The document discusses the need for a next-generation data architecture that supports hyper-personalization and efficient data integration across various data types and sources. It emphasizes challenges with legacy systems, including data silos and cloud lock-in, and presents operational data warehouses as a solution with capabilities for real-time analytics, performance at scale, and ease of use across different user personas. The architecture aims to enhance agility, reduce total cost of ownership, and facilitate advanced analytics while ensuring security and compliance.

1. Raghu Chakravarthi
Uncovering the next generation of Data Architecture
Insights at the speed of thought

2. Customer 360
Risk Analytics
Hyper Personalization
Modern use cases need a cross section of data
Behavioral Data
Social Data
Transactional
Data

3. Customer Journey
Hyper Personalization
What do these data demand from data architecture
Behavioral Data
Social Data
Transactional
Data
Quick Access, Analytics
On Prem
Fast inserts, updates
Hybrid
Heavy inserts, Realtime
Cloud
Realtime updates, Fast ingestion, Quick Access,
Analytics where data lives
Hybrid Cloud

4. Key trends in the data and analytics
Managed
Performance at scale
Agility
Best TCO
Edge
Hybrid
Key Trends

5. Legacy enterprise databases hinder business growth
Data siloed across on-
prem & cloud
Enterprises require existing on-prem
investments to be leveraged
Hard to move data into the cloud
Cloud lock-in
Performance slows with
concurrency
Designed to perform at TB scale not
PB requiring additional compute to
handle concurrency
While pricing starts low, production
workloads at scale are expensive
Pricing scales up
too quickly
© 2019 Actian Corporation5
Vs.

6. We need something better …
Data
Integration
Performance to PB
scale
Pay for what
you use
Easy to
manage
© 2019 Actian Corporation6

7. Introducing Operational Data Warehouse
Usability
Performance at scale
Agility
Best TCO
Edge Hybrid
Operational Data
Warehouse

8. These have become Table Stakes
Compute
Storage
Separation
Columnar
Store
Data
Variety
Fast Data
Ingestion
On Prem
& Cloud
Elasticity
Hot, Warm,
Cold Data
Separation

9. Analytics to
Data
Parallel Updates Federated
Query
Persona
based access
Data Labs
Data Agility from access to analytics

10. User experience improves Productivity and Collaboration
Usability
SQL, Py, R, Scala, Java
Data Cleansing
Tokenizing
Feature Prep
Atomic Data
Aggregated Data
User Data
Pipelines
Access Rules
Annotations
Sandbox
Versioning

11. Data lives at the Edge
Zero ETL SQL & API Access
Zero DBA Embeddable
Secure Time Series

12. Performance at scale…its in the engines
• Vector Processing
• Leverage Chip Cache
• Realtime Trickle Updates
• Automatic Indexing
• Smart Compression

13. Total Cost of Ownership... Its about doing more with less
Linear for Data
Size growth
Cost to
performance
$/workload
Linear for
Concurrency Growth
Integrates
with current
ecosystem
Best
TCO
50 100 200 500 1000
$perconcurrent
workload
Count of Workloads
Cost Comparison

14. Lets not forget the Personas
Data
Scientist
Data
Engineer
Business
Analyst
Atomic Data
R, Python, Scala, SQL
Data Prep Functions
Feature Engineering Functions
Jupyter Hub, R Studio, SAS
Atomic and Aggregated Data
Python, Java, Scala
Data Manipulation Functions
Custom Functions
RESTful, Anaconda, deplyR
Aggregated, UI Data
SQL, Drag and Drop GUI
Data Visualization Functions
In Database Functions
Alteryx, Dataiku, Looker, Qlik

15. Operational Data Warehouse Architecture
Operational
Data Warehouse
Persistent
Storage
Cross-Engine
Orchestration
SQL Access
Studio, BI and Visualization
tools
Hadoop
S3
ADLS
Local Storage
Cloud Storage
Languages
SQL, Javascript, Python, R,
Java, Scala
Tools
Jupyter, RStudio, KNIME,
SAS, Dataiku
Column
Engine
NoSQL EngineRow Engine
Spark
Engine
Custom
Engine
Deep
Learning
Engine
Gluster
GFS
Data Fabric
Ingestion
Spark, Kafka, ETL
Data Scientist
Data Engineer
Business
Analyst

16. Actian Avalanche Cloud Data Warehouse
Business Intelligence
Advanced
Analytics
Artificial Intelligence
& Machine Learning
External data source ingestion
On Premise Apps/EDWsNative Cloud Apps & DataMobile & IoT
Batch (e.g. JDBC/ODBC)Streaming (e.g. Kafka & Spark)
Avalanche FlexPath™ Augmented Data Management
Performance Security Hybrid ExtensibilityScale
• Vectorization
• Adv. Columnar
• In-chip processing
• Automated Storage
Indexing
• Multi-core parrallization
• Real-time updates
• Petabyte Scale
• High Concurrency
• Extreme Transaction
Processing
• Analyze all data – no
sampling
• Authentication
• Discretionary Access
control
• Security auditing
• Auditing & GDPR
compliance
• End-to-end encryption
• Data masking
• Cloud & On-premise
• 100% functional
compatible
• Load and go exchange
between query workloads
• Multi-cloud support
• Open architecture
• Premise & SaaS
Connectors
• External Table
• Native Spark
• Access to any data
source
On-Demand Encrypted Self-tuning Autonomic Standards based
ActianAvalancheQuickStart
ActianAvalancheMigrationQuickStart
ActianAvalancheConnectorQuickStart
Authentication.
Service
Provisioning
Service
Management
Service
Inform
Service
Metering
Service
Monitoring
Service
Avalanche
QuickStart
Services

17. Introduction to Actian
Performance
at Scale
Hybrid by Design
Spectacular Operational
Savings
© 2019 Actian Corporation17
Fully Managed with
Pre-built
Connectors
Cloud Data Warehouse
www.actian.com/avalancheFree 30 day trial with $500 credit
Visit us at Booth 1238

18. @rchakravarthi1 linkedin.com/in/raghuc