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

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Page 1: Big Data Trends 2017 - BI Consultingbiconsulting.hu/letoltes/2017budapestdata/arato_bence...Big Data Market The Big Data technology and services market will grow at a 27% compound

Big Data Trends 2017

Arató Bence

BI Consulting

[email protected]

Page 2: Big Data Trends 2017 - BI Consultingbiconsulting.hu/letoltes/2017budapestdata/arato_bence...Big Data Market The Big Data technology and services market will grow at a 27% compound

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.

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Big data and the Vs

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Big data and the Vs

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Big Data in 2016

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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

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9 Google TrendsGoogle Trends

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10 Google TrendsGoogle Trends

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11 Google TrendsGoogle Trends

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12 Google TrendsGoogle Trends

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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

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Big Data Market

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Big Data Landscape in 2017

mattturck.com/bigdata2017

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Hype Cycle

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

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Hadoop ecosystem

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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

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Hadoop ecosystem

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

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Hadoop ecosystem

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

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Photo: iStocks

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2017

Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics

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2016

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

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2016

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

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2017

Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics

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2017

Gartner Magic Quadrant for Data Science Platforms, February 2017

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Szállítók

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Teradata

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Teradata

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Hortonworks

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Hortonworks

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Hortonworks

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Hortonworks

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Big Data Trends

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Atscale Big Data Maturity Survey 2016

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Big Data vs. DW

Atscale Big Data Maturity Survey 2016

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Big Data & Cloud

Atscale Big Data Maturity Survey 2016

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HDD vs. SSD

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HDD vs. SSD

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SQL on Big Data

jethro.io/hadoop-hive

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SQL on Big Data

jethro.io/hadoop-hive

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SQL on Big Data

Atscale BI on Hadoop Benchmark Q4-2016

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Spark

Google Trends

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Spark

Google Trends

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Integrált stack

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Enterprise adoption

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Test Data as a service

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Test Data as a service

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Data Science

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M

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Cloud ML API

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Cloud ML API

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

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A GPU korszak

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github.com/luanfujun/deep-photo-styletransfer

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github.com/luanfujun/deep-photo-styletransfer

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github.com/luanfujun/deep-photo-styletransfer

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

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Hardware

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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

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

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

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HW

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GPU for Deep Learning

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

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GPU for Deep Learning

80.000 Ft

50.000 Ft

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