- SANDVOX TUTORIAL HOW TO
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- SANDVOX TUTORIAL CODE
SANDVOX TUTORIAL CODE
Example code for persisting the streaming data to MapR Database. Example Spark code for querying the indexed streams at interactive speeds, enabling Spark SQL queries. A multi-threaded Consumer microservice that indexes the trades by receiver and sender. 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). The data source is the Daily Trades dataset described here. A Producer microservice that streams trades using the NYSE TAQ format. The application consists of the following components: This project provides an engine for processing real time streams trading data from stock exchanges. The source code of the Customer 360 View application is available in this GitHub Repository.Īpplication for Processing Stock Market Trade Data Predictive analytics through machine learning insights. SQL-based data integration of disparate datasets. Big Data storage of structured and semi-structured data in files, tables, and streams. This application focuses on showing how the following three tenants to customer 360 applications can be achieved on MapR: Check out the Customer 360 Quick Start Solution to learn more about MapR's products and solutions for Customer 360 applications. MapR enables applications to glean customer intelligence through machine learning that relates to customer personality, sentiment, propensity to buy, and likelihood to churn. Look at the project Readme to get more informations about this sample application.Ĭustomer 360 applications require the ability to access data lakes containing structured and unstructured data, integrate data sets, and run operational and analytical workloads simultaneously. The source code of the MapR Predictive Maintenance application is available in this GitHub Repository. Therefore, this project focuses on data ingest with MapR Event Store, time-series data storage with MapR Database and OpenTSDB, and feature engineering with MapR Database and Apache Spark. Predictive Maintenance applications place high demands on data streaming, time-series data storage, and machine learning. SANDVOX TUTORIAL HOW TO
This project is intended to show how to build Predictive Maintenance applications on MapR. The source code of the MapR Smart Home application is available in this GitHub Repository.
Run the application in a Docker Container. The following Tutorial will drive you throught the steps to build the application: The system is built on top of MapR Converged Data Platform and you will be familiarized with: 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. 
You can find informations about this implementation in the project readme file. MapR Music Catalog application is also implemented with a GraphQL endpoint instead of REST, the application code is available in this GitHub Repository. The source code of the MapR Music Catalog application is available in this GitHub Repository.
SANDVOX TUTORIAL FULL
Add Full Text Search to the Application.Discover MapR Database Shell and Apache Drill.
Here are the steps to develop, build and run the application: The MapR Music Catalog application explain the key MapR Database features, and how to use them to build a complete Web application. "MapR Music Catalog" Tutorial: REST and GraphQL
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