With cloud object storage, you'd expect business intelligence (BI) applications to benefit from the scale of data and real-time analytics. However, traditional BI in the cloud surfaces non-obvious challenges. Priyank Patel reviews service-oriented cloud design (storage, compute, catalog, security, SQL) and shows how native cloud BI provides analytic depth, low cost, and high performance.

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Design Patterns for BI in the Cloud
Priyank Patel, Co-founder and CPO
March 28th, 2019

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Canonical Architecture for BI – Circa 2000s
Data warehouse
In-memory Cubing
Data mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents

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Key Principles of the Legacy Design…
MPP
Data warehouse
In-memory Cubing
Data mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents
 Co-located compute and storage
 Scale together
 Data duplicated in multiple systems
 Governance and security overhead
 Data summarized
 Choice between high performance or high granularity

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Cloud is Built on Decoupled Compute and Storage
4
…
Compute Compute Compute
S3 ADLS GCS
Cloud Storage
Cloud Compute
instances, containers, server less
… …

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Lift-and-Shift Existing Architecture to the Cloud
Data warehouse
In-memory Cubing
Data mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents

6. Arcadia Data. Proprietary and Confidential
Lift-and-Shift Existing Architecture to the Cloud
Data Warehouse
In-memory Cubing
Data Mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents
Cloud Storage
Cloud Lake

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Lift-and-Shift Architecture
Data Warehouse
In-memory Cubing
Data Mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents
Cloud Storage
Cloud Data Lake
Well suited when :
 Analytics can be done with small or medium
scale data
Misses the mark by :
 Increasing governance and data movement
burden
 Higher security vuln footprint
 Increasing latency to analytics

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What happens if you turn this on its head ?

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Data warehouse
In-memory Cubing
Data mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents
Cloud storage
Cloud lake
Instead of
moving
data to BI …
Move BI to data

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Cloud Native BI Architecture
Data warehouse
In-memory Cubing
Data mart
Visualization
Data Sources
Customer
Records
Customer
Information
Product Data
Industry-Wide
Data
Unstructured
Documents
Cloud storage
Cloud lake
Compute Compute Compute
Distributed
BI/OLAP
Move BI to data

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Modernize BI Architecture: “In-Data Lake BI”
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“ 3. Bring BI to data — physically:
… now you can "bring BI to data": run BI/analytics on exactly the same platform where the
DBMS is located. Such in-data-lake BI architecture reduces LAN/WAN data movement;
eliminates "choke points" like JDBC connectors; and enables BI applications, not just
DBMSes, to be fully distributed.
In addition to these technical merits, there's also a real business benefit: Curating and
modeling data before it is analyzed limits BI use cases. Analyzing data "as is" broadens the
number of questions it can answer.”

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Leading Companies are Now Choosing Two Enterprise BI Standards

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Business users can easily analyze data.
Brings BI to data in existing data platforms.
Integrated to shared metadata and security.
Requirements for Modern BI Native to Cloud and Data Lakes
Self-Service
In-Data-Lake BI
(Distributed BI)
Shared-service

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Arcadia Enterprise
ArcEngine. Distributed BI engine
runs directly on cloud storage.
Elastic and containerized
Analytical Views. Pre-computed partial
aggregates to accelerate reports and
analytics.
Automatically recommended
Incrementally maintained
ArcViz. Native Visualizations.
Cloud Instances or Containers
3
2
1
3rd Party BI. Connect to any BI tool of choice3
Distributed BI and
OLAP
Cloud Object Storage
Smart
Acceleration

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See Arcadia Smart Acceleration in Action
6 Minute video:
“Learn Arcadia Data - Smart Acceleration and Analytical Views”
https://www.youtube.com/watch?v=3VOYot7QfWM

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Data Drives Market Disruption
 Sometimes a lift-and-shift architecture works
 Data is small or medium sized
 But a decoupling of compute and storage is truly powerful only if the architecture of the
BI engines leverage it
 No duplication of data
 Distributed BI and OLAP capability, elastic scale up and down
 Automatically optimize
In Summary
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