Try MapR with New Interactive Tutorials

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5 min read

What do you call the difference between knowledge and information? Experience!Experiential learning is a core part of how we humans learn new things. At MapR, we know this all too well and it's why we're launching new interactive tutorials that people can use to learn how to use MapR as their data platform for AI and analytics.

The MapR Data Platform plays an important role in the fast-moving landscape of technology for AI and analytics. As much as we espouse the capabilities of Apache Spark, Tensorflow, and other computational tools that run on MapR, at the end of the day we want to bring the conversation back to how MapR solves pain points relating to the practicalities of building data-intensive applications. For instance, my favorite way to introduce people to MapR is to walk through a basic command-line demo using the Unix ls command to show how MapR provides things that cannot be taken for granted in other data systems. Simply by navigating the MapR Filesystem I can demonstrate how things like POSIX compliance, a global namespace, and a unified approach to data storage for files, tables, and streams make the data platform easy to use so you can focus on building amazing new applications.

In the spirit of experiential learning, we've teamed up with Katacoda, an interactive learning platform for information technology, to create browser-based labs, courses, and playgrounds that help people learn how to build applications using MapR.

An Interactive Introduction to MapR

The first tutorial we're rolling out provides users full access to a live MapR instance pre-configured with sample datasets and analytical tools, such as Apache Spark and Drill. Click here to try the tutorial. It includes a step-by-step guide that walks through important concepts alongside an in-browser terminal and embedded web interfaces for Drill and MapR. Users who prefer a more unstructured learning style can use the environment in a self-guided fashion to experiment with the same administrative control they would have in their own MapR installations but safely confined to an isolated learning environment. In summary, this tutorial provides the fastest way to learn what MapR is all about.

This tutorial is organized into sections separately focusing on each of the core MapR Data Platform components:

POSIX compliance is what makes MapR XD the "killer app" in the minds of many data managers, so the tutorial begins by explaining what POSIX means and why it's such a competitive differentiator for applications built using MapR. It also explains how users can interact with files using standard APIs, such as Hadoop and NFS.

The MapR Database is a scalable, high performance, NoSQL database designed for data-intensive applications. The second section of the tutorial demonstrates how CRUD operations can be performed using the mapr dbshell utility and standard SQL queries with Apache Drill (see screenshot below).

Streams are the third area of focus for the tutorial. In this section, the tutorial illustrates how to create and interact with the MapR Event Store for Apache Kafka with basic commands using the standard Kafka API.

The final section of the tutorial focuses on demonstrating how analytical workloads, such as MapReduce and Spark jobs, can be invoked on a MapR cluster to process data with minimal data movement. Data storage and analytical workloads have traditionally required separate clusters, but with MapR they can operate on a single all-purpose cluster, thereby reducing cluster sprawl, improving performance, simplifying administration, and lowering hardware costs.

Conclusion

MapR is teaming up with Katacoda to create interactive tutorials that can be used without requiring downloads or configuration, so users can jump in and start learning right away! This extends our existing catalogue of free downloadable training environments, such as the MapR Sandbox for Hadoop, the MapR Sandbox for Apache Drill, and the MapR Docker Container for Developers.

Try the interactive tutorial yourself below


This blog post was published February 07, 2019.
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