In January of this year, Kyligence announced the immediate availability of Kyligence Cloud 4, the first fully cloud-native, distributed OLAP platform. During our announcement, EMA analyst John Santaferraro said: “As the race for unified analytics heats up, Kyligence offers a solution that overcomes the challenges of querying data in both data lakes and data warehouses located both in the cloud and on premises.” Join Li Kang - VP of North America at Kyligence - as he provides an overview of the Kyligence Cloud 4 release that will show: --The new cloud native architecture that employs Apache Kylin, Apache Spark, and Apache Parquet to ensure optimal performance. --How KC4 delivers sub-second query responses on very large datasets using precomputed aggregate indexes (hyper-cubes) and table indexes. --The AI-Augmented engine that intelligently organizes your data and reduces data modeling time from days/weeks to minutes. In this presentation, we will present the Kyligence Cloud 4 story - high-speed analytics with unprecedented sub-second query response times against petabyte datasets.

1. Kyligence Cloud 4 – An Overview
George Demarest
Head of Marketing
george.demarest@kyligence.io
Li Kang
VP, North America
li.kang@kyligence.io

2. Agenda
 Introduction to Kyligence
 Announcement: Kyligence Cloud 4
 Kyligence Innovations
 Use Cases and Successes
 Demonstration

3. © Kyligence Inc. 2021, Confidential.
Three Pillars of Modern Cloud Analytics
Distributed Compute Columnar Storage Precomputation

4. © Kyligence Inc. 2021, Confidential.
MPP Queries – Slow and Costly in the Cloud
Common to Greenplum, Snowflake, Synapse, Redshift
Data Volume /
$$$
Resource
Consumption
Runtime Computation
MPP Engines : Runtime Computation
Data
Concurrency

5. © Kyligence Inc. 2021, Confidential.
OLAP Precomputation - Faster and Cheaper in the Cloud
Q: How many laptops were sold in Los Angeles in June?
An Aggregate Index (a.k.a. Cube)
Precomputation – Compute Once, Query Many Times
Data
Data Volume /
$$$
Resource
Consumption
Runtime Computation
Concurrency
Precomputation

6. © Kyligence Inc. 2021, Confidential.
Apache Kylin
Top Level Project
 The only open-source OLAP
technology on big data platform
AwardWinning
 InfoWorld’s Bossies 2015 & 2016
(Best of Open-Source Software Awards)
Sub-Second Interactive Query
 Large scale, high concurrency, multi-
dimensional, sub-second query latency
1,000+ Organizations
 Adopted by thousands of
organizations globally

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Kyligence: Kylin + Intelligence
• Founded in 2016 by the creators of Apache Kylin
• Enhances Kylin with AI-assisted auto-modeling, Unified Semantic
Layer for leading BI tools
• 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
2016
Pre-A
Redpoint
Cisco
2017
Series A
CBC
Shunwei
2018
Series B
8Roads
2019
Series C
Coatue

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Kyligence in Action
Migrated 1,200+ Cognos
cubes to 2 Kyligence cubes
360º real-time view of spend
insights for finance teams
Migrated 2nd largest SSAS
deployment to Kyligence/Azure
Top 10 Global
Investment Bank
US FinTech
SaaS Provider
World’s Largest
Credit/Debit Card Network
Results:
 95% queries < 2s
 100+ concurrent users
Results:
 500X improved efficiency
 10X faster + more stable analytics
Results:
 Single, 100+ TB distributed
Kyligence cube

9. © Kyligence Inc. 2021, Confidential.
Trusted by Global Fortune 500
Banking & Finance
Telecom
Technology
CPG & Retail

10. © Kyligence Inc. 2021, Confidential.
Announced Jan. 21
Kyligence Cloud 4
The First Cloud-Native OLAP Platform
Cloud-native architecture Elasticity, separate compute from storage
Cloud immediacy Deploys in minutes, on AWS/Azure marketplace
Enhanced AI engine Auto-modeling, self-tuning cubes/indexes
Unified Semantic Model MDX, SQL, REST access transparent to BI users

11. © Kyligence Inc. 2021, Confidential.
Kyligence Cloud Architecture
Unified Semantic Layer
Data as a Service
Machine Learning
Data as a Service
SaaS & Apps
CRM HCM
SCM
Low Latency Queries
Any BI Tool
Any Cloud
Any Data Platform
Data Warehouse
Streaming Data
Data Lake
AI Augmented Engine
Precomputation Layer
Distributed Cubes
& Table Indexes
Multi-dimensional
Modeling
Security &
Governance
Finance
Marketing
Sales

12. © Kyligence Inc. 2021, Confidential.
Cloud Native Architecture
Separation of compute and
storage
• Spark compute cluster
• Shared cloud object storage
• Scale and optimize separately for best
results and price performance
• Continuity: build cluster separate from – and
doesn’t disrupt – query cluster
Removed legacy Hadoop requirement
• MapReduce, HDFS, HBase
Spark Cubing Persisted in Parquet format,
Optimized for Cloud Storage
High performance components
• Cubing with Spark, stored as Parquet
• All tasks are executed in memory with Spark
- cubing, querying, modeling, aggregation
• Cubes and indexes saved in Parquet

13. © Kyligence Inc. 2021, Confidential.
Precomputing Everything Isn’t Practical
Precomputation
Total Indexes = 2 ^ Number of dimensions - 1

14. © Kyligence Inc. 2021, Confidential.
Precomputing Everything Isn’t Practical
Challenge
• 50 dimensions => 1.2T Indexes
• Today, 100+ dimensions are possible
• Cost is always critical
Solution
• AI-brokered compromise

15. © Kyligence Inc. 2021, Confidential.
AI-Assisted Cubing and Auto-Modeling
Model Training
Pre-aggregation
Data Processing
Auto-Generate
Cube & Index
AI-Augmented
Engine
Self-training
Group wisdom
Queries across the org
ingested as input
Benefits
- Continuous model improvement without
human intervention
- Greater user satisfaction

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Intelligent Query Routing
With Smart Pushdown™
Goal:
• Cubes: maximize query hits on
precomputed results
• Table Indexes: precompute
detailed/ad-hoc queries if possible
• Intelligent pushdown if not
Total Freedom for BI users to slice
and dice, drill down, roll up, etc.
Seconds to Minutes
Smart Pushdown™
Kyligence
millions of queries/day

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Unified Semantic Layer
 ​From technical details to
business contexts
 Consistent semantics across
all major BI tools
 Centralized access control
 Advanced business
definitions and logic

18. © Kyligence Inc. 2021, Confidential.
Case Study: UnionPay – Modernize Traditional OLAP
World’s largest debit/credit card issuer
Results
 500x improvement in maintenance efficiency
 10x faster with greater stability
 Time to insight: less than 4 hours
 Significant TCO reduction
Solution
 Replaced Cognos backend with Kyligence
 Consolidated 1,200 cubes to 2
 Retained Cognos reporting frontend
Challenge
 1,200+ existing Cognos cubes to manage
 1,000+ ETL jobs to maintain
 Time to insight: over 4 days
Backend migrated to
2 Kyligence cubes

19. © Kyligence Inc. 2021, Confidential.
Case Study: UnionPay – Modernize Traditional OLAP
World’s largest debit/credit card issuer
Blog
eBook

20. © Kyligence Inc. 2021, Confidential.
Case Study: US Wealth Management – OLAP Cloud Migration
 Challenges
 Microsoft SSAS reaching practical limits
 Many TB OLAP Cube, 350B rows
 Query latencies unacceptable for dashboards
 MDX queries 20 secs – 60+ secs
 SQL queries 5 secs – 10+ secs
 Hampered analyst productivity
 Slow load times
 Duplicate/derived cubes: duplicate effort
 Solution
 Redeploy to Azure/Kyligence
 Same data sources, same BI tools
 80+% query latencies < 1 sec

21. © Kyligence Inc. 2021, Confidential.
Case Study: US AI Fintech Vendor – Self-Service Cloud Analytics
 SaaS vendors sit on top of a large
amount of transaction data
 Provide business insights and industry
benchmarks as an add-on product
subscription
 Challenges are performance,
concurrency, and cost
 Fixed cloud infrastructure cost while
# of users/queries grow
 Not possible with Cloud DWs

22. © Kyligence Inc. 2021, Confidential.
Use Case – Cloud Data Warehouse Enhancements
 Improved query performances
 Cost reduction by 10x
 Support 100s of users is very expensive on Snowflake
 Every query / compute causes charges
 Excel pivot table support
 Target prospects
 Snowflake customers
 Large dataset and/or large # of users
 Prefer to use Excel pivot table

23. Demo
Demo

24. © Kyligence Inc. 2021, Confidential.
Kyligence Cloud 4
The First Cloud-Native OLAP Platform
Cloud-native architecture Elasticity, separate compute from storage
Cloud immediacy Deploys in minutes, on AWS/Azure marketplace
Enhanced AI engine Auto-modeling, self-tuning cubes/indexes
Unified Semantic Model MDX, SQL, REST access transparent to BI users

25. © Kyligence Inc. 2021, Confidential.
Contact Us
Kyligence Inc
 http://kyligence.io
 info@kyligence.io
 Twitter: @Kyligence
Apache Kylin
 http://kylin.apache.org
 dev@kylin.apache.org
 Twitter: @ApacheKylin

26. © Kyligence Inc. 2021, Confidential.

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Solution: Kyligence Pivot to Snowflake

28. © Kyligence Inc. 2021, Confidential.
Kyligence Pivot to Snowflake Architecture
• Leverage MDX for semantic consistency
• Seamless user experience
• Zero client configuration
• Calculate once, consume many
• Supports AWS and Azure
Database Events Files IoT
Unified Semantic Layer
MDX
Engine
Access
Control
Enterprise
Security
Query
Pushdown