Learn how to empower your analysts with easier access to all the data they need, exactly when they need it - all while reducing workloads for IT and data engineering. This presentation will walk you through those challenges, what modern options are available for solving them, and how taking an AI-powered approach to self-service analytics may yield the greatest level of data access along with the best possible performance. Learn more here: https://kyligence.io/

1. AI-Powered Analytics
Saswata Sengupta
Kyligence.io

2. © Kyligence Inc. 2019, Confidential.
Pain Points of Collaboration
Data Engineer
Unable to efficiently fulfill business requirements
Spends large amounts of time on new analytics
requirements
• Manage data sources
• Design data models to maintain a single source of truth
• ETL
Data Analyst
Limited by the number of dimensions and measures in
models – unable to perform complex calculations
Frustrated by slow time to insight
• Develop dashboard/reporting
• Find insights to answer key business questions

3. © Kyligence Inc. 2019, Confidential.
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Analytical Request Process
BI Developer/Architect
Creates new calculations
Analyst/End User
Raises requirements
Requirements finally met
Ready for end user to
conduct analysis
Big Data Architect/Admin
Responsible for partitioning
or indexing
Source systems are constantly changing as analysts raise new requirements.
Systems need to be dynamic and not rely on a chain of approval.
Self-service analytics eliminates this long and inefficient process.
Analytical Requests Are Constantly Changing

4. © Kyligence Inc. 2019, Confidential.
Challenges in Big Data
Title
Massive Amounts of
Data
Lack of Governance
Slow Response Time
Ingesting large amounts of data leads to scaling challenges.
Analysts conducting complex analysis require new calculations, placing the burden on engineering for model changes.
No systematic governance, including schema changes, new measures or analytical functions.
Departments connect to their own data sources.
This can cause definition and calculation conflicts across various departments.
Slow processing times lead to inefficient decision making.
Petabyte-scale datasets combined with many concurrent users often becomes too challenging for many organizations.

5. © Kyligence Inc. 2019, Confidential.
Current Solutions for Big Data Analytics
Native Hadoop Solution
Cloud Data Solutions
Data Virtualization Drawbacks
• Not fast enough – rely on distributed framework without any pre-aggregation
• Support SQL variations and not full ANSI SQL
• Not for business users – require programming knowledge
• Concurrency issues with many users
• Dependent on caching layer for query performance
• Some require dedicated hardware for installation and configuration
• Cannot scale elastically
• Every single query requires federation or pushdown
• Require data to be moved out of the data lake

6. © Kyligence Inc. 2019, Confidential.
The Challenge of Data Silos
• Discrepancies in business definitions
• Steep learning curve
• Duplicated IT development
• Multiple security policies
Data Mart
SQL Server
Analysis Service
Excel
RDBMS
Data Mart
Tableau Server
Tableau Desktop
MPP Database
Data Mart
Cognos Power Cube
Cognos
In-Memory Engine
Data Sources
Cloud DW Parquet ORCBlob Storage CSVSnowflake

7. © Kyligence Inc. 2019, Confidential.
Components of a Modern Data Platform
Scalable & Elastic
Unified Semantic Layer and Governance
Pre-Aggregation and Query Federation
ANSI SQL Compliant
Self-Service

8. © Kyligence Inc. 2019, Confidential.
Apache Kylin
Top Level Apache Project
 The only open-source OLAP on big
data platform
BestOpen-Source Big Data Tool
 InfoWorld’s Bossies (Best of Open Source
Software Awards) in 2015 & 2016
Sub-Second Interactive
Query
 Large scale, high concurrency, multi-
dimensional, sub-second query latency
1,000+ Organizations
 Adopted by thousands of
organizations globally

9. © Kyligence Inc. 2019, Confidential.
Kyligence = Kylin + Intelligence
• Founded in 2016 by the creators of Apache Kylin
• Built around Kylin with augmented AI, enhanced to deliver unprecedented
enterprise analytic performance
• CRN Top-10 big data startups in 2018
• Global Presence: San Jose, Seattle, New York, Shanghai, Beijing
• VCs: Fidelity International, Shunwei Capital, Broadband Capital, Redpoint,
Cisco, Coatue
Accelerate Critical Business Decisions with AI-Augmented Data Management and
Analytics
2016
Founded Pre-A
Redpoint
Cisco
2017
Series A
CBC
Shunwei
2018
Series B
8Roads
2019
Series C
Coatue

10. © Kyligence Inc. 2019, Confidential.
Adopted by 1,000+ Organizations Worldwide

11. © Kyligence Inc. 2019, Confidential.
Kyligence Solution
Automatic Model Creation
AI-augmented engine automatically designs the
most optimal model based on past user
behaviors and query patterns. This reduces the
need for manual modeling and maintenance.
Adaptive Schema Evolution
As analytical requests change, the model needs to
reflect those changes. Our model automatically
adapts to any schema changes. The model evolves
along with your analytical needs.
Automatic Query Optimization
The model continuously evolves and self-
optimizes as it obtains new usage behavior. This
guarantees sub-second performance, no matter
the data volume or concurrency.

12. © Kyligence Inc. 2019, Confidential.
Traditional OLAP vs. Kyligence
• Rigid schema, dependent on data warehouse
• Single node solution
• End-user analytics is limited by the OLAP cube. If the measures and
dimensions do not already exist, the query cannot be answered.
• Adaptive schema
• Distributed multi-node solution
• OLAP cube provides sub-second responses
• Smart pushdown capabilities, guaranteed query responses

13. © Kyligence Inc. 2019, Confidential.
AI-Powered Data Management For Most Valuable Data
ANSI SQL
MDX
REST
Semantic Layer
FinanceMarketing
Sales
Index
AI-Augmented Engine

14. © Kyligence Inc. 2019, Confidential.
AI-Augmented Engine: One-Click Acceleration
• Self-maintaining
• Dynamic auto-modeling
• Self-learning engine
• One-click acceleration
• Adaptive model

15. © Kyligence Inc. 2019, Confidential.
Kyligence Architecture
Data Source
Analytics
Data Service
Data Lake
Azure
Blob Storage
AWS
S3
Hadoop
Google
Cloud Storage
Azure SynapseSnowflake
Management
Query Engine Semantic Layer SQL Query Engine Smart Modeling
Scaling Maintenance Monitor
Enterprise-Level Security
TCO
Database Events Files Logs IoT
Business Insights Multidimensional Analysis 3rd-Party Applications Machine Learning
Visualization Self-Service Collaboration 3rd-Party BI Tools

16. © Kyligence Inc. 2019, Confidential.
DEMO

17. © Kyligence Inc. 2019, Confidential.
Title
Originally built to replace Teradata 3 trillion rows of detail
100,000 concurrent users on
hand-held devices
With milli-second responses
Replaced Teradata
IBM Cognos replacement
From 1,200+ cubes down
to 2 cubes
Complete replacement of Greenplum
eBay Global Top
3 Bank
Top Global
Insurance
Company
World’s Largest
Credit Card
Processor
Title

18. © Kyligence Inc. 2019, Confidential.
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