Top 17 Data Science and Machine Learning vendors shortlisted by Gartner

Machine Learning   |   
Published February 4, 2019   |   

Gartner has published a list of top 17 data science and machine learning vendors in its 2019 Magic Quadrant for Data Science and Machine Learning Platforms.

Previously titled the Magic Quadrant for Data Science Platforms and the Magic Quadrant for Advanced Analytics Platforms, the report evaluated the vendors for their completeness of vision and ability to execute.

Here are the top 17 Data Science and Machine Learning vendors shortlisted by Gartner.

1. Alteryx

Alteryx is based in Irvine, California, U.S. It provides four software products, which comprise its data science platform. The Alteryx Analytics platform includes Alteryx Connect, Alteryx Designer, Alteryx Server and Alteryx Promote.

2. Anaconda

Anaconda is based in Austin, Texas, U.S. It offers Anaconda Enterprise 5.2, a data science development environment based on the interactive notebook concept (this analysis excludes the Conda Distribution Packages) that sees users exploiting open-source Python and R-based packages.

3. Databricks

Databricks is based in San Francisco, U.S. Its Apache Spark-based Unified Analytics Platform combines data engineering and data science capabilities that use a variety of open-source languages. In addition to Spark, the platform provides proprietary features for security, reliability, operationalization, performance and real-time enablement on Amazon Web Services (AWS).

4. Dataiku

Dataiku is headquartered in New York City, U.S., and has a main office in Paris, France. It offers Data Science Studio (DSS) with a focus on cross-discipline collaboration and ease of use.

5. DataRobot

DataRobot is based in Boston, Massachusetts, U.S. It provides an augmented data science and ML platform. The platform automates key tasks, enabling data scientists to work efficiently and citizen data scientists to build models easily.

6. Datawatch (Angoss)

Datawatch is based in Bedford, Massachusetts, U.S. In January 2018, it acquired Angoss and its main data science product components. These include KnowledgeSEEKER, the most basic offering, aimed at citizen data scientists in a desktop context; KnowledgeSTUDIO, which includes many more models and capabilities than KnowledgeSEEKER; and KnowledgeENTERPRISE, a flagship product that includes the full range of capabilities.

7. Domino

Domino (Domino Data Lab) is headquartered in San Francisco, California, U.S. The Domino Data Science Platform represents a comprehensive end-to-end solution designed for expert data scientists. The platform incorporates both open-source and proprietary tool ecosystems, while providing capabilities for collaboration, reproducibility, and centralization of model development and deployment.

8. Google

Google, a subsidiary of Alphabet, is based in Mountain View, California, U.S. Its core ML platform offerings include Cloud ML Engine, Cloud AutoML, the open-source TensorFlow, and the recently announced BigQuery ML. Its ML components require other Google components for end-to-end capabilities, such as Google Cloud Dataprep, Google Datalab, Google BigQuery, Google Cloud Dataflow, Google Cloud Dataproc, Google Data Studio, Kubeflow and Google Kubernetes Engine.

9. is based in Mountain View, California, U.S. and offers the free open-source H2O Open-Source Machine Learning (H2O, Sparkling Water and H2O4GPU) and a commercial product called H2O Driverless AI. H2O’s core strength is its high-performing ML components, which are tightly integrated within several competing platforms evaluated in this Magic Quadrant.

10. IBM

IBM is based in Armonk, New York, U.S. For this Magic Quadrant we evaluated two platforms: SPSS (including SPSS Modeler and SPSS Statistics) and Watson Studio, an offering that incorporates and builds on IBM’s previous Data Science Experience (DSX) product.


KNIME is based in Zurich, Switzerland. It provides the KNIME Analytics Platform on a fully open-source basis for free, while a commercial extension, KNIME Server, offers more advanced functions, such as team, automation and deployment capabilities.


KNIME is based in Zurich, Switzerland. It provides the KNIME Analytics Platform on a fully open-source basis for free, while a commercial extension, KNIME Server, offers more advanced functions, such as team, automation and deployment capabilities.

13. Microsoft

Microsoft is based in Redmond, Washington, U.S. It provides a number of software products for data science and ML. In the cloud, it offers Azure Machine Learning (including Azure Machine Learning Studio), Azure Data Factory, Azure HDInsight, Azure Databricks and Power BI. For on-premises workloads, Microsoft offers Machine Learning Server.

14. RapidMiner

RapidMiner is based in Boston, Massachusetts, U.S. Its platform includes RapidMiner Studio, RapidMiner Server, RapidMiner Cloud, RapidMiner Real-Time Scoring and RapidMiner Radoop.

15. SAP

SAP is based in Walldorf, Germany. It offers SAP Predictive Analytics (PA). This platform has a number of components, including Data Manager for dataset preparation and feature engineering, Automated Modeler for citizen data scientists, Expert Analytics for more advanced ML, and Predictive Factory for operationalization. SAP PA is tightly integrated with SAP HANA.

16. SAS

SAS is based in Cary, North Carolina, U.S. It provides many software products for analytics and data science. For this Magic Quadrant, we evaluated SAS Enterprise Miner (EM) and SAS Visual Data Mining and Machine Learning (VDMML).

17. TIBCO Software

TIBCO Software is based in Palo Alto, California, U.S. Through the acquisition of enterprise reporting and modern BI platform vendors (Jaspersoft and Spotfire), descriptive and predictive analytics platform vendors (Statistica and Alpine Data), and a streaming analytics vendor (StreamBase Systems), TIBCO has built a well-rounded and powerful analytics platform.