Accelerate Third-Party and Custom Analytics Tools

Just as the MapD server-side technology enables a zero-latency experience in MapD Immerse, it is also used by developers and ISV's to accelerate third-party applications. MapD Core works in conjunction with open source JavaScript libraries, Python, Apache Thrift, and JDBC drivers.

The MapD Core open source SQL engine is available under the Apache 2.0 license along with other components such as the Python interface (pymapd), and JavaScript infrastructure (mapd-connector, mapd-charting).

Develop Custom Analytics Applications

Developers create custom web applications and data visualization applications to take advantage of MapD’s open source SQL engine, MapD Core, via JavaScript or JDBC drivers. Developers creating data visualization applications with MapD Core and MapD Render can access open source data visualization libraries like Highcharts, D3, React, and MapBox.

MapD maintains a GitHub library, mapd-core, for the MapD Core SQL engine. Developers can send SQL queries to mapd-core, by using our connector components like mapd-connector, and use the result with any data visualization tool. MapD utilizes the Vega backend rendering engine to generate geospatial images computed on the GPU from their large datasets. Developers simply pass the correct JSON string that conforms to the Vega specification, and MapD returns an image

Accelerate BI and GIS Tools

MapD Core is able to accelerate a variety of data visualization, BI and GIS tools by executing queries orders of magnitude faster than legacy systems. MapD Render can also be used to serve large-scale geo visualizations to third-party tools, enhancing their 'at-scale' geospatial capabilities.

Integrate Machine Learning Workflows

MapD Core integrates seamlessly with the broader data science and machine learning ecosystem. Python developers can leverage the native Python DBAPI client, JupyterLab integration, or Ibis driver, which provides the expressivity of Pandas but at massive scale. Machine learning practitioners can tap the native Apache Arrow support in MapD Core to push query results directly from MapD into their algorithms of choice, such as Tensorflow or H2O’s XGBoost, all without the data ever leaving the GPU.  This makes it easier and faster to do pre-processing, feature engineering, modeling, and comparison of predictions to outcomes.

Get the MapD Whitepaper

Learn more about how to create custom applications and accelerate existing GIS and BI tools, developing with open source MapD Core.