Description
Note: Enrolling here will not give you access to the actual course. The course is available by purchasing the Full OnDemand Library subscription.
About This Course
Cloudera University’s 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.
Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Cloudera environments. Apache Impala enables real-time interactive analysis of the data stored in Hadoop using a native SQL environment. Together, they make multi-structured data accessible to analysts, database administrators, and others without Java programming expertise.
Course Length
This course includes 7 hours of video content, plus 2 hours of exercise review. Hands-on exercises will take approximately 10.5 hours.
Audience and Prerequisites
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.
Note: Enrolling here will not give you access to the actual course. The course is available by purchasing the Full OnDemand Library subscription.
Objectives
Through videos and 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
- How to use Apache Hive and Apache Impala to provide SQL access to data
- Hive and Impala syntax and data formats, including functions and subqueries
- How to create, modify, and delete tables, views, and databases; load data; and store results of queries
- How to create and use partitions and different file formats
- How to combine two or more datasets using JOIN or UNION, as appropriate
- What analytic and windowing functions are, and how to use them
- How to store and query complex or nested data structures
- How to process and analyze semi-structured and unstructured data
- Techniques for optimizing Hive and Impala queries
- How to 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
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