Most data-driven enterprises continue to struggle to generate the insights they need from their data. More data volumes from more data sources, combined with escalating user concurrency, have led to declining query throughput performance and skyrocketing data warehouse costs. Moreover, modern use cases such as customer-360 and hyper-personalization have blurred the boundaries between operational and analytics systems, making even greater demands on data warehouse solutions.

1. Attributes of a
Modern Data Warehouse
Jack Mardack, VP Actian
Gartner Catalyst
August 12-15, 2019

2. Why does this matter?
Well, it’s a bit like a milkshake…

3. Feeding the data analytics demands of the enterprise
 Exploding demand on
traditional analytics use
cases.
 Democratization of
access has spawned
many more consumers.
 Rise of bold, new
analytics use cases and
need for real-time.
 Enterprises get the
power (like the big social
players) to leverage
massive amounts of
customer data to drive
large-scale CX
personalization and
marketing.
 Reporting
 Ad hoc Analysis
 Data Mining
Cloud data
warehouse
X
Many more
 Customer-360
 Hyper-personalization
 ML & AI
 Contextual Communications
TRENDS
Demanding
NEW
Use Cases
CONSUMERS
Traditional Analytics
Use Cases

4. Generational Evolution of the Data Warehouse
Appliances
 Multiple clouds support and
3rd party integrations make
data flow easier
 Admin is much easier.
 Storage separation makes cost
more elastic, thus lower.
 Time to value drops to weeks
 Time-to-value drops to < months.
 Much easier to get data in (from S3).
 Compute and storage sold together
(so not elastic).
 Heavy technical setup and
maintenance
 Much more powerful relative
compute power
 Hybrid and multi-cloud coverage
 Built-in integrations make data
flow much easier
 Fast ingestion and extraction
3rd Party
Integrations
Built-in
Integrations
3.
X n
1.
X n
2.
CSVs
SPARK
OLTPOLAP
X n
The Cloud Multi-Cloud Gen III
S3
 Integrated software and hardware gave
great analytics performance vs the
world of before.
 Very expensive to buy more (CAPEX).
 Very expensive administration.
 Hard to get data in and out.
 Low tolerance for concurrent demand.
 Time to value > months.
Data
Sources
Hybrid
AvalancheSnowflakeRedshiftExadata TeradataNetezza

5. The Essential Requirements:
Put it where you need it
 “Play your data where it lives" - bring
high-performance analytics to all
your data sources, wherever they are.​
Higher compute performance ceiling
 Great absolute throughput performance at
scale (petabytes of data + thousands of users +
plus high query complexity).​
Unparalleled unit economics
 Great cost-performance (at scale).
Your rules, over your data
 Your unique corporate governance,
compliance, and security needs can
be met headache-free.​
A pleasure to use
 Easy to setup.
 Easy to scale up and down, so peaks don’t
destroy performance nor break the bank.

6. Avalanche Gen III Cloud Data Warehouse Service
Multi-Cloud & Hybrid
Run on-premise or in the cloud
platform of your choice.
Platform Support
Google Cloud
Platform (Planned)
HDFS Azure ADLS AWS EBS S3 Azure Blob Storage Data Lake
Spark Enabled External TablesNative High Performance
Intelligent Elastic Storage
Horizontally Partitioned DataStorage is Separate & Smart
Not just separate from compute
costs, but smartly used .
Pre-Built Connectors
Plus +200 more
apps supported with
pre-built connectors
App2App
Hybrid Data
All your Data Sources
Connect easily to the data
sources your business runs on.
Industry
Standard SQL
Vector
Processing
Query Resource
Optimization
Federated
Query
Real-time
Updates
Advanced
Columnar
Advanced Cloud Compute
Gen-III Cloud Architecture
Elevates absolute throughput
performance to new levels.
Robust Access Business Analyst Data Scientist Data Engineer
ODBC JDBC .NET Python Spark Kafka
Ecosystem Tools
Give everyone on the team
the access they need.

7. Things to do now
Come say hello at booth #405
Visit actian.com/avalanche
Tweet something you liked from my talk  (@2hp, @actiancorp)

8. Thank you!