Learn about using a semantic layer to make data accessible and how to accelerate the business impact of AI and BI at your organization. This session will offer practical advice on how to drive AI & BI business outcomes with an effective data strategy that leverages a semantic layer. You will learn how to achieve quantifiable results by modernizing your data and analytics stack with a semantic layer that delivers an order of magnitude better query performance, increased data team productivity, lower query compute costs, and improved Speed-to-Insights. Attend this session to learn about: - Gaining business alignment and reducing data prep for your AI and BI teams. - Making a consistent set of business metrics “analytics-ready” and accessible. - Accelerating end-to-end query performance while optimizing cloud resources. - Treating “data as a product” and how to drive business value for all consumers.

1. How to use a semantic
layer on Big Data to
drive AI & BI impact
David P. Mariani, CTO & Founder
Dataversity

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3. A semantic layer provides
the agility, consistency,
and control needed to
scale enterprise BI and AI
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4. What a Semantic Layer looks like in real life
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The Semantic Model Tableau Excel Power BI

5. What does a Semantic Layer do?
CONSUMPTION INTEGRATION
DATA INTEGRATION
Connect consumption tools to data LIVE
Connect to data platforms, abstract location & format of data
SEMANTIC
MODELING
MULTI-DIMENSIONAL
CALCULATION ENGINE
PERFORMANCE
OPTIMIZATION
ANALYTICS
GOVERNANCE
Define business-friendly
data models
Support complex, cell-based
expressions
Optimize performance and
cost
Enforce access control and
data policies
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6. How is a
semantic layer
used?
CLOUD ANALYTICS OPTIMIZATION
Speed of thought analytics on live
cloud data, agile data integration with
minimal data movement.
ENTERPRISE METRIS STORE
Single source of governed enterprise
metrics that can be self-served from
any AI/BI tool.
BRIDGING AI AND BI
Common view of data assets and
platform for publishing AI-generated
insights to decision makers.
OLAP MODERNIZATION
Migrate legacy OLAP (e.g. SSAS)
models to modern cloud-first
infrastructure.
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7. Where does a semantic layer fit in the stack?
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DATA CONSUMPTION
SEMANTIC LAYER
DATA API LAYER
DATA PREPARATION
RAW DATA
BI TOOLS AI/ML TOOLS APPLICATIONS
CLOUD DATA
WAREHOUSE
DATA LAKE
ENGINES
SAAS APIs
DATA
TRANSFORMATION
TOOLS
DATA LAKES
SAAS
APPLICATIONS
DATA
CATALO
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8. The Value a
Semantic Layer
Provides
Control the Complexity
and Cost of Analytics
Simplify & Accelerate
Self-service
Provide Consistency &
Governance for AI & BI
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9. TPC-DS 10TB Benchmark: Improvements with AtScale
1. Elapsed time for executing 1 query five times
2. Elapsed time executing 1 (x5), 5, 25, 50 queries
3. Compute costs for cluster time (Redshift, Snowflake) or bytes read (BigQuery) for user concurrency test
4. Complexity score for SQL queries for number of: functions, operations, tables, objects & subqueries (AtScale = 258, TPC-DS = 1,057)
Test
Improvement Factor with AtScale
BigQuery Redshift Snowflake Synapse
Query
Performance1 4x Faster 11x Faster 3x Faster 6x Faster
User Concurrency2 11x Faster 31x Faster 12x Faster 11x Faster
Improved ROI3 3x Better 4x Better 16x Better 2x Better
Complexity4 76% less complex SQL queries
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10. www.atscale.co
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