The Death of Big Data and the Emergence of the Multi-Cloud Era

RIP Era of Big Data
April 1, 2006 – June 5, 2019

The Era of Big Data passed away on June 5, 2019 with the announcement of Tom Reilly’s upcoming resignation from Cloudera and subsequent market capitalization drop. Coupled with MapR’s recent announcement intending to shut down in late June, which will be dependent on whether MapR can find a buyer to continue operations, June of 2019 accentuated that the initial Era of Hadoop-driven Big Data has come to an end. Big Data will be remembered for its role in enabling the beginning of social media dominance, its role in fundamentally changing the mindset of enterprises in working with multiple orders of magnitude increases in data volume, and in clarifying the value of analytic data, data quality, and data governance for the ongoing valuation of data as an enterprise asset.

As I give a eulogy of sorts to the Era of Big Data, I do want to emphasize that Big Data technologies are not actually “dead,” but that the initial generation of Hadoop-based Big Data has reached a point of maturity where its role in enterprise data is established. Big Data is no longer part of the breathless hype cycle of infinite growth, but is now an established technology.
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Data Science and Machine Learning News Roundup, January 2019

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

Cloudera and Hortonworks Complete Planned Merger

In early January, Cloudera and Hortonworks completed their planned merger. With this, Cloudera becomes the default machine learning ecosystem for Hadoop-based data, while providing an easy pathway for expanding into  machine learning and analytics capabilities for Hortonworks customers.

Study: 89 Percent of Finance Teams Yet to Embrace Artificial Intelligence

A study conducted by the Association of International Certified Professional Accountants (AICPA) and Oracle revealed that 89% of organizations have not deployed AI to their finance groups. Although a correlation exists between companies with revenue growth and companies that are using AI, the key takeaway is that artificial intelligence is still in the early adopter phase for most organizations.

Gartner Magic Quadrant for Data Science and Machine Learning Platforms

In late January, Gartner released its Magic Quadrant for Data Science and Machine Learning Platforms. New to the Data Science and Machine Learning MQ this year are both DataRobot and Google – two machine learning offerings with completely different audiences and scope. DataRobot offers an automated machine learning service targeted towards “citizen data scientists,” while Google’s machine learning tools, though part of Google Cloud Platform, are more of a DIY data pipeline targeted towards developers. By contrast, I find it curious that Amazon’s SageMaker machine learning platform – and its own collection of task-specific machine learning tools, despite their similarity to Google’s – failed to make the quadrant, given this quadrant’s large umbrella.

While data science and machine learning are still emerging markets, the contrasting demands of these technologies made by citizen data scientists and by cutting-edge developers warrants splitting the next Data Science and Machine Learning Magic Quadrant into separate reports targeted to the considerations of each of these audiences. In particular, the continued growth of automated machine learning technologies will likely drive such a split, as citizen data scientists pursue a “good enough” solution that provides quick results.

Data Science and Machine Learning News, October 2018

On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, DominoElastic, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta, TROVE.

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