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Big Data Trends 2017 Arató Bence BI Consulting [email protected] 1

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  • Big Data Trends 2017

    Arató Bence

    BI Consulting

    [email protected]

  • IntroductionArató Bence

    Consulting and Advisory

    BI/DW/Big Data strategy, Architecture planning, vendor and tool

    selection. Also provides QA and on-the-job mentoring services.

    Publications

    Editor of the BI.hu portal and the BI Yearbook series.

    Research

    Leader of the Hungary-focused BI-TREK, DW-TREK and NOSQL-TREK

    surveys.

    Teaching

    Teaching several courses at BI Akadémia from data visualization to Big

    Data.

    Conferences and events

    Head organizer of the Budapest Data Forum, Budapest BI Forum and

    Budapest NOSQL Forum conferences and several data-related

    meetups.

  • Big data and the Vs

  • Big data and the Vs

  • Big Data in 2016

  • Big Data – hype is over?

    „The biggest big data event of 2016 was people ceasing to talk about big data. Big data now 'just is'. „

    „2016, felt like Big Data was losing the buzz as compared to a few years ago”

    2016 was an exciting year for big data, as finally, Big data is no longer a hype or a buzzword.

    www.kdnuggets.com/2016/12/big-data-main-developments-2016-key-trends-2017.html

  • 9 Google TrendsGoogle Trends

  • 10 Google TrendsGoogle Trends

  • 11 Google TrendsGoogle Trends

  • 12 Google TrendsGoogle Trends

  • Big Data Market

    The Big Data technology and services

    market will grow at a 27% compound

    annual growth rate to $32.4 billion

    through 2017 - or about six times the

    growth rate of the overall ICT market

    IDC Worldwide Big Data Technology and Services 2013-2017 Forecast, Dec 2013

  • Big Data Market

  • Big Data Landscape in 2017

    mattturck.com/bigdata2017

  • Hype Cycle

    17 Forrás: Gartner Hype Cycle for Emerging Technologies, 2016 Gartner Hype Cycle for Emerging Technologies, 2016

  • Hadoop ecosystem

  • Hadoop ecosystem

    „Hadoop declined more rapidly in 2016 from the big-data landscape than I expected. MapReduce, HBase, and even HDFS are less relevant to data scientists than ever.”

    www.kdnuggets.com/2016/12/big-data-main-developments-2016-key-trends-2017.html

  • Hadoop ecosystem

    blogs.gartner.com/svetlana-sicular/hadoop-is-dead-long-live-hadoop

  • Hadoop ecosystem

    blog.dataiku.com/2016/07/19/trends-observed-from-q2-q3-2016-european-big-data-events

  • Photo: iStocks

  • 2017

    Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics

  • 2016

    Gartner 2016 Magic Quadrant for Data Warehouse and Database Management Solutions for Analytics

  • 2016

    Gartner 2016 Magic Quadrant for Data Warehouse and Database Management Solutions for Analytics

  • 2017

    Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics

  • 2017

    Gartner Magic Quadrant for Data Science Platforms, February 2017

  • Szállítók

  • Teradata

  • Teradata

  • Hortonworks

  • Hortonworks

  • Hortonworks

  • Hortonworks

  • Big Data Trends

  • Atscale Big Data Maturity Survey 2016

  • Big Data vs. DW

    Atscale Big Data Maturity Survey 2016

  • Big Data & Cloud

    Atscale Big Data Maturity Survey 2016

  • HDD vs. SSD

  • HDD vs. SSD

  • SQL on Big Data

    jethro.io/hadoop-hive

  • SQL on Big Data

    jethro.io/hadoop-hive

  • SQL on Big Data

    Atscale BI on Hadoop Benchmark Q4-2016

  • Spark

    Google Trends

  • Spark

    Google Trends

  • Integrált stack

  • Enterprise adoption

  • Test Data as a service

  • Test Data as a service

  • Data Science

  • M

  • Cloud ML API

  • Cloud ML API

    60% of ML applications will run on Amazon, Google, IBM and Microsoft

  • A GPU korszak

  • github.com/luanfujun/deep-photo-styletransfer

  • github.com/luanfujun/deep-photo-styletransfer

  • github.com/luanfujun/deep-photo-styletransfer

  • www.nextplatform.com/2017/03/20/google-team-refines-gpu-powered-neural-machine-translation

  • Hardware

  • One shortcoming of current NMT architectures is the amount of compute required to train them. Training on real-world datasets of several million examples typically requires dozens of GPUs and convergence time is on the order of days to weeks.

    ... an effort that required more than 250,000 GPU hours on their in-house cluster, which is based on Nvidia Tesla K40m and Tesla K80 GPUs

    www.nextplatform.com/2017/03/20/google-team-refines-gpu-powered-neural-machine-translation

  • www.nvidia.com/object/gpu-accelerated-applications-tensorflow-benchmarks.html

  • www.nvidia.com/object/gpu-accelerated-applications-tensorflow-benchmarks.html

  • HW

  • GPU for Deep Learning

    timdettmers.com/2017/03/19/which-gpu-for-deep-learning

  • GPU for Deep Learning

    80.000 Ft

    50.000 Ft

    timdettmers.com/2017/03/19/which-gpu-for-deep-learning