HPE Ezmeral Data Fabric
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.
Learn from the Experts
What is HPE Ezmeral Data Fabric?
How to size a data fabric system
Practical Erasure Coding in a Data Fabric
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:
- MapR Music Architecture
- Setup your environment
- Import the Data Set
- Discover MapR Database Shell and Apache Drill
- Work with MapR Database and Java
- Add Indexes
- Create a REST API
- Deploy to Wildfly
- Build the Web Application with Angular
- Work with JSON Arrays
- Change Data Capture
- Add Full Text Search to the Application
- 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.
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:
- Smart Home Architecture
- Setup your environment
- Data visualization with Grafana
- Run the application in a Docker Container
The source code of the Smart Home application is available in this GitHub Repository.
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 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:
- Big Data storage of structured and semi-structured data in files, tables, and streams
- SQL-based data integration of disparate datasets
- Predictive analytics through machine learning insights
The source code of the Customer 360 View application is available in this GitHub Repository.
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.
Free On-Demand Training
Learn for free with online courses that teach you how to build applications and administer the HPE Ezmeral Data Fabric. Visit HPE Ezmeral Learn On-Demand to enroll.
- Artificial Intelligence and Machine Learning. Newer course series covering the basics of data science, machine learning, and AI, with step-by-step instructions on managing successful machine learning projects.
- Apache Spark. This course series offers an overview of Apache Spark 2.x, the Spark execution model, and some advanced topics on developing data pipeline apps using Spark streaming, Spark SQL, GraphFrame, and MLlib.
- Data Fabric Cluster Administration. Learn about preparing and testing a bare metal cluster to installing a data fabric, to running it on a day to day basis.
- Kubernetes and Stateful Applications. Covers the basics of containers and Kubernetes, and methods for building stateful applications to run in a containerized world using a data fabric.
Take advantage of our free, Jupyter-Notebook based Workshops-on-Demand available in the Hack Shack. These technical workshops provide you with an in-depth, hands-on learning experience where you can interact with and learn from the experts. Designed to fit your schedule, these workshops are available 24/7 – any time, from anywhere. HPE Ezmeral Data Fabric workshops are available today.
Any questions on Ezmeral Data Fabric?
Not a Slack user? You can also ask your questions in our Ezmeral Forum.