Big Data Trends Arató Bence. BI Consulting
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1 Big Data Trends 2017 Arató Bence BI Consulting 1
2 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.
3 Big data and the Vs
4 Big data and the Vs
5 Big Data in 2016
6 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.
7 9 Google Trends Google Trends
8 10 Google Trends Google Trends
9 11 Google Trends Google Trends
10 12 Google Trends Google Trends
11 Big Data Market The Big Data technology and services market will grow at a 27% compound annual growth rate to $32.4 billion through or about six times the growth rate of the overall ICT market IDC Worldwide Big Data Technology and Services Forecast, Dec 2013
12 Big Data Market
13 Big Data Landscape in 2017 mattturck.com/bigdata2017
14 17 Forrás: Gartner Hype Cycle for Emerging Technologies, 2016 Gartner Hype Cycle for Emerging Technologies, 2016 Hype Cycle
15 Hadoop ecosystem
16 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.
17 Hadoop ecosystem blogs.gartner.com/svetlana-sicular/hadoop-is-dead-long-live-hadoop
18 Hadoop ecosystem blog.dataiku.com/2016/07/19/trends-observed-from-q2-q european-big-data-events
19 Photo: istocks
20 Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics 2017
21 2016 Gartner 2016 Magic Quadrant for Data Warehouse and Database Management Solutions for Analytics
22 2016 Gartner 2016 Magic Quadrant for Data Warehouse and Database Management Solutions for Analytics
23 Gartner 2017 Magic Quadrant for Data Management Solutions for Analytics 2017
24 Gartner Magic Quadrant for Data Science Platforms, February
25 Szállítók
26 Teradata
27 Teradata
28 Hortonworks
29 Hortonworks
30
31 Hortonworks
32 Hortonworks
33
34 Big Data Trends
35 Atscale Big Data Maturity Survey 2016
36 Atscale Big Data Maturity Survey 2016 Big Data vs. DW
37 Atscale Big Data Maturity Survey 2016 Big Data & Cloud
38 HDD vs. SSD
39 HDD vs. SSD
40 jethro.io/hadoop-hive SQL on Big Data
41 jethro.io/hadoop-hive SQL on Big Data
42 Atscale BI on Hadoop Benchmark Q SQL on Big Data
43 Google Trends Spark
44 Google Trends Spark
45 Integrált stack
46 Enterprise adoption
47 Test Data as a service
48 Test Data as a service
49 Data Science
50 M
51 Cloud ML API
52 Cloud ML API 60% of ML applications will run on Amazon, Google, IBM and Microsoft
53
54 A GPU korszak
55 github.com/luanfujun/deep-photo-styletransfer
56 github.com/luanfujun/deep-photo-styletransfer
57 github.com/luanfujun/deep-photo-styletransfer
58
59
60 Hardware
61
62 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
63
64
65
66
67 HW
68
69 GPU for Deep Learning timdettmers.com/2017/03/19/which-gpu-for-deep-learning
70 GPU for Deep Learning Ft Ft timdettmers.com/2017/03/19/which-gpu-for-deep-learning
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