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Big Data Management:
What’s New, What’s Different
and What You Need to Know
2
Today’s Featured Presenter
Matt Aslett
Research Director,
Data Platforms and Analytics
451 Research
As Research Director, Matt has overall responsibility for the data platforms and
analytics research coverage, which includes operational and analytic databases,
Hadoop, grid/cache, stream processing, search-based data platforms, data
integration, data quality, data management, analytics, and advanced analytics.
Matt's own primary area of focus includes data management, reporting and
analytics, and exploring how the various data platform and analytics technology
sectors are converging in the form of next-generation data platform
33
Agenda
• Big Data Management
– Matt Aslett, 451 Research
• SnapLogic Overview
• SnapLogic Demonstration
– Ravi Dharnikota, Head of SnapLogic Enterprise Architecture
• Q&A
Copyright (C) 2016 451 Research LLC
Big Data Management
Matt Aslett, Research Director
Copyright (C) 2016 451 Research LLC
451 Research is a leading IT research & advisory company
5
Founded in 2000
250+ employees, including over 100 analysts
1,000+ clients: Technology & Service providers, corporate
advisory, finance, professional services, and IT decision makers
50,000+ IT professionals, business users and consumers in our research
community
Over 52 million data points published each quarter and 4,500+ reports
published each year
2,000+ technology & service providers under coverage
451 Research and its sister company, Uptime Institute, are the two divisions
of The 451 Group
Headquartered in New York City, with offices in London, Boston, San
Francisco, Washington DC, Mexico, Costa Rica, Brazil, Spain, UAE, Russia,
Taiwan, Singapore and Malaysia
Research & Data
Advisory
Events
Go 2 Market
Copyright (C) 2016 451 Research LLC
Big data and beyond
• V is for various things…
but does not define big data
3
Copyright (C) 2016 451 Research LLC
Big data and beyond
• V is for various things…
but does not define big data
• To understand the trends driving
‘big data’ 451 Research focused
beyond the nature of the data on
what enterprises wanted to do
with it
4
Copyright (C) 2016 451 Research LLC
Big data and beyond
8
• V is for various things…
but does not define big data
• To understand the trends driving
‘big data’ 451 Research focused
beyond the nature of the data on
what enterprises wanted to do
with it
• Totality – storing and processing all data (or as much as is economically viable)
• Exploration – schema-free approaches to analyzing data to identify new patterns
• Frequency – more frequent analysis of data to enable real-time decision making
Copyright (C) 2016 451 Research LLC
‘Big data’ is primarily driven by economics, not data
6
• ‘Big Data’ is the realization of competitive advantage based on the fact that it is now
more economically feasible to store and process data that was previously ignored due
to the cost and functional limitations of traditional data management technologies to
handle its volume, velocity and variety
Copyright (C) 2016 451 Research LLC
‘Big data’ is primarily driven by economics, not data
6
“Big data is what happened when the cost of keeping information became less than the cost of throwing
it away.”
George Dyson
• ‘Big Data’ is the realization of competitive advantage based on the fact that it is now
more economically feasible to store and process data that was previously ignored due
to the cost and functional limitations of traditional data management technologies to
handle its volume, velocity and variety
Copyright (C) 2016 451 Research LLC
‘Big data’ is primarily driven by economics, not data
7
“Big data is what happened when the cost of keeping information became less than the cost of throwing
it away.”
George Dyson
• ‘Big Data’ is the realization of competitive advantage based on the fact that it is now
more economically feasible to store and process data that was previously ignored due
to the cost and functional limitations of traditional data management technologies to
handle its volume, velocity and variety
• Moved from storing 1% of data for 60 days in EDW @ $100,000/TB
• To 100% of data for a year in Hadoop @ $900/TB
Copyright (C) 2016 451 Research LLC
Source: 451 Research, Total Data Analytics 2016
The evolution of enterprise analytics
12
REPORTING
- What happened
ANALYSIS
- Why did it happen?
PRESCRIPTIVE
- Influence what happens
STATISTICAL
MODELING
MACHINE
LEARNING
DESCRIPTIVE
- What is happening?
PREDICTIVE
- What will happen?
Complexity
AutomatedUser-drivenIT-driven
VISUALIZATION
Copyright (C) 2016 451 Research LLC
Data sources:
Multi-structured
RDBMS,
Hadoop, NoSQL,
stream processing,
historical and real-time
Source: 451 Research, Total Data Analytics 2016
Data sources:
Structured,
RDBMS,
historical
The evolution of enterprise analytics
13
REPORTING
- What happened
ANALYSIS
- Why did it happen?
PRESCRIPTIVE
- Influence what happens
STATISTICAL
MODELING
MACHINE
LEARNING
DESCRIPTIVE
- What is happening?
PREDICTIVE
- What will happen?
Complexity
AutomatedUser-drivenIT-driven
VISUALIZATION
Copyright (C) 2016 451 Research LLC
EDW vs Hadoop (Schema-on-write vs schema-on-read)
14
Source: https://www.flickr.com/photos/wbaiv/16510090506/ Source: https://www.flickr.com/photos/notbrucelee/5696238930/
Copyright (C) 2016 451 Research LLC
Schema-on-write
15
Source: https://www.flickr.com/photos/wbaiv/16510090506/
• Pre-prepared
• Single-purpose
• Some assembly required
• Inflexible
Copyright (C) 2016 451 Research LLC
Schema-on-read
16
Source: https://www.flickr.com/photos/notbrucelee/5696238930/
• Flexible
• Reusable
• Some imagination required*
• Multi-purpose
• *Instructions available if desired
Copyright (C) 2016 451 Research LLC
Hadoop-based data lakes
• The concept of the data lake
has taken off in recent years,
with the Apache Hadoop
data-processing framework
serving as the unified
repository into which raw
data is landed from multiple
sources and made available
to multiple users for multiple
purposes.
17
Photo: Myrabella / Wikimedia Commons, CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=11263585
Copyright (C) 2016 451 Research LLC
Hadoop-based data lakes
• The concept of the data lake
has taken off in recent years,
with the Apache Hadoop
data-processing framework
serving as the unified
repository into which raw
data is landed from multiple
sources and made available
to multiple users for multiple
purposes.
• Beware the data swamp
18
https://www.flickr.com/photos/lofink/4501610335/
Copyright (C) 2016 451 Research LLC
Data governance, data preparation and the data lake
• Data needs to be filtered, processed, treated
and managed to make it suitable for multiple
analytics use cases.
• Data governance
• Data catalog
• Data security
• Data lineage
• Data preparation
• Data discovery
• Data cleansing
• Data harmonization
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• Data inventory
• Data quality
• Data pipelines
• Data enrichment
• Data matching
• Collaboration
Copyright (C) 2016 451 Research LLC
Data governance, data preparation and the data lake
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DATA-AS-A-SERVICE
PARTNERS
SUPPLIERS
SELF-SERVICE
DATA PREPARATION
IT
DATA LAKE
APPLICATIONS
DATA GOVERNANCE
Data lineage Data inventory
Data catalog
Data security Data quality
Data pipelines
DATA STEWARDS
Data cleansing
Data harmonization
Data discovery
Collaboration
Data matching
Data enrichment
ADVANCED ANALYTICS
DATA SCIENTISTS
SELF-SERVICE ANALYTICS
SENIOR EXECUTIVES BUSINESS ANALYSTS DATA ANALYSTS
Copyright (C) 2016 451 Research LLC
Hadoop and other animals
21
Copyright (C) 2016 451 Research LLC
Recommendations
22
• Enterprises should seriously consider the data governance and management requirements before
embarking on data lake projects to ensure that the functionality is available to turn the concept into
reality.
• For flexibility and agility, employ data management approaches and technologies that abstract data
processing pipelines from the execution environment.
• Look for data integration and transformation technologies that execute natively, taking advantage of
the underlying engine (e.g. Spark, YARN).
• Seek out data management and integration technologies that enable consumption and
transformation of large volumes of structured and unstructured data.
Copyright (C) 2016 451 Research LLC
Thank You!
matthew.aslett@451research.com
@maslett
www.451research.com
SnapLogic Elastic Integration
Accelerate Your Integration. Accelerate Your Business
“We can do more in two hours with SnapLogic than we could in two days with traditional solutions.”
25
CSV
Big Data and hybrid cloud environments are making
yesterday’s approaches to integration obsolete
26
Anything
apps | data | APIs | things
SnapLogic: Unified Platform for Data and Application Integration
Anytime
batch | streaming | real-time
Anywhere
on prem | cloud | hybrid
2727
SnapLogic in the Modern Data Fabric: Ingest, Transform, Deliver
ConsumeStore&ProcessSource
z z z z
HANA
Data Warehouses &
Data Marts
Big Data and Data
Lakes
INGEST INGEST
Data Integration and
Transformation
On Prem
Applications
Relational
Databases
Cloud
Applications
NoSQL
Databases
Web
Logs
Internet of
Things
DELIVER DELIVER
28
Modern Architecture: Hybrid and Elastic Execution
Streams: No data is
stored/cached
Secure: 100%
standards-based
Elastic: Scales out &
handles data and app
integration use cases
Metadata
Data
Databases
On Prem
Apps
Big Data
Cloud Apps
and DataCloud-Based Designer, Manager,
Dashboard
Execution
Execution
Execution
Firewall
SnapLogic “respects data’s gravity.”
SnapLogic Demonstration
30
Discussion
Matt Aslett
Research Director,
Data Platforms and Analytics
451 Research
Ravi Dharnikota
Head of Enterprise Architecture
SnapLogic
31
Integrate at the speed of
modern business
+1 888-494-1570
sales@snaplogic.com
@SnapLogic
www.snaplogic.com

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Big Data Management: What's New, What's Different, and What You Need To Know

  • 1. 1 Big Data Management: What’s New, What’s Different and What You Need to Know
  • 2. 2 Today’s Featured Presenter Matt Aslett Research Director, Data Platforms and Analytics 451 Research As Research Director, Matt has overall responsibility for the data platforms and analytics research coverage, which includes operational and analytic databases, Hadoop, grid/cache, stream processing, search-based data platforms, data integration, data quality, data management, analytics, and advanced analytics. Matt's own primary area of focus includes data management, reporting and analytics, and exploring how the various data platform and analytics technology sectors are converging in the form of next-generation data platform
  • 3. 33 Agenda • Big Data Management – Matt Aslett, 451 Research • SnapLogic Overview • SnapLogic Demonstration – Ravi Dharnikota, Head of SnapLogic Enterprise Architecture • Q&A
  • 4. Copyright (C) 2016 451 Research LLC Big Data Management Matt Aslett, Research Director
  • 5. Copyright (C) 2016 451 Research LLC 451 Research is a leading IT research & advisory company 5 Founded in 2000 250+ employees, including over 100 analysts 1,000+ clients: Technology & Service providers, corporate advisory, finance, professional services, and IT decision makers 50,000+ IT professionals, business users and consumers in our research community Over 52 million data points published each quarter and 4,500+ reports published each year 2,000+ technology & service providers under coverage 451 Research and its sister company, Uptime Institute, are the two divisions of The 451 Group Headquartered in New York City, with offices in London, Boston, San Francisco, Washington DC, Mexico, Costa Rica, Brazil, Spain, UAE, Russia, Taiwan, Singapore and Malaysia Research & Data Advisory Events Go 2 Market
  • 6. Copyright (C) 2016 451 Research LLC Big data and beyond • V is for various things… but does not define big data 3
  • 7. Copyright (C) 2016 451 Research LLC Big data and beyond • V is for various things… but does not define big data • To understand the trends driving ‘big data’ 451 Research focused beyond the nature of the data on what enterprises wanted to do with it 4
  • 8. Copyright (C) 2016 451 Research LLC Big data and beyond 8 • V is for various things… but does not define big data • To understand the trends driving ‘big data’ 451 Research focused beyond the nature of the data on what enterprises wanted to do with it • Totality – storing and processing all data (or as much as is economically viable) • Exploration – schema-free approaches to analyzing data to identify new patterns • Frequency – more frequent analysis of data to enable real-time decision making
  • 9. Copyright (C) 2016 451 Research LLC ‘Big data’ is primarily driven by economics, not data 6 • ‘Big Data’ is the realization of competitive advantage based on the fact that it is now more economically feasible to store and process data that was previously ignored due to the cost and functional limitations of traditional data management technologies to handle its volume, velocity and variety
  • 10. Copyright (C) 2016 451 Research LLC ‘Big data’ is primarily driven by economics, not data 6 “Big data is what happened when the cost of keeping information became less than the cost of throwing it away.” George Dyson • ‘Big Data’ is the realization of competitive advantage based on the fact that it is now more economically feasible to store and process data that was previously ignored due to the cost and functional limitations of traditional data management technologies to handle its volume, velocity and variety
  • 11. Copyright (C) 2016 451 Research LLC ‘Big data’ is primarily driven by economics, not data 7 “Big data is what happened when the cost of keeping information became less than the cost of throwing it away.” George Dyson • ‘Big Data’ is the realization of competitive advantage based on the fact that it is now more economically feasible to store and process data that was previously ignored due to the cost and functional limitations of traditional data management technologies to handle its volume, velocity and variety • Moved from storing 1% of data for 60 days in EDW @ $100,000/TB • To 100% of data for a year in Hadoop @ $900/TB
  • 12. Copyright (C) 2016 451 Research LLC Source: 451 Research, Total Data Analytics 2016 The evolution of enterprise analytics 12 REPORTING - What happened ANALYSIS - Why did it happen? PRESCRIPTIVE - Influence what happens STATISTICAL MODELING MACHINE LEARNING DESCRIPTIVE - What is happening? PREDICTIVE - What will happen? Complexity AutomatedUser-drivenIT-driven VISUALIZATION
  • 13. Copyright (C) 2016 451 Research LLC Data sources: Multi-structured RDBMS, Hadoop, NoSQL, stream processing, historical and real-time Source: 451 Research, Total Data Analytics 2016 Data sources: Structured, RDBMS, historical The evolution of enterprise analytics 13 REPORTING - What happened ANALYSIS - Why did it happen? PRESCRIPTIVE - Influence what happens STATISTICAL MODELING MACHINE LEARNING DESCRIPTIVE - What is happening? PREDICTIVE - What will happen? Complexity AutomatedUser-drivenIT-driven VISUALIZATION
  • 14. Copyright (C) 2016 451 Research LLC EDW vs Hadoop (Schema-on-write vs schema-on-read) 14 Source: https://www.flickr.com/photos/wbaiv/16510090506/ Source: https://www.flickr.com/photos/notbrucelee/5696238930/
  • 15. Copyright (C) 2016 451 Research LLC Schema-on-write 15 Source: https://www.flickr.com/photos/wbaiv/16510090506/ • Pre-prepared • Single-purpose • Some assembly required • Inflexible
  • 16. Copyright (C) 2016 451 Research LLC Schema-on-read 16 Source: https://www.flickr.com/photos/notbrucelee/5696238930/ • Flexible • Reusable • Some imagination required* • Multi-purpose • *Instructions available if desired
  • 17. Copyright (C) 2016 451 Research LLC Hadoop-based data lakes • The concept of the data lake has taken off in recent years, with the Apache Hadoop data-processing framework serving as the unified repository into which raw data is landed from multiple sources and made available to multiple users for multiple purposes. 17 Photo: Myrabella / Wikimedia Commons, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=11263585
  • 18. Copyright (C) 2016 451 Research LLC Hadoop-based data lakes • The concept of the data lake has taken off in recent years, with the Apache Hadoop data-processing framework serving as the unified repository into which raw data is landed from multiple sources and made available to multiple users for multiple purposes. • Beware the data swamp 18 https://www.flickr.com/photos/lofink/4501610335/
  • 19. Copyright (C) 2016 451 Research LLC Data governance, data preparation and the data lake • Data needs to be filtered, processed, treated and managed to make it suitable for multiple analytics use cases. • Data governance • Data catalog • Data security • Data lineage • Data preparation • Data discovery • Data cleansing • Data harmonization 19 • Data inventory • Data quality • Data pipelines • Data enrichment • Data matching • Collaboration
  • 20. Copyright (C) 2016 451 Research LLC Data governance, data preparation and the data lake 20 DATA-AS-A-SERVICE PARTNERS SUPPLIERS SELF-SERVICE DATA PREPARATION IT DATA LAKE APPLICATIONS DATA GOVERNANCE Data lineage Data inventory Data catalog Data security Data quality Data pipelines DATA STEWARDS Data cleansing Data harmonization Data discovery Collaboration Data matching Data enrichment ADVANCED ANALYTICS DATA SCIENTISTS SELF-SERVICE ANALYTICS SENIOR EXECUTIVES BUSINESS ANALYSTS DATA ANALYSTS
  • 21. Copyright (C) 2016 451 Research LLC Hadoop and other animals 21
  • 22. Copyright (C) 2016 451 Research LLC Recommendations 22 • Enterprises should seriously consider the data governance and management requirements before embarking on data lake projects to ensure that the functionality is available to turn the concept into reality. • For flexibility and agility, employ data management approaches and technologies that abstract data processing pipelines from the execution environment. • Look for data integration and transformation technologies that execute natively, taking advantage of the underlying engine (e.g. Spark, YARN). • Seek out data management and integration technologies that enable consumption and transformation of large volumes of structured and unstructured data.
  • 23. Copyright (C) 2016 451 Research LLC Thank You! matthew.aslett@451research.com @maslett www.451research.com
  • 24. SnapLogic Elastic Integration Accelerate Your Integration. Accelerate Your Business “We can do more in two hours with SnapLogic than we could in two days with traditional solutions.”
  • 25. 25 CSV Big Data and hybrid cloud environments are making yesterday’s approaches to integration obsolete
  • 26. 26 Anything apps | data | APIs | things SnapLogic: Unified Platform for Data and Application Integration Anytime batch | streaming | real-time Anywhere on prem | cloud | hybrid
  • 27. 2727 SnapLogic in the Modern Data Fabric: Ingest, Transform, Deliver ConsumeStore&ProcessSource z z z z HANA Data Warehouses & Data Marts Big Data and Data Lakes INGEST INGEST Data Integration and Transformation On Prem Applications Relational Databases Cloud Applications NoSQL Databases Web Logs Internet of Things DELIVER DELIVER
  • 28. 28 Modern Architecture: Hybrid and Elastic Execution Streams: No data is stored/cached Secure: 100% standards-based Elastic: Scales out & handles data and app integration use cases Metadata Data Databases On Prem Apps Big Data Cloud Apps and DataCloud-Based Designer, Manager, Dashboard Execution Execution Execution Firewall SnapLogic “respects data’s gravity.”
  • 30. 30 Discussion Matt Aslett Research Director, Data Platforms and Analytics 451 Research Ravi Dharnikota Head of Enterprise Architecture SnapLogic
  • 31. 31 Integrate at the speed of modern business +1 888-494-1570 sales@snaplogic.com @SnapLogic www.snaplogic.com

Notas do Editor

  1. Cast your mind back to 2010/11 – everyone is trying to define ‘big data’ with words beginning with V. 451 Research took a different tack
  2. Cast your mind back to 2010/11 – everyone is trying to define ‘big data’ with words beginning with V. 451 Research took a different tack
  3. Cast your mind back to 2010/11 – everyone is trying to define ‘big data’ with words beginning with V. 451 Research took a different tack
  4. Connecting applications or data from multiple sources is not new – ESB, SOA, ETL have been around for a long time. But the old ways are not keeping up with today’s realities…
  5. Leading enterprises choose SnapLogic because we help them connect data and applications faster. We connect anything: sources including applications, APIs, things, or data We connect anytime: in batches, streaming, or in real time And we connect anywhere: on premises, in the cloud or a combination of both
  6. Here is an example of a SnapLogic deployment. The SnapLogic control plane – including he Designer, Manager and Dashboard - does not store your data. It’s metadata only. Once a pipeline is executed, it looks for the associated Snaplex or Hadooplex. The plex dynamically scales out, adding more nodes as needed. We like to say that SnapLogic “respects data gravity” and runs as close to the data as need be. If you are integrating only cloud applications, it would make no sense to run your integrations behind the firewall. Similarly, if you’re doing ground to ground or cloud to ground, you may want to run your Snaplex on Window or Linux servers. Note that the dotted line is sending instructions via metadata to the plex, which is waiting to run. The solid line indicates how data movies bi-directionally between systems.
  7. Leading enterprises choose SnapLogic because we help them connect data and applications faster. We connect anything: sources including applications, APIs, things, or data We connect anytime: in batches, streaming, or in real time And we connect anywhere: on premises, in the cloud or a combination of both