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Apache Hive for Data Engineers (Hands On)
Learn everything about Apache Hive a modern, data warehouse.
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.
Goals
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
Prerequisites
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
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
8 Lectures
- Introduction to Course 04:47 04:47
- Introduction to Apache Hive 01:51 01:51
- Hive Architecture 05:05 05:05
- How a Hive query flows through the system. 03:16 03:16
- (Optional) Introduction to Big Data 03:42 03:42
- (Optional) What is Hadoop 07:13 07:13
- Hive Features 02:28 02:28
- 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
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