Course Overview
Cloudera Educational Services' four-day Data Analyst Training course will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.
Who should attend
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators.
Prerequisites
Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.
Course Objectives
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the ecosystem, learning:
- How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
- Using Apache Hive and Apache Impala to provide SQL access to data
- Hive and Impala syntax and data formats, including functions and subqueries
- Create, modify, and delete tables, views, and databases; load data; and store results of queries
- Create and use partitions and different file formats
- Combining two or more datasets using JOIN or UNION, as appropriate
- What analytic and windowing functions are, and how to use them
- Store and query complex or nested data structures
- Process and analyze semi-structured and unstructured data
- Techniques for optimizing Hive and Impala queries
- Extending the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
- How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task
Course Content
- Introduction
- Apache Hadoop Fundamentals
- Introduction to Apache Hive and Impala
- Querying with Apache Hive and Impala
- Common Operators and Built-In Functions
- Data Management
- Data Storage and Performance
- Working with Multiple Datasets
- Analytic Functions and Windowing
- Complex Data
- Analyzing Text
- Apache Hive Optimization
- Apache Impala Optimization
- Extending Apache Hive and Impala
- Choosing the Best Tool for the Job
- Conclusion