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Ed Thewlis
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Modern Data Architecture
1.
Bramhope a modern data architecture for
BI © the DataShed Limited 2015
2.
a (not so)
long time ago in a galaxy far, far away… The most complex system you had to handle was an old AS/400 Publishing data weekly was adequate for most users Analysts didn’t really exist Most queries took hours to run – but that was ok © the DataShed Limited 2015
3.
what the hell
happened? © the DataShed Limited 2015
4.
data explosion Source(s): CSC:
http://www.csc.com/insights/flxwd/78931-big_data_universe_beginning_to_explode Gartner: http://www.gartner.com/technology/research/it-spending-forecast/ Growth between 2010 and 2020: Data: 500% Budget: 16% $3.0 $4.0 $5.0 0 5 10 15 20 25 30 35 40 2010 2012 2013 2014 2015 2016 2017 2018 2020 GlobalITBudget($Trillion) GlobalDataVolume(Zettabytes) Data Growth IT Budget Growth Expon. (Data Growth) Expon. (IT Budget Growth) © the DataShed Limited 2015
5.
help is at
hand… © the DataShed Limited 2015
6.
Hadoop & Big
Data tools First incarnation in 2005 Highly-scalable data processing, based on a distributed file system (HDFS) Ability to handle PB size workloads Becoming more mature – including: ANSI-SQL compliant data warehousing tools (Hive & Stinger.next) Batch processing (Map Reduce/Tez, Pig) Operations management (Ambari) Security (Knox) Governance (HCatalog) © the DataShed Limited 2015
7.
…or is it? ©
the DataShed Limited 2015
8.
too small? Small cluster:
5 – 50 nodes Assuming a single node: 24GB RAM Single socket quad core 4-6 2 TB SATA drives Total storage ≈ 10 TB How many of us need to process 10TB of data? Source: http://docs.hortonworks.com/HDPDocuments/HDP1/HDP-1.3.2/bk_cluster-planning-guide/content/conclusion.html © the DataShed Limited 2015
9.
if not Hadoop,
then what? Big Data has driven innovation in both technology and tools. If you can’t adopt the tools, you can still adopt some of the principles: Design for scale-out, rather than up. ELT vs ETL Lambda data architecture … amongst other things! © the DataShed Limited 2015
10.
prepare to scale
out Data Storage Data Integration Data Marts & Cubes Business Intelligence Apps Executives: Dashboards Managers & Stakeholders: Reports Business/Data Analysts: Cubes & Direct Access Specific, small data marts CRM, ERP, Transactional System ETL Tools (SSIS, Informatica, Scripting, Oracle Data Integrator) Data Integration Data Marts & Cubes ETL Tools (SSIS, Informatica, Scripting, Oracle Data Integrator) Data Integration Data Marts & Cubes ETL Tools (SSIS, Informatica, Scripting, Oracle Data Integrator) Specific, small data marts Specific, small data marts Data Storage Data Integration Data Marts & Cubes Business Intelligence Apps Executives: Dashboards Managers & Stakeholders: Reports Business/Data Analysts: Cubes & Direct Access Data marts constructed on top of an EDW Cubes present views of this data to business users ETL Tools (SSIS, Informatica, Scripting, Oracle Data Integrator) CRM, ERP, Transactional System © the DataShed Limited 2015
11.
ELT vs ETL Key
considerations: Metadata & data lineage How real-time is real-time? How long does it take you to get data to analysts? How powerful is your presentation server? Can you use both? vs Schema on read? Or schema on write? © the DataShed Limited 2015
12.
lambda data architecture ©
the DataShed Limited 2015
13.
…most importantly, think differently ©
the DataShed Limited 2015
14.
You don’t need
big data to use Big Data tools Example: Prediction.io (http://prediction.io/) Open Source Machine Learning Server, utilizes Hadoop, HBase, Spark and ElasticSearch © the DataShed Limited 2015
15.
any questions? © the
DataShed Limited 2015
16.
ed thewlis tech director
– the DataShed @edthewlis ed@thedatashed.co.uk © the DataShed Limited 2015
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