Big Data Trends Arató Bence. BI Consulting

Big Data Trends 2017 Arató Bence BI Consulting arato@biconsulting.hu 1

Introduction Arató 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 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 Trends Google Trends

10 Google Trends Google Trends

11 Google Trends Google Trends

12 Google Trends Google 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

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

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

Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics 2017

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

Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics 2017

Gartner Magic Quadrant for Data Science Platforms, February 2017 2017

Szállítók

Teradata

Hortonworks

Big Data Trends

Atscale Big Data Maturity Survey 2016

Atscale Big Data Maturity Survey 2016 Big Data vs. DW

Atscale Big Data Maturity Survey 2016 Big Data & Cloud

HDD vs. SSD

jethro.io/hadoop-hive SQL on Big Data

Atscale BI on Hadoop Benchmark Q4-2016 SQL on Big Data

Google Trends Spark

Integrált stack

Enterprise adoption

Test Data as a service

Data Science

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

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

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