You can store, manage and access your data from edge to core to cloud at any scale or speed that you need. You can build data structures that span your enterprise using the data fabric to handle data storage and motion. Your current systems can access data in the fabric, and the same bits can be processed by cloud native applications.

• HPE Ezmeral Data Fabric Documentation
• HPE Ezmeral Data Fabric API Documentation
• README

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Learn from the Experts

What is HPE Ezmeral Data Fabric?

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Practical Erasure Coding in a Data Fabric

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Tutorials

"Music Catalog" Tutorial: REST and GraphQL

The Music Catalog application explain the key Ezmeral Data Fabric Database features, and how to use them to build a complete Web application. Here are the steps to develop, build and run the application:

1. Introduction
2. MapR Music Architecture
3. Setup your environment
4. Import the Data Set
5. Discover MapR Database Shell and Apache Drill
6. Work with MapR Database and Java
7. Add Indexes
8. Create a REST API
9. Deploy to Wildfly
10. Build the Web Application with Angular
11. Work with JSON Arrays
12. Change Data Capture
13. Add Full Text Search to the Application
14. Build a Recommendation Engine

The source code of the Music Catalog application is available in this GitHub Repository. Music Catalog application is also implemented with a GraphQL endpoint instead of REST, the application code is available in this GitHub Repository. You can find informations about this implementation in the project readme file.

"Smart Home" IoT Tutorial

The Smart Home Tutorial is designated to walk the developer through a process of developing event processing system, starting from defining business requirements and ending with system deployment and testing. The system is built on top of MapR Converged Data Platform and you will be familiarized with:

• Ezmeral Data Fabric Event Store for Apache Kafka
• Apache Spark
• Ezmeral Data Fabric Database (JSON and OpenTSDB)

The following Tutorial will drive you through the steps to build the application:

1. Introduction
2. Motivation
3. Smart Home Architecture
4. Setup your environment
5. Deployment
6. Data visualization with Grafana
7. Run the application in a Docker Container

The source code of the Smart Home application is available in this GitHub Repository.

Ezmeral Data Fabric for Predictive Maintenance

This project is intended to show how to build Predictive Maintenance applications on Ezmeral Data Fabric. Predictive Maintenance applications place high demands on data streaming, time-series data storage, and machine learning. Therefore, this project focuses on data ingest with Ezmeral Data Fabric Event Store, time-series data storage with Ezmeral Data Fabric Database and OpenTSDB, and feature engineering with Ezmeral Data Fabric Database and Apache Spark. The source code of the Predictive Maintenance application is available in this GitHub Repository. Look at the project Readme to get more informations about this sample application.

Customer 360 View

Customer 360 applications require the ability to access data lakes containing structured and unstructured data, integrate data sets, and run operational and analytical workloads simultaneously. MapR enables applications to glean customer intelligence through machine learning that relates to customer personality, sentiment, propensity to buy, and likelihood to churn. This application focuses on showing how the following three tenants to customer 360 applications can be achieved on Ezmeral Data Fabric:

1. Big Data storage of structured and semi-structured data in files, tables, and streams
2. SQL-based data integration of disparate datasets
3. Predictive analytics through machine learning insights

The source code of the Customer 360 View application is available in this GitHub Repository.

Application for Processing Stock Market Trade Data

This project provides an engine for processing real time streams trading data from stock exchanges. The application consists of the following components:

• A Producer microservice that streams trades using the NYSE TAQ format
  • The data source is the Daily Trades dataset described here
  • The schema for our data is detailed in Table 6, "Daily Trades File Data Fields", on page 26 of Daily TAQ Client Specification (from December 1st, 2013)
• A multi-threaded Consumer microservice that indexes the trades by receiver and sender
• Example Spark code for querying the indexed streams at interactive speeds, enabling Spark SQL queries
• Example code for persisting the streaming data to Ezmeral Data Fabric Database
• Performance tests for benchmarking different configurations
• A supplementary python script to enhance the above TAQ dataset with "level 2" bid and ask data at a user-defined rate

The source code of the Application for Processing Stock Market Trade Data application is available in this GitHub Repository.

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Free On-Demand Training

Educate yourself for free with online courses that teach you how to build applications and administer the HPE Ezmeral Data Fabric. These lecture and lab courses are part of HPE Ezmeral Learn On-Demand academy.

• Developer Series

The developer series of courses includes content on basic and advanced programming with Apache Spark as well as information about how to develop applications using some of the unique capabilities of the HPE Ezmeral Data Fabric such as the integrated JSON-oriented document database.

• Admin Series

This admin series covers a range of topics from preparing and testing a bare metal cluster to installing a data fabric to running it on a day to day basis. Hands-on labs help you make sure you have the necessary skills wired by the time you need to install a production system.

• Containers and Kubernetes Series

This new and expanding series covers the basics of containers and Kubernetes through to up-to-date methods for building stateful applications to run in a containerized world using a data fabric.

Any questions on Ezmeral Data Fabric?

Join the HPEDEV Slack Workspace and start a discussion in our #ezmeral-data-fabric channel.

Not a Slack user? You can also ask your questions in our Ezmeral Forum.

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