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Arcadia Data. Proprietary and Confidential
Take Your Enterprise Analytics to the Next Level with
Native BI Platforms for Data Lakes
April 19, 2018
Arcadia Data. Proprietary and Confidential
Meet Your Presenters
2
Special Guest Speaker:
Boris Evelson
VP, Principal Analyst, Forrester
Boris serves the Application Development
& Delivery role. He is a leading expert in
business intelligence (BI) — a set of
processes, methodologies, and
technologies used to transform raw data
into meaningful, useful, and action-oriented
enterprise information.
Steve Wooledge
VP Marketing, Arcadia Data
Steve Wooledge is responsible for overall
go-to-market strategy and marketing for
Arcadia Data. He is an 17-year veteran of
enterprise analytics software and customer
solutions.
Alex Gutow
Director, Product Marketing, Cloudera
Alex Gutow is the product marketing
director at Cloudera, where she focuses on
the analytic database platform solution and
technologies.
© 2018 FORRESTER. REPRODUCTION PROHIBITED.
© 2018 FORRESTER. REPRODUCTION PROHIBITED.
Take Your Enterprise Analytics to the Next Level
with Native BI Platforms for Data Lakes
Boris Evelson, VP, Principal Analyst
April 19th, 2018
© 2018 Forrester Research, Inc. Reproduction Prohibited 5
Data Driven
Insights Driven
Enterprises
must transform
from data-
driven to
insights-driven
© 2018 Forrester Research, Inc. Reproduction Prohibited 6
Systems of
insight (SOI)
power insights-
driven business
Source: Forrester’s “Digital Insights Are The New Currency Of Business” report
Systems of
engagement
touch people
Systems of
record host
processes
Systems of insight
power digital
businessSystems of
automation
connect the
physical world
© 2018 Forrester Research, Inc. Reproduction Prohibited 7
“A new kind of company
— we call them insights-
driven businesses — has
formed. They are
growing at an average of
more than 30% annually
and are on track to earn
$1.8 trillion by 2021”
© 2018 Forrester Research, Inc. Reproduction Prohibited 8
Income-
Statement-Based
Top- And Bottom-
Line Tangible BI
Benefits
© 2018 Forrester Research, Inc. Reproduction Prohibited 9
Balance-Sheet-Based
Tangible BI Benefits
© 2018 Forrester Research, Inc. Reproduction Prohibited 10
“We are drowning in data and
starving for insight.”
— Global Bank
The quote >10 years old and
we still hear about it from
most of our clients
© 2018 Forrester Research, Inc. Reproduction Prohibited 11
46% of
organizations
still don’t realize
quantitative
BI/analytics ROI
© 2018 Forrester Research, Inc. Reproduction Prohibited 12
49% of
organizations
still take one or
more years to
realize payback
on their
BI/analytics
investments
© 2018 Forrester Research, Inc. Reproduction Prohibited 13
TECHNOLOGY
› Single BI platform
› Streamlined data
architecture
› Centralized support
› Single version of the truth
BUSINESS
› I just want to get my job done
› Single version of the truth is
not my top priority
› Good enough but timely
data/info is good enough for
me
Business and technology pros
are not in complete alignment
© 2018 Forrester Research, Inc. Reproduction Prohibited 14
While the number of companies storing
>100Tb of data almost doubled in 2017…
30%
28%
8%
0%
5%
10%
15%
20%
25%
30%
35%
2015 2016 2017
<10Tb
30% 31%
22%
2015 2016 2017
10Tb-99Tb
31% 31%
59%
2015 2016 2017
>100Tb
Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2017
© 2018 Forrester Research, Inc. Reproduction Prohibited 15
Source: anecdotal evidence
Used
50%
Unused
50%
Used
20%
Unused
80%
Used
33%
Unused
67%
Used
10%
Unused
90%
Structured data
Unstructured
data
Perception Reality
…We only get insights
from a subset of ALL
data available
© 2018 Forrester Research, Inc. Reproduction Prohibited 16
Majority of analytical apps
are still being built using
spreadsheets
› 66% report >50% of BI content
in spreadsheets
› 15% report >80%
Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2016
© 2018 Forrester Research, Inc. Reproduction Prohibited 17
© 2018 Forrester Research, Inc. Reproduction Prohibited 18
We have entered the Age of the Customer
© 2018 Forrester Research, Inc. Reproduction Prohibited 19
Awareness
Dangerous
Formidable
Execution
Clueless
Paralyzed
CI Channel integration
MR Market responsiveness
KD Knowledge dissemination
DP Digital psychology
CM Change management
BI Business intelligence
IE Infrastructure elasticity
PA Process architecture
SI Software innovation
SC Sourcing & supply chain
Business agility is a key success factor in the age of the
customer
Source: Forrester’s “The 10 Dimensions Of Business Agility” report
© 2018 Forrester Research, Inc. Reproduction Prohibited 20
Awareness
Dangerous
Formidable
Execution
Clueless
Paralyzed
Lower performers
CI
MR
KD
DP
CM
BI
IE
PA
SI
SC
Awareness
Dangerous
Formidable
Execution
Clueless
Paralyzed
Higher performers
CIMRKD
DP
CM
BI
IE
PASI
SC
Source: Forrester’s “The 10 Dimensions Of Business Agility” report
Agile enterprises are more likely to be industry leaders
© 2018 Forrester Research, Inc. Reproduction Prohibited 21
Four
components of
Agile BI
22© 2018 Forrester Research, Inc. Reproduction Prohibited
© 2018 Forrester Research, Inc. Reproduction Prohibited 23
Data warehouse
Data hub
Data lake
Modern BI data architecture to get insights from ALL data
Staging area, data mining,
searching, exploration,
profiling, cataloging
Agile insights apps
Mission critical, low latency
insights apps
• Less expensive HW SW
• All enterprise data
• More latency
• Less governance
• Lower data quality
• Used by data scientists
• More expensive HW SW
• Use case specific data
• Less latency
• More governance
• Higher data quality
• Used by end users and
data analysts
Use cases
© 2018 Forrester Research, Inc. Reproduction Prohibited 24
In-memory analytics. Data on
demand
RDBMS. Single version of the
truth. 20%-50% of data
Schema-on-write
SQL on Big Data. 50% of data
Schema-on-read
SQL on Big Data. 80% of data
Data lake. HDFS. NoSQL.
100% of data
Data mining,
search, explore,
profile, catalog
Non mission
critical, agile
analytical apps
Mission
critical, stable
analytical
apps
• Less expensive HW SW
• All enterprise data
• More latency
• Less governance
• Lower data quality
• Used by data scientists
• More expensive HW SW
• Use case specific data
• Less latency
• More governance
• Higher data quality
• Used by end users and
data analysts
Modern BI data architecture to get insights from ALL data
© 2018 Forrester Research, Inc. Reproduction Prohibited 25
› Data movement in/out of
clusters
› Increased WAN/LAN traffic
› JDBC choke point
› SQL
› Metadata is lost in/out of
cluster
› Only queries are distributed
and linearly scalable
Data Node Data Node
Edge Node
JDBC
BI Server
JDBC
Earlier generation BI architecture – bring the data to BI
Browser
Semantic
layer
Cubes/index
Data Node
Data Lake
Cluster
© 2018 Forrester Research, Inc. Reproduction Prohibited 26
Data Node Data Node
Edge Node
BI Server
Semantic layer
Cubes/Index
Next generation BI architecture –V1 – bring BI to the data
Browser
Data Node
Data Lake
Cluster
› No data movement in/out of
clusters
› No extra WAN/LAN traffic
› No JDBC choke point
› SQL and files (JSON, etc.)
› Rich metadata
› Only queries are distributed and
linearly scalable
© 2018 Forrester Research, Inc. Reproduction Prohibited 27
Data Node
Semantic layer
Cubes/Index
Data Node
Semantic layer
Cubes/Index
Edge Node
Rendering
Next generation BI architecture – V2 – bring BI to the data
Browser
Data Node
Semantic layer
Cubes/Index
Data Lake
Cluster
› No data movement in/out of
clusters
› No extra WAN/LAN traffic
› No JDBC choke point
› SQL and files (JSON, etc.)
› Rich metadata
› More functionality “pushed down”
› Linearly scalable
Arcadia Data. Proprietary and Confidential
POLL:
How do you (or plan to) give users access to analyze data in data lake?
1. Earlier generation BI architecture (e.g., Tableau, Qlik,
MicroStrategy)
2. BI middleware accelerators
3. Native BI architecture
28
FORRESTER.COM
Thank you
© 2018 FORRESTER. REPRODUCTION PROHIBITED.
Boris Evelson
bevelson@forrester.com
http://www.forrester.com/Boris-Evelson
http://blogs.forrester.com/boris_evelson
https://twitter.com/bevelson
https://www.linkedin.com/in/bevelson
https://www.facebook.com/ForresterBI
DATA WAREHOUSING & ANALYTICS
WITH CLOUDERA
31 © Cloudera, Inc. All rights reserved.
LIMITATIONS OF EXISTING INFRASTRUCTURE
• Not able to take on more reports,
use cases, users, etc.
• Constrained exploration to prevent
risking critical SLAs
• Proliferation of data silos to address
additional workloads
• Maintain data copies causes
inefficiencies for storage,
processing, and people
• Need to contain costs for
existing workloads
• Difficult to justify budget and
maintenance for expansion
• Struggle to do more with less
• Designed for curated reports, not
iterative, self-service analytics
• Not built for elasticity or object
store integration
Compute
Store
32 © Cloudera, Inc. All rights reserved.
ADVANTAGES OF A MODERN ANALYTIC DATABASE
Data Flexibility
• Iterative modeling and
self-service accessibility
• Portability: No proprietary formats
or storage lock-in
Go Beyond SQL
• Consolidate data silos with
an open architecture
• Shared data across SQL
and non-SQL workloads
High-Performance SQL +
Cost-Effective Scalability
• Elastic scale in any environment
• Cloud-native integration for
optimized pay-per-use costs
• Proven at massive scale
Hybrid Decoupled Architecture
• Runs across multi-cloud & on-prem
for zero lock-in
• Multi-storage over S3, ADLS, HDFS,
Kudu, Isilon, etc
Shared Data
33 © Cloudera, Inc. All rights reserved.
MODERNIZED DATA WAREHOUSING ARCHITECTURE
Fixed
Reports
DATA SOURCES MODERN ANALYTIC DATABASE
Flexible
Reporting
Advanced
Analytics
Self-Service
BI/Ad Hoc
Dashboards/
Analytic Apps
EDW
34© Cloudera, Inc. All rights reserved.
5 keys to success
1) Build a data-driven culture
2) Develop the right team and skills
3) Be agile/lean in development
4) Leverage DevOps for production
5) Right-size data governance
34© Cloudera, Inc. All rights reserved.
35 © Cloudera, Inc. All rights reserved.
CLOUDERA ENTERPRISE
The modern platform for machine learning and analytics optimized for the cloud
Amazon
S3
Microsoft
ADLS HDFS KUDU
SECURITY GOVERNANCE
WORKLOAD
MANAGEMENT
INGEST &
REPLICATION
DATA CATALOG
Core
Services
Storage
Services
ANALYTIC
DATABASE
DATA
SCIENC
E
EXTENSIBLE
SERVICES
OPERATIONAL
DATABASE
DATA
ENGINEERING
Arcadia Data. Proprietary and Confidential
BI – “Native” to Data Lakes
Steve Wooledge
Arcadia Data. Proprietary and Confidential
37
“Data” and “Platforms" Have Changed – Why Haven’t BI Tools?
From To
Data
Platforms
BI Tools
rows and columns and multi-structured
batch and interactive and real-time
small and large volumes
many sources
internal and external
tables and doc’s, search indexes, events
schema on write and schema on read
commodity hardware
ETL and ELT and ELDT
data warehouses and data lakes
rows and columns
batch
smaller data volumes
limited # sources
mainly internal
tables
schema on write
proprietary hardware
ETL
data warehouses
SQL queries
extracts
cubes
BI servers
small/med scale
Why haven’t
BI tools
evolved?
Arcadia Data. Proprietary and Confidential
38
Enterprises Today Need Two Separate BI Standards
Arcadia Data. Proprietary and Confidential
39
Data Warehouse BI Architecture
39
BI Server
Analytic Process
Optimize Physical
Semantic Layer
Secure Data
Load Data
Big Data Requirements
Native Connection
Semi-Structured
Parallel
Real-time
Data Warehouse
(RDBMS)
Arcadia Data. Proprietary and Confidential
40
Data Lake BI Architecture – The Native BI and Analytics Way
40
BI Server
Analytic Process
Optimize Physical
Semantic Layer
Secure Data
Load Data
Big Data Requirements
Native Connection
Semi-Structured
Parallel
Real-time
Data Warehouse
(RDBMS)
Data Lake
(*DFS, Cloud Object Storage)
Arcadia Data was built
from inception to
run natively within data lakes
Arcadia Data. Proprietary and Confidential
41
Query acceleration for scale,
performance,
and concurrency
Smart Acceleration Leverages What Is Learned during Data Discovery
Ad hoc queries
Arcadia Enterprise makes
recommendations –
build these with a click.
Data Lake Cluster
• Fast query responses
• Minimal modeling
• Live acceleration (no downtime)
All Granular
Data
Analytical
Views
Accelerated application
queries
Arcadia Data. Proprietary and Confidential42
Visual Insights To Purchase Paths
“Arcadia Enterprise is the first product we found
that provides truly on-cluster Hadoop BI…
Its execution model and user self-service approach
deliver performance at Hadoop scale and let us
develop our analytics quickly.”
— Director, Global Business Services
Digital Marketing Use Cases
• Increase campaign effectiveness
• Measure brand recognition
• Understand and respond to customer preferences
• Incorporate insights into future products
Challenges
• Fragmented silos of applications with product and brand information
• Lack of granular insight into customer response to marketing campaigns
• Manual process to create reports requires data extraction & movement
Results
• 100s of brand managers have direct access to self-service visual analytics
across all data on the effect of digital campaigns on product performance
• Increased visibility into campaign effectiveness and brand recognition
across geographies
• Marketers and product managers can leverage insights to drive campaign
creation and execution as well as product roadmap
Arcadia Data. Proprietary and Confidential
Data Drives Market DisruptionRetail Store Drill Down
Interactive maps allow for
easy visualization of spatial
data zooming into details
Arcadia Data. Proprietary and Confidential44
Faster Supply Chain Optimization
“Supply chain optimization with visual
analytics has been transformative for
us.”
— Director of BI & Analytics
Use Cases
• Integrate financial and physical flow data
• Self-service visual analytics
Challenges
• One-off consulting project typically costs
hundreds of thousands of dollars and lasts 6-8 months.
Results
• Business analysts have instant access to all data –
no data movement necessary
• Visualizations make it easy to highlight anomalies and
potential issues
• Analysts, engineers, and data scientists all can
create stories directly on the data
Arcadia Data. Proprietary and Confidential
45
BI Native to Data Lakes Provides Faster Time to Value
1. Land / secure data 2. Build semantic
layer
3. Analytic /
Visual Discovery 5. Production
Visual Analytics and BI Native to Data Lake
4. AI-driven
performance
modeling
1. Land / secure data
4. Performance
Modeling - Cube /
Aggregates
6. Analytic /
Visual
Discovery
Data Warehouse or Data Lake
Traditional BI Server or Middleware Cubes
9.
Production
Time to Value in Days
Time in Value in Weeks or Months
2. Transform
3NF or Star
Schema
3. Build
Semantic Layer
7. Performance modeling
(2 places)
Time to Value Delayed Weeks
- One security model
- No movement of data
- Discover and take action
- Model based on usage
8.
Arcadia Data. Proprietary and Confidential
46
Top Use Cases for Native BI and Analytics on Data Lakes
46
MODERN BI PLATFORM &
CUSTOMER
INTELLIGENCE
FINANCIAL SERVICES AND
INSURANCE
RISK & SECURITY
OPTIMIZATION
IOT ANALYTICS
 DW optimization
 Customer 360
 Marketing analytics
 Fundamental Review of
Trading Book (FRTB)
 Trade surveillance
 Anti-money laundering
 Location intelligence
 Cybersecurity
 Security information &
event management
 Fraudulent behavioral
analysis
 Data center monitoring
 Telematics
 System log analysis
 Manufacturing quality
assurance
 Predictive maintenance
Arcadia Data. Proprietary and Confidential
Demo: See It in Action
Social media: @arcadiadataarcadiadata.com 48
Thank You
The Forrester Wave™: Native
Hadoop BI Platforms, Q3 2016 See Cloudera &
Arcadia in Action
Download
Arcadia Instant
https://www.arcadiadata.com/lp/forrester-wave-
hadoop-bi-research-report/
https://www.youtube.com/watch?v=APPpg
GNP5Gs
arcadiadata.com/instant
The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical
representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product,
or service depicted in the Forrester Wave™. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.

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Take Your Enterprise Analytics to the Next Level With Native BI Platforms for Data Lakes

  • 1. Arcadia Data. Proprietary and Confidential Take Your Enterprise Analytics to the Next Level with Native BI Platforms for Data Lakes April 19, 2018
  • 2. Arcadia Data. Proprietary and Confidential Meet Your Presenters 2 Special Guest Speaker: Boris Evelson VP, Principal Analyst, Forrester Boris serves the Application Development & Delivery role. He is a leading expert in business intelligence (BI) — a set of processes, methodologies, and technologies used to transform raw data into meaningful, useful, and action-oriented enterprise information. Steve Wooledge VP Marketing, Arcadia Data Steve Wooledge is responsible for overall go-to-market strategy and marketing for Arcadia Data. He is an 17-year veteran of enterprise analytics software and customer solutions. Alex Gutow Director, Product Marketing, Cloudera Alex Gutow is the product marketing director at Cloudera, where she focuses on the analytic database platform solution and technologies.
  • 3. © 2018 FORRESTER. REPRODUCTION PROHIBITED.
  • 4. © 2018 FORRESTER. REPRODUCTION PROHIBITED. Take Your Enterprise Analytics to the Next Level with Native BI Platforms for Data Lakes Boris Evelson, VP, Principal Analyst April 19th, 2018
  • 5. © 2018 Forrester Research, Inc. Reproduction Prohibited 5 Data Driven Insights Driven Enterprises must transform from data- driven to insights-driven
  • 6. © 2018 Forrester Research, Inc. Reproduction Prohibited 6 Systems of insight (SOI) power insights- driven business Source: Forrester’s “Digital Insights Are The New Currency Of Business” report Systems of engagement touch people Systems of record host processes Systems of insight power digital businessSystems of automation connect the physical world
  • 7. © 2018 Forrester Research, Inc. Reproduction Prohibited 7 “A new kind of company — we call them insights- driven businesses — has formed. They are growing at an average of more than 30% annually and are on track to earn $1.8 trillion by 2021”
  • 8. © 2018 Forrester Research, Inc. Reproduction Prohibited 8 Income- Statement-Based Top- And Bottom- Line Tangible BI Benefits
  • 9. © 2018 Forrester Research, Inc. Reproduction Prohibited 9 Balance-Sheet-Based Tangible BI Benefits
  • 10. © 2018 Forrester Research, Inc. Reproduction Prohibited 10 “We are drowning in data and starving for insight.” — Global Bank The quote >10 years old and we still hear about it from most of our clients
  • 11. © 2018 Forrester Research, Inc. Reproduction Prohibited 11 46% of organizations still don’t realize quantitative BI/analytics ROI
  • 12. © 2018 Forrester Research, Inc. Reproduction Prohibited 12 49% of organizations still take one or more years to realize payback on their BI/analytics investments
  • 13. © 2018 Forrester Research, Inc. Reproduction Prohibited 13 TECHNOLOGY › Single BI platform › Streamlined data architecture › Centralized support › Single version of the truth BUSINESS › I just want to get my job done › Single version of the truth is not my top priority › Good enough but timely data/info is good enough for me Business and technology pros are not in complete alignment
  • 14. © 2018 Forrester Research, Inc. Reproduction Prohibited 14 While the number of companies storing >100Tb of data almost doubled in 2017… 30% 28% 8% 0% 5% 10% 15% 20% 25% 30% 35% 2015 2016 2017 <10Tb 30% 31% 22% 2015 2016 2017 10Tb-99Tb 31% 31% 59% 2015 2016 2017 >100Tb Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2017
  • 15. © 2018 Forrester Research, Inc. Reproduction Prohibited 15 Source: anecdotal evidence Used 50% Unused 50% Used 20% Unused 80% Used 33% Unused 67% Used 10% Unused 90% Structured data Unstructured data Perception Reality …We only get insights from a subset of ALL data available
  • 16. © 2018 Forrester Research, Inc. Reproduction Prohibited 16 Majority of analytical apps are still being built using spreadsheets › 66% report >50% of BI content in spreadsheets › 15% report >80% Source: Forrester’s Business Technographics® Global Data And Analytics Survey, 2016
  • 17. © 2018 Forrester Research, Inc. Reproduction Prohibited 17
  • 18. © 2018 Forrester Research, Inc. Reproduction Prohibited 18 We have entered the Age of the Customer
  • 19. © 2018 Forrester Research, Inc. Reproduction Prohibited 19 Awareness Dangerous Formidable Execution Clueless Paralyzed CI Channel integration MR Market responsiveness KD Knowledge dissemination DP Digital psychology CM Change management BI Business intelligence IE Infrastructure elasticity PA Process architecture SI Software innovation SC Sourcing & supply chain Business agility is a key success factor in the age of the customer Source: Forrester’s “The 10 Dimensions Of Business Agility” report
  • 20. © 2018 Forrester Research, Inc. Reproduction Prohibited 20 Awareness Dangerous Formidable Execution Clueless Paralyzed Lower performers CI MR KD DP CM BI IE PA SI SC Awareness Dangerous Formidable Execution Clueless Paralyzed Higher performers CIMRKD DP CM BI IE PASI SC Source: Forrester’s “The 10 Dimensions Of Business Agility” report Agile enterprises are more likely to be industry leaders
  • 21. © 2018 Forrester Research, Inc. Reproduction Prohibited 21 Four components of Agile BI
  • 22. 22© 2018 Forrester Research, Inc. Reproduction Prohibited
  • 23. © 2018 Forrester Research, Inc. Reproduction Prohibited 23 Data warehouse Data hub Data lake Modern BI data architecture to get insights from ALL data Staging area, data mining, searching, exploration, profiling, cataloging Agile insights apps Mission critical, low latency insights apps • Less expensive HW SW • All enterprise data • More latency • Less governance • Lower data quality • Used by data scientists • More expensive HW SW • Use case specific data • Less latency • More governance • Higher data quality • Used by end users and data analysts Use cases
  • 24. © 2018 Forrester Research, Inc. Reproduction Prohibited 24 In-memory analytics. Data on demand RDBMS. Single version of the truth. 20%-50% of data Schema-on-write SQL on Big Data. 50% of data Schema-on-read SQL on Big Data. 80% of data Data lake. HDFS. NoSQL. 100% of data Data mining, search, explore, profile, catalog Non mission critical, agile analytical apps Mission critical, stable analytical apps • Less expensive HW SW • All enterprise data • More latency • Less governance • Lower data quality • Used by data scientists • More expensive HW SW • Use case specific data • Less latency • More governance • Higher data quality • Used by end users and data analysts Modern BI data architecture to get insights from ALL data
  • 25. © 2018 Forrester Research, Inc. Reproduction Prohibited 25 › Data movement in/out of clusters › Increased WAN/LAN traffic › JDBC choke point › SQL › Metadata is lost in/out of cluster › Only queries are distributed and linearly scalable Data Node Data Node Edge Node JDBC BI Server JDBC Earlier generation BI architecture – bring the data to BI Browser Semantic layer Cubes/index Data Node Data Lake Cluster
  • 26. © 2018 Forrester Research, Inc. Reproduction Prohibited 26 Data Node Data Node Edge Node BI Server Semantic layer Cubes/Index Next generation BI architecture –V1 – bring BI to the data Browser Data Node Data Lake Cluster › No data movement in/out of clusters › No extra WAN/LAN traffic › No JDBC choke point › SQL and files (JSON, etc.) › Rich metadata › Only queries are distributed and linearly scalable
  • 27. © 2018 Forrester Research, Inc. Reproduction Prohibited 27 Data Node Semantic layer Cubes/Index Data Node Semantic layer Cubes/Index Edge Node Rendering Next generation BI architecture – V2 – bring BI to the data Browser Data Node Semantic layer Cubes/Index Data Lake Cluster › No data movement in/out of clusters › No extra WAN/LAN traffic › No JDBC choke point › SQL and files (JSON, etc.) › Rich metadata › More functionality “pushed down” › Linearly scalable
  • 28. Arcadia Data. Proprietary and Confidential POLL: How do you (or plan to) give users access to analyze data in data lake? 1. Earlier generation BI architecture (e.g., Tableau, Qlik, MicroStrategy) 2. BI middleware accelerators 3. Native BI architecture 28
  • 29. FORRESTER.COM Thank you © 2018 FORRESTER. REPRODUCTION PROHIBITED. Boris Evelson bevelson@forrester.com http://www.forrester.com/Boris-Evelson http://blogs.forrester.com/boris_evelson https://twitter.com/bevelson https://www.linkedin.com/in/bevelson https://www.facebook.com/ForresterBI
  • 30. DATA WAREHOUSING & ANALYTICS WITH CLOUDERA
  • 31. 31 © Cloudera, Inc. All rights reserved. LIMITATIONS OF EXISTING INFRASTRUCTURE • Not able to take on more reports, use cases, users, etc. • Constrained exploration to prevent risking critical SLAs • Proliferation of data silos to address additional workloads • Maintain data copies causes inefficiencies for storage, processing, and people • Need to contain costs for existing workloads • Difficult to justify budget and maintenance for expansion • Struggle to do more with less • Designed for curated reports, not iterative, self-service analytics • Not built for elasticity or object store integration Compute Store
  • 32. 32 © Cloudera, Inc. All rights reserved. ADVANTAGES OF A MODERN ANALYTIC DATABASE Data Flexibility • Iterative modeling and self-service accessibility • Portability: No proprietary formats or storage lock-in Go Beyond SQL • Consolidate data silos with an open architecture • Shared data across SQL and non-SQL workloads High-Performance SQL + Cost-Effective Scalability • Elastic scale in any environment • Cloud-native integration for optimized pay-per-use costs • Proven at massive scale Hybrid Decoupled Architecture • Runs across multi-cloud & on-prem for zero lock-in • Multi-storage over S3, ADLS, HDFS, Kudu, Isilon, etc Shared Data
  • 33. 33 © Cloudera, Inc. All rights reserved. MODERNIZED DATA WAREHOUSING ARCHITECTURE Fixed Reports DATA SOURCES MODERN ANALYTIC DATABASE Flexible Reporting Advanced Analytics Self-Service BI/Ad Hoc Dashboards/ Analytic Apps EDW
  • 34. 34© Cloudera, Inc. All rights reserved. 5 keys to success 1) Build a data-driven culture 2) Develop the right team and skills 3) Be agile/lean in development 4) Leverage DevOps for production 5) Right-size data governance 34© Cloudera, Inc. All rights reserved.
  • 35. 35 © Cloudera, Inc. All rights reserved. CLOUDERA ENTERPRISE The modern platform for machine learning and analytics optimized for the cloud Amazon S3 Microsoft ADLS HDFS KUDU SECURITY GOVERNANCE WORKLOAD MANAGEMENT INGEST & REPLICATION DATA CATALOG Core Services Storage Services ANALYTIC DATABASE DATA SCIENC E EXTENSIBLE SERVICES OPERATIONAL DATABASE DATA ENGINEERING
  • 36. Arcadia Data. Proprietary and Confidential BI – “Native” to Data Lakes Steve Wooledge
  • 37. Arcadia Data. Proprietary and Confidential 37 “Data” and “Platforms" Have Changed – Why Haven’t BI Tools? From To Data Platforms BI Tools rows and columns and multi-structured batch and interactive and real-time small and large volumes many sources internal and external tables and doc’s, search indexes, events schema on write and schema on read commodity hardware ETL and ELT and ELDT data warehouses and data lakes rows and columns batch smaller data volumes limited # sources mainly internal tables schema on write proprietary hardware ETL data warehouses SQL queries extracts cubes BI servers small/med scale Why haven’t BI tools evolved?
  • 38. Arcadia Data. Proprietary and Confidential 38 Enterprises Today Need Two Separate BI Standards
  • 39. Arcadia Data. Proprietary and Confidential 39 Data Warehouse BI Architecture 39 BI Server Analytic Process Optimize Physical Semantic Layer Secure Data Load Data Big Data Requirements Native Connection Semi-Structured Parallel Real-time Data Warehouse (RDBMS)
  • 40. Arcadia Data. Proprietary and Confidential 40 Data Lake BI Architecture – The Native BI and Analytics Way 40 BI Server Analytic Process Optimize Physical Semantic Layer Secure Data Load Data Big Data Requirements Native Connection Semi-Structured Parallel Real-time Data Warehouse (RDBMS) Data Lake (*DFS, Cloud Object Storage) Arcadia Data was built from inception to run natively within data lakes
  • 41. Arcadia Data. Proprietary and Confidential 41 Query acceleration for scale, performance, and concurrency Smart Acceleration Leverages What Is Learned during Data Discovery Ad hoc queries Arcadia Enterprise makes recommendations – build these with a click. Data Lake Cluster • Fast query responses • Minimal modeling • Live acceleration (no downtime) All Granular Data Analytical Views Accelerated application queries
  • 42. Arcadia Data. Proprietary and Confidential42 Visual Insights To Purchase Paths “Arcadia Enterprise is the first product we found that provides truly on-cluster Hadoop BI… Its execution model and user self-service approach deliver performance at Hadoop scale and let us develop our analytics quickly.” — Director, Global Business Services Digital Marketing Use Cases • Increase campaign effectiveness • Measure brand recognition • Understand and respond to customer preferences • Incorporate insights into future products Challenges • Fragmented silos of applications with product and brand information • Lack of granular insight into customer response to marketing campaigns • Manual process to create reports requires data extraction & movement Results • 100s of brand managers have direct access to self-service visual analytics across all data on the effect of digital campaigns on product performance • Increased visibility into campaign effectiveness and brand recognition across geographies • Marketers and product managers can leverage insights to drive campaign creation and execution as well as product roadmap
  • 43. Arcadia Data. Proprietary and Confidential Data Drives Market DisruptionRetail Store Drill Down Interactive maps allow for easy visualization of spatial data zooming into details
  • 44. Arcadia Data. Proprietary and Confidential44 Faster Supply Chain Optimization “Supply chain optimization with visual analytics has been transformative for us.” — Director of BI & Analytics Use Cases • Integrate financial and physical flow data • Self-service visual analytics Challenges • One-off consulting project typically costs hundreds of thousands of dollars and lasts 6-8 months. Results • Business analysts have instant access to all data – no data movement necessary • Visualizations make it easy to highlight anomalies and potential issues • Analysts, engineers, and data scientists all can create stories directly on the data
  • 45. Arcadia Data. Proprietary and Confidential 45 BI Native to Data Lakes Provides Faster Time to Value 1. Land / secure data 2. Build semantic layer 3. Analytic / Visual Discovery 5. Production Visual Analytics and BI Native to Data Lake 4. AI-driven performance modeling 1. Land / secure data 4. Performance Modeling - Cube / Aggregates 6. Analytic / Visual Discovery Data Warehouse or Data Lake Traditional BI Server or Middleware Cubes 9. Production Time to Value in Days Time in Value in Weeks or Months 2. Transform 3NF or Star Schema 3. Build Semantic Layer 7. Performance modeling (2 places) Time to Value Delayed Weeks - One security model - No movement of data - Discover and take action - Model based on usage 8.
  • 46. Arcadia Data. Proprietary and Confidential 46 Top Use Cases for Native BI and Analytics on Data Lakes 46 MODERN BI PLATFORM & CUSTOMER INTELLIGENCE FINANCIAL SERVICES AND INSURANCE RISK & SECURITY OPTIMIZATION IOT ANALYTICS  DW optimization  Customer 360  Marketing analytics  Fundamental Review of Trading Book (FRTB)  Trade surveillance  Anti-money laundering  Location intelligence  Cybersecurity  Security information & event management  Fraudulent behavioral analysis  Data center monitoring  Telematics  System log analysis  Manufacturing quality assurance  Predictive maintenance
  • 47. Arcadia Data. Proprietary and Confidential Demo: See It in Action
  • 48. Social media: @arcadiadataarcadiadata.com 48 Thank You The Forrester Wave™: Native Hadoop BI Platforms, Q3 2016 See Cloudera & Arcadia in Action Download Arcadia Instant https://www.arcadiadata.com/lp/forrester-wave- hadoop-bi-research-report/ https://www.youtube.com/watch?v=APPpg GNP5Gs arcadiadata.com/instant The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave™. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.