MapR Tutorials

"MapR Music Catalog" Tutorial: REST and GraphQL

The MapR Music Catalog application explain the key MapR-DB 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-DB Shell and Apache Drill
  6. Work with MapR-DB 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 MapR Music Catalog application is available in this GitHub Repository.

MapR 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.


"MapR Smart Home" IoT Tutorial

The MapR 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:

  • MapR Streams
  • Apache Spark
  • MapR-DB (JSON and OpenTSDB)

The following Tutorial will drive you throught 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 MapR Smart Home application is available in this GitHub Repository.


MapR for Predictive Maintenance

This project is intended to show how to build Predictive Maintenance applications on MapR. Predictive Maintenance applications place high demands on data streaming, time-series data storage, and machine learning. Therefore, this project focuses on data ingest with MapR Streams, time-series data storage with MapR-DB and OpenTSDB, and feature engineering with MapR-DB and Apache Spark.

The source code of the MapR 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. Check out the Customer 360 Quick Start Solution to learn more about MapR's products and solutions for Customer 360 applications.

This application focuses on showing how the following three tenants to customer 360 applications can be achieved on MapR:

  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 MapR-DB
  • 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.


Deploying a Java application accessing M7 native tables on Apache Tomcat (Linux)

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Achieving replication in Hive if the metastore is in MySQL

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Basic Notes on Configuring Eclipse

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Batched Puts into HBase

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Building a Classification Model Using Spark

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Developing M7 applications with Maven

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Drilling CSV files - A simple example

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Getting Started with Drill

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Getting Started with HBase Shell

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Getting Started with Spark on MapR Sandbox

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How to Mount a MapR Cluster Using NFS

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Hue Tutorial Part 1: FileBrowser, Metastore Manager and Beeswax

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Hue Tutorial Part 2: Pig, Job Designer and Oozie

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Immediate MapReduce on Continuously Ingested Data

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Loading HBase tables into Spark

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MapR Control System Part 1: Dashboard and Setting Topology

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MapR Control System Part 2: Setting up Volumes, Snapshots and Mirrors

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MapR Control System Part 3: Alarms and Metrics

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Memory Management Basics

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Recommender System with Mahout and Elasticsearch

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Running SQL Queries on a JSON (YELP)

--- Dataset using Drill

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Scan Functionality on HBase

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Sorting Daily Files

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Stabilizing HBase Clusters

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Steps to Deploy a MapR Cluster: Part 1 of 2

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Steps to Deploy a MapR Cluster: Part 2 of 2

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Tackling Java Heap Space Errors

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Using Hive and Pig on Baseball Statistics

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