This certification demonstrates proficiency using Hive, Pig, and Apache Drill to perform data analytics.
The Essentials series of courses is intended for anyone interested in getting started with big data, Apache Hadoop, or MapR.
This course is designed to introduce students to the basics of the MapR Converged Data Platform. At the end of the course, students will have some of the fundamental knowledge necessary for other MapR Academy courses.
This course is designed to introduce students to the basics of Apache Hadoop. The course begins with a brief introduction to the Hadoop Distributed File System and MapReduce, then covers several open source ecosystem tools such as Apache Spark, Apache Drill, and Apache Flume. Finally, these tools are applied to real-world use cases.
This course introduces students to the basics of big data. Students will learn about big data concepts and how different tools and roles can help solve real-world big data problems.
Targeted towards data analysts, data architects, and application developers, the goal of this course is to learn more about architecting your Apache HBase applications for performance and security. This course covers how to bulk load data into HBase, performance considerations and tips for designing your HBase application, benchmarking and monitoring your HBase application, and MapR-DB security. Concepts are conveyed through lectures, hands-on labs, and scenario analyses.
Apache Pig Essentials is an introductory-level course designed for data analysts and developers. The course begins with a review of data pipeline tools, then covers how to load and manipulate relations in Pig.
DA 440 is an introductory-level course designed for data analysts and developers. You will learn how Apache Hive fits in the Hadoop ecosystem, how to create and load tables in Hive, and how to query data using the Hive Query Language.
Targeted towards data architects and application developers who have experience with Java, the goal of this course is to learn how to write HBase programs using Hadoop as a distributed NoSQL datastore.
Targeted towards data analysts, data architects and application developers, the goal of this course is to enable you to design HBase schemas based on design guidelines. You will learn about the various elements of schema design and how to design for data access patterns. The course offers an in-depth look at designing row keys, avoiding hot-spotting and designing column families. It discusses how to transition from a relational model to an HBase model. You will learn the differences between tall tables and wide tables. Concepts are conveyed through lectures, hands-on labs and analysis of scenarios.