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From 0 to 1: Hive for Processing Big Data

From 0 to 1: Hive for Processing Big Data

From 0 to 1: Hive for Processing Big Data
From 0 to 1: Hive for Processing Big Data, End-to-End Hive : HQL, Partitioning, Bucketing, UDFs, Windowing, Optimization, Map Joins, Indexes
BESTSELLER
Created by Loony Corn
English
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What you'll learn
  • Write complex analytical queries on data in Hive and uncover insights
  • Leverage ideas of partitioning, bucketing to optimize queries in Hive
  • Customize hive with user defined functions in Java and Python
  • Understand what goes on under the hood of Hive with HDFS and MapReduce



Description
Prerequisites: Hive requires knowledge of SQL. The course includes and SQL primer at the end. Please do that first if you don't know SQL. You'll need to know Java if you want to follow the sections on custom functions. 

Taught by a 4 person team including 2 Stanford-educated, ex-Googlers  and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with large-scale data. 

 Hive is like a new friend with an old face (SQL). This course is an end-to-end, practical guide to using Hive for Big Data processing. 

Let's parse that 

A new friend with an old face: Hive helps you leverage the power of Distributed computing and Hadoop for Analytical processing. It's interface is like an old friend : the very SQL like HiveQL. This course will fill in all the gaps between SQL and what you need to use Hive. 

End-to-End: The course is an end-to-end guide for using Hive:  whether you are analyst who wants to process data  or an Engineer who needs to build custom functionality or optimize performance - everything you'll need is right here. New to SQL? No need to look elsewhere. The course  has a primer on all the basic SQL constructs, . 

Practical: Everything is taught using real-life examples, working queries and code . 

What's Covered: 

Analytical Processing: Joins, Subqueries, Views, Table Generating Functions, Explode, Lateral View, Windowing and more

Tuning Hive for better functionality: Partitioning, Bucketing, Join Optimizations, Map Side Joins, Indexes, Writing custom User Defined functions in Java. UDF, UDAF, GenericUDF, GenericUDTF,  Custom functions in Python,  Implementation of MapReduce for Select, Group by and Join

For SQL Newbies: SQL In Great Depth


Learn A to Z of Apache Airflow from Basic to ADVANCE level. Build and deploy workflows & data pipelines using Airflow

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Hands-on examples of processing massive streams of data - in real time, on a cluster - with Apache Spark Streaming

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