The Forrester Wave™ Cloud Hadoop/Spark Platforms Q1 2019

Modern enterprises are insights driven. They have to serve customers, scale, adapt, and compete — all with great efficiency. The veracity, timeliness, and scope of their insights will determine enterprise winners and losers. The perennial challenge faced by business insights pros is to turn the data-to-insights wheel faster. That “wheel” includes data acquisition, data security, data transformation, data governance, data exploration, analytics, and machine learning that is massively scalable, performant, and fault tolerant.

Hadoop is more than 10 years old. It has gone from a batch-oriented storage, processing, and highly programmatic data platform to become a cloud-enabled, highly interconnected analytical platform.

Forrester defines enterprise Cloud Hadoop/Spark (HARK) platforms as:

Distributed computing software and services that are rooted in open source Apache Hadoop and Apache Spark to ingest, store, process, manage, and analyze data to generate insights that drive business outcomes.

AWS (EMR, RDS..), Hortonworks, Cloudera and Microsoft (Azure HDInsight) are the leaders in “The Forrester Wave™ Cloud Hadoop/Spark Platforms Q1 2019”, Google (DataProc, Cloud Platform services such as BigQuery and BigTable) is almost there.

Gartner magic quadrant for analytics and business intelligence platforms 2019

"By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence, data science and machine learning platforms, and embedded analytics.Also 50% of analytical queries either will be generated via search, natural language processing or voice, or will be automatically generated."

Modern analytics and business intelligence (BI) platforms are characterized by easy-to-use tools that support the full analytic workflow — from data preparation and ingestion to visual exploration and insight generation. Most modern analytics and BI platforms also have their own self-contained in-memory columnar engine to ensure fast performance and support rapid prototyping, but many can optionally use existing modeled data sources.

Microsoft, Tableau, Qlik and ThoughtSpot are the leaders in "Gartner magic quadrant for analytics and business intelligence platforms 2019". Check out for SAP, IBM, SAS, MSTR, Oracle etc.

Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms

“Expert data scientists prefer to code data science models in Python or R, or to build and run data models in notebooks. Other users are most comfortable building models by using a point-and-click UI to create visual pipelines. Hype about AI is at its peak, but AI must be distinguished from data science and ML. Of course, data science is a core discipline for the development of AI, and ML is a core enabler of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions.”

Gartner define a data science platform as:

A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.

Knime, RapidMiner, Tibco Software (acquired… Statistica and Alpine Data) and SAS are the leaders in "Magic Quadrant for Data Science and Machine Learning Platforms 2019", check out Alteryx and DataIku.. Open Source based Anaconda, Conda libraries..

Gartner’s 2019 Magic Quadrant for Data Management Solutions for Analytics

Disruption slows as cloud and non-relational technology take their place beside traditional approaches, the leaders extend their lead, and distributed data approaches solidify their place as a best practice for DMSA (Data Management Solutions for Analytics).

Gartner defines a data management solution for analytics (DMSA) as a complete software system that supports and manages data in one or many file management systems, most commonly a database or multiple databases. These management systems include specific optimization strategies designed for supporting analytical processing — including, but not limited to, relational processing, non-relational processing (such as graph processing), and machine learning or programming languages such as Python or R. Data is not necessarily stored in a relational structure, and can use multiple data models — relational, XML, JavaScript Object Notation (JSON), key-value, graph, geospatial and others.

Oracle, Microsoft-Azure, Amazon-AWS, SAP, Teradata, Snowflake, Google-GCP and IBM (blue suite) are the leaders in “Magic Quadrant for Data Management Solutions for Analytics”. Check out for the Niche Player category..

The Forrester Wave™: Enterprise Insight Platforms, Q1 2019

Enterprise insight platforms help CIOs address the continual business demand to go faster in the face of technology complexity.
Forrester defines an enterprise insight platform as: An integrated set of data management, analytics, and development tools that provide a general, enterprise-class platform for building systems of insight.

Forrester's research uncovered a market in which IBM, Microsoft, and SAS are Leaders; Google, SAP, TIBCO Software, and GoodData are Strong Performers; Reltio is a Contender; and EdgeVerve is a Challenger in "The Forrester Wave™: Enterprise Insight Platforms, Q1 2019"

The Forrester Wave™ Strategic iPaaS And Hybrid Integration Platforms, Q1 2019

Forrester’s research uncovered a market in which TIBCO Software, Workato, Dell Boomi, Software AG, MuleSoft, and SnapLogic are Leaders; Liaison Technologies, Jitterbit, Talend, IBM, Adeptia, Pantheon, and Axway are Strong Performers; Red Hat is a Contender; and Microsoft is a Challenger. "The Forrester Wave™ Strategic iPaaS And Hybrid Integration Platforms, Q1 2019"

Gartner Magic Quadrant for Master Data Management Solutions 2018

Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of an enterprise’s official, shared master data assets.

MDM solutions are enterprise software products that:

o   Support the global identification, linking and synchronization of master data across heterogeneous data sources through semantic reconciliation of master data.

o   Create and manage a central, persisted system of record or index of record for master data.

o   Support the four MDM hub implementation styles, as defined by Gartner (see the Completeness of Vision section, “Offering [Product] Strategy”).

o   Enable generation and delivery of a trusted version of one or more subject areas (i.e., data domains) to all stakeholders, in support of various business initiatives.

o   Support ongoing master data stewardship and governance requirements through workflow-based monitoring and corrective-action techniques.

o   Are agnostic to the business application landscape in which they reside; that is, they do not assume or depend on the presence of any particular business application(s) to function (aka “application-neutral”).

Informatica & Orchestra Networks lead the pack in "Gartner Magic Quadrant for Master Data Management Solutions 2018" with SAP, IBM, Stibo, Semarchy, Profisee are challenger.