Course description
From 0 to 1: Hive for Processing Big Data
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 .
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Upcoming start dates
Who should attend?
Audience:
- Analysts who want to write complex analytical queries on large scale data
- Engineers who want to know more about managing Hive as their data warehousing solution
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.
Training content
Course Objective:
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
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