extended with user code via user defined functions (UDFs), user defined aggregates (UDAFs), and user defined table functions (UDTFs).

There is not a single "Hive format" in which data must be stored. Hive comes with built in connectors for comma and tab-separated values (CSV/TSV) text files, Apache Parquet™, Apache ORC™, and other formats. Users can extend Hive with connectors for other formats. Please see File Formats and Hive SerDe in the Developer Guide for details.

Hive is not designed for online transaction processing (OLTP) workloads. It is best used for traditional data warehousing tasks.

Hive is designed to maximize scalability (scale out with more machines added dynamically to the Hadoop cluster), performance, extensibility, fault-tolerance, and loose-coupling with its input formats.


What will you learn in this course:

  • Why Hive is necessary for Data Engineer
  • The goal of this course is to help you become familiar with Apache Hive bits and bytes
  • Learn A to Z of Apache HIVE (From Basic to Advance level).
  • Hands on Experience on Apache Hive and Real-time Use Case


What are the prerequisites for this course?

  • Basic Knowledge of Hadoop
  • Basic Knowledge of SQL and Database
  • Desktop or Laptop with Ubuntu Operating System and Minimum 8 GB RAM is recommended
Apache Hive for Data Engineers (Hands On)


Check out the detailed breakdown of what’s inside the course

8 Lectures
  • play icon Introduction to Course 04:47 04:47
  • play icon Introduction to Apache Hive 01:51 01:51
  • play icon Hive Architecture 05:05 05:05
  • play icon How a Hive query flows through the system. 03:16 03:16
  • play icon (Optional) Introduction to Big Data 03:42 03:42
  • play icon (Optional) What is Hadoop 07:13 07:13
  • play icon Hive Features 02:28 02:28
  • play icon Hive Limitation 00:58 00:58
Installing Apache Hive on Ubuntu (Linux) Machine
2 Lectures
Hive Data Model
4 Lectures
Hive Data Types
3 Lectures
HIVE Data Definition Language.
18 Lectures
HIVE Data Manipulation Language
5 Lectures
Hive View, Metastore, Partitions, and Bucketing
11 Lectures
Hive Built-In Functions
3 Lectures
Built-in Operators
4 Lectures
Hive Join
5 Lectures
Frequently Asked Interview Question and Answers
8 Lectures
Hands On Projects (2 Projects)
19 Lectures
Working with XML and JSON
2 Lectures

Instructor Details

Bigdata Engineer

Bigdata Engineer

I am Solution Architect with 12+ year’s of experience in Banking, Telecommunication and Financial Services industry across a diverse range of roles in Credit Card, Payments, Data Warehouse and Data Center programmes

My role as Bigdata and Cloud Architect to work as part of Bigdata team to provide Software Solution.

Responsibilities includes,

- Support all Hadoop related issues
- Benchmark existing systems, Analyse existing system challenges/bottlenecks and Propose right solutions to eliminate them based on various Big Data technologies
- Analyse and Define pros and cons of various technologies and platforms
- Define use cases, solutions and recommendations
- Define Big Data strategy
- Perform detailed analysis of business problems and technical environments
- Define pragmatic Big Data solution based on customer requirements analysis
- Define pragmatic Big Data Cluster recommendations
- Educate customers on various Big Data technologies to help them understand pros and cons of Big Data
- Data Governance
- Build Tools to improve developer productivity and implement standard practices

I am sure the knowledge in these courses can give you extra power to win in life.

All the best!!

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Dipak Panda

Sounds like reading the slides. Need to be more elaborative and use more diagrams.

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