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Introduction Arató Bence
Managing director of BI Consulting Hungary, with 20+ years of
experience in the BI industry.
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 Learning to fly
Big data and the Vs
Big data and the Vs
6 Forrás: Cloudera
7 Forrás: Hortonworks
Classic vs. Big Data
11 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
13 Google Trends Google Trends
14 Google Trends Google Trends
15 Google Trends Google Trends
16 Google Trends Google Trends
Big Data Landscape in 2017
mattturck.com/bigdata2017
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
Wikibon
Hype Cycle
21 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
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 2017 Magic Quadrant for Data Management Solutions for Analytics
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
Oracle.com
Cloud services
Photo: pixabay.com
Cloud services
cloud.oracle.com
Cloud services
SQL on Big Data
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
SQL on Big Data
Atscale BI on Hadoop Benchmark Q4-2016
Spark
Google Trends
Spark
Google Trends
Oracle & Spark
technology.amis.nl/2016/10/01/spark-with-a-k-how-apache-spark-is-omnipresent-at-oracle-openworld-2016
Oracle & Spark
technology.amis.nl/2016/10/01/spark-with-a-k-how-apache-spark-is-omnipresent-at-oracle-openworld-2016
Oracle Big Data SQL
oracle.com
Oracle Big Data SQL
oracle.com
Oracle Big Data SQL
blogs.oracle.com/datawarehousing/entry/big_data_sql_quick_start6
Oracle Big Data SQL
blogs.oracle.com/datawarehousing/entry/big_data_sql_quick_start6
GPU Performance
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
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
GPU for Deep Learning
80.000 Ft
50.000 Ft
timdettmers.com/2017/03/19/which-gpu-for-deep-learning
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