Learn everything about Apache Hive a modern, data warehouse.
What you will learn
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
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.
We will learn
1) Apache Hive Overview
2) Apache Hive Architecture
3) Installation and Configuration
4) How a Hive query flows through the system.
5) Hive Features, Limitation and Data Model
6) Data Type, Data Definition Language, and Data Manipulation Language
7) Hive View, Partition, and Bucketing
8) Built-in Functions and Operators
9) Join in Apache Hive
10) Frequently Asked Interview Question and Answers
11) 2 Realtime Projects
My goal is to provide you with practical tools that will be beneficial for you in the future. While doing that, with a real use opportunity.
I am really excited you are here, I hope you are going to follow all the way to the end of the course. It is fairly straight forward fairly easy to follow through the course I will show you step by step each line of code & I will explain what it does and why we are doing it. So please I invite you to follow up on it to go through all the lectures. All right I will see you soon in the course.
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