Light news this week. We have results from an interesting survey on fast data, an excellent paper from Facebook and a nice crop of explainers. From one dumb name to another. Dato loses trademark dispute, rebrands as Turi. They should have googled it first. Wikibon’s George Gilbert opines on the state of Big Data performance benchmarks. Spoiler: he thinks that
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Quite a few open source announcements this week. One of the most interesting is Apache Bahir, which includes a number of bits spun out from Apache Spark. It’s another indicator of the size and strength of Spark, in case anyone needs a reminder. In other news, Altiscale and H2O.ai concurrently develop time travel: both vendors claim to support Spark 2.0,
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Google announces that it has designed an application-specific integrated circuit (ASIC) expressly for deep neural nets. Tech press goes bananas. The chips, branded Tensor Processing Units (TPUs) require fewer transistors per operation, so Google can fit more operations per second into the chip. In about a year of operation, Google has achieved an order of magnitude improvement in performance per watt for
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HPE wins the internet this week by announcing the re-re-release of Haven, this time on Azure. The other big story this week: Flink announces Release 1.0. Third Time’s a Charm Hewlett Packard Enterprise (HPE) announces Haven on Demand on Microsoft Azure; PR firestorm ensues. Haven is a loose bundle of software assets salvaged from the train wreck of Autonomy, Vertica, ArcSight
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Every year around this time I review last year’s forecast and publish some thoughts about the coming year. 2015 Assessment First, a brief review of my predictions for 2015: (1) Apache Spark usage will explode. Nailed it. (2) Analytics in the cloud will take off. In 2015, all of the leading cloud platforms — AWS, Azure, IBM and Google — released new tools
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Looking back at 2015, a few stories stand out: Steady progress for Spark, punctuated by two big announcements. Solid growth in cloud-based machine learning, led by Microsoft. Expanding options for SQL and OLAP on Hadoop. In 2015, the most widely read post on this blog was Spark is Too Big to Fail, published in April. I wrote this post in
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With the holidays approaching, we still have some hard news; plus, some explainers and end of 2015 roundups. I’ll post my own roundup of 2015 later this week. On the BlueData blog, Anant Chintamaneni delivers an excellent overview of Hadoop virtualization, and the trend toward decoupling compute and storage. (h/t Hadoop Weekly) Quick Hits In InfoWorld, H2O.ai’s Sri Ambati delivers
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