Develop with HEAVY.AI
Accelerate Third-Party and Custom Analytics Tools
Just as the HEAVY.AI server-side technology enables a zero-latency experience in HeavyImmerse, it is also used by developers and ISVs to accelerate third-party applications and create custom analytics tools. HeavyDB works in conjunction with open source JavaScript libraries, Python, Apache Thrift, and JDBC drivers.The HeavyDB 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 analytics tools and data visualization applications to take advantage of HEAVY.AI's open source SQL engine, HeavyDB, via JavaScript or JDBC drivers. Developers creating data visualization applications with HeavyDB and HeavyRender can access open source data visualization libraries like Highcharts, D3, React, and MapBox.
HEAVY.AI maintains a GitHub library, mapd-core, for the HeavyDB 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. HEAVY.AI 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 HEAVY.AI returns an image
Accelerate BI and GIS Tools
HeavyDB is able to accelerate a variety of data visualization, BI and GIS tools by executing queries orders of magnitude faster than legacy systems. HeavyRender can also be used to serve large-scale geo visualizations to third-party tools, enhancing their "at-scale" geospatial capabilities.
Integrate Machine Learning Workflows
HeavyDB 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 HEAVY.AI to push query results directly from HEAVY.AI 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.