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Return to Data science, Big data, Azure Data, Python, Scala, R, Spark

See Open Data Initiative

• Alteryx - https://alteryx.com - Alteryx is based in Irvine, California. “Alteryx offers a unified machine-learning platform, Alteryx Analytics, which enables citizen data scientists to build data models in a single data workflow,” according to Gartner.
  • https://github.com/search?q=Alteryx
  • https://github.com/alteryx/promote-python
  • https://github.com/alteryx/microsoft-starter-kit

• H20.ai - https://h2o.ai - H2O.ai is based in Mountain View, California. “H2O.ai offers an open-source machine-learning platform,” Gartner says. “For this Magic Quadrant, we evaluated H2O Flow, its core component; H2O Steam; H2O Sparkling Water, for Spark integration; and H2O Deep Water, which provides deep-learning capabilities.
  • https://github.com/search?q=H20.ai

• KNIME - https://knime.com - KNIME is based in Zurich, Switzerland. “KNIME provides the fully open-source KNIME Analytics Platform, which is used by over 100,000 people worldwide,” according to Gartner. “KNIME offers commercial support and commercial extensions to boost collaboration, security and performance for enterprise deployments. In the past year, KNIME has introduced cloud versions of its platform for AWS and Microsoft Azure, paid more attention to data quality, expanded its deep-learning features, and converted some of its commercial capabilities to open source.
  • https://github.com/knime/knime-core
  • https://github.com/knime/knime-deeplearning
  • https://github.com/knime/knime-python

• RapidMiner - https://rapidminer.com - RapidMiner is based in Boston, Massachusetts. “RapidMiner’s platform includes RapidMiner Studio, RapidMiner Server and RapidMiner Radoop, “ Gartner says. RapidMiner Studio is the model development tool, available as both a free edition and a commercial edition; it is priced according to the number of logical processors and the amount of data used by a model.
  • https://github.com/search?q=RapidMiner

• SAS - https://sas.com - SAS is based in Cary, North Carolina. “SAS provides many software products for analytics and data science,” the Gartner analysts write. https://www.information-management.com/slideshow/16-top-platforms-for-data-science-and-machine-learning

• MathWorks - https://mathworks.com - MATLAB and Simulink - MathWorks is a privately held company headquartered in Natick, Massachusetts. “Mathwork’s two major products are MATLAB and Simulink, but only MATLAB met the inclusion criteria for this Magic Quadrant,” according to the Gartner analysts.
  • https://github.com/search?q=MathWorks
  • https://github.com/search?q=MATLAB
  • https://github.com/search?q=Simulink

• TIBCO Software - https://tibco.com - TIBCO Software is based in Palo Alto, California. “Building on its presence in the analytics and BI sector, TIBCO entered the data science and machine-learning market by acquiring the well-established Statistica platform from Quest Software in June 2017,” according to Gartner.
  • https://github.com/search?q=TIBCO
  • https://github.com/TIBCOSoftware/flogo

• Databricks - https://databricks.com - Databricks is based in San Francisco, California. “Databricks offers the Apache Spark-based Databricks Unified Analytics Platform in the cloud, Gartner analysts said. “In addition to Spark, it provides proprietary features for security, reliability, operationalization, performance and real-time enablement on Amazon Web Services (AWS). Databricks announced a Microsoft Azure Databricks platform for preview in November 2017.
  • https://github.com/search?q=Databricks
  • https://github.com/databricks/spark-sklearn Scikit-learn integration package for Apache Spark
  • https://github.com/databricks/spark-deep-learning
  • https://github.com/databricks/databricks-cli

• IBM - https://www.ibm.com - IBM is based in Armonk, New York. “IBM provides many analytic solutions,” according to Gartner analysts. “For this Magic Quadrant, we evaluated SPSS, including both SPSS Modeler and SPSS Statistics. Data Science Experience (DSX), a second data science and machine-learning offering, did not meet our criteria for evaluation on the Ability to Execute axis, but does contribute to IBM's Completeness of Vision. IBM is now a Visionary, having lost ground in terms of both Completeness of Vision and Ability to Execute, relative to other vendors.

• Microsoft - https://microsoft.com - Microsoft is based in Redmond, Washington. “Microsoft provides a number of software products for data science and machine learning,” according to the Gartner analysts. “In the cloud, it offers Azure Machine Learning (including Azure Machine Learning Studio), Azure Data Factory, Azure Stream Analytics, Azure HDInsight, Azure Data Lake and Power BI. For on-premises workloads, Microsoft offers SQL Server 2019 and SQL Server 2017 with SQL Server Machine Learning Services, which was released in September 2017 — after the cutoff date for consideration in this Magic Quadrant. Only Azure Machine Learning Studio fulfilled the inclusion criteria for this Magic Quadrant, although Microsoft's broader advanced analytics offerings did influence our assessment of its Completeness of Vision. Microsoft remains a Visionary. Its position in this regard is attributable to low scores for market responsiveness and product viability, as Azure Machine Learning Studio's cloud-only nature limits its usability for the many advanced analytic use cases that require an on-premises option.”

• Anaconda (Python distribution) - https://anaconda.com - They advertise as “The Most Popular Python Data Science Platform.” - Anaconda , formerly known as Continuum Analytics, is based in Austin, Texas. “Anaconda sells Anaconda Enterprise 5.0, an open-source development environment based on the interactive-notebook concept,” Gartner analysts explained. “It also provides a loosely coupled distribution environment, giving access to a wide range of open-source development environments and open-source libraries, mainly Python-based. Anaconda's strength lies in its ability to federate and provide a central access point for a very large number of Python developers who build machine-learning capabilities continuously. However, Anaconda has little or no control over those developers' efforts in terms of quality, dependability and sustainability. Anaconda nurtures a broad developer community through Anaconda Cloud. Anaconda's position as a Niche Player reflects its suitability for seasoned data scientists fluent in Python.”

• SAP - https://sap.com - SAP is based in Walldorf, Germany. “SAP has yet again rebranded its platform: SAP Business Objects Predictive Analytics is now simply SAP Predictive Analytics (PA),” Gartner writes. “This platform has a number of components, such as Data Manager for dataset preparation and feature engineering, Automated Modeler for citizen data scientists, Predictive Composer for more sophisticated machine learning, and Predictive Factory for operationalization. SAP Leonardo Machine Learning and other components of the SAP Leonardo ecosystem did not contribute to SAP's Ability to Execute position in this Magic Quadrant. Over the past year, SAP has made good progress in several respects, but still lags behind in others. It is a Niche Player due to low customer satisfaction scores, a lack of mind share, a fragmented toolchain, and significant technological weak spots (in relation to the cloud, deep learning, Python and notebooks, for example), relative to others.
  • See Open Data Initiative

• https://github.com/search?q=Data+science
• https://duckduckgo.com/?q=Data+Science+Platforms
• https://www.gartner.com/doc/3606026/magic-quadrant-data-science-platforms

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