Mainframe and IBM i data continues to be prevalent in several industries including financial services, insurance, and retail where critical customer information lives on legacy systems. In fact, in 2019 alone, studies show that there was a 55% increase in transaction volumes on the mainframe across all industries. To thrive in highly competitive markets, you must quickly break down legacy data silos to swiftly gain a full picture of data for insights for strategic action. Traditional storage solutions that are mainframe proprietary struggle to scale for high data volumes and real-time analytics use cases. This results in increased costs, diminished performance, and missed SLAs. To solve this, Precisely and Databricks provide a modern approach for organizations to optimize volumes of data by leveraging the massive scalability of the cloud to power high-performance analytics, AI, and machine learning, regardless of where data lives. In this webinar, we discuss: - Quickly ingesting data from on-premises sources – such as mainframe and IBM i – to the cloud with the Databricks Unified Data Analytics Platform and Delta Lake - Modernizing ETL processes and reduce development costs with visual data pipelines that uses the elastic scalability of Databricks - Empowering business users with the most up to date data by populating Delta Lake with realtime data changes from legacy systems View this webinar on-demand to see a live demo of the joint solution and how it can modernize your legacy infrastructure

1. Liberate legacy data sources
Precisely and Databricks
Prasad Kona | Partner Solutions Architect | Databricks
Ashwin Ramachandran | Senior Product Manager | Precisely

2. What we will discuss today
• Next generation innovation and legacy sources
• Precisely and Databricks Overview
• Innovation in action - demos!
2

3. 2021
Organizations who
started a hybrid-cloud
approach in the last
year
3
“Next”-generation tech is really “this”-generation
Organizations that will
have a machine
learning component of
their business
Organizations will
have real-time data
delivery
2020 2023

4. Legacy sources
cannot be
left behind
of executives say their customer-
facing applications are completely
or very reliant on mainframe and
IBM i processing.
Forrester Consulting, 2019
55%
Your traditional systems
– including mainframes, IBM i
servers & data warehouses –
adapt and deliver increasing
value with each new technology
wave
72%
increase in transaction volume
on mainframe environments in
2019
BMC 2019
$1.65trillion
invested by enterprise IT
to support data warehouse &
analytics workloads over the past
decade
Wikibon “10-Year Worldwide Enterprise IT Spending 2008-2017”
4

5. The global leader in data integrity
Trust your data. Build your possibilities.
Our data integrity software and data enrichment products
deliver accuracy and consistency to power confident
business decisions.
Brands you trust, trust us
Data leaders partner with us
of the Fortune 100
90
Customers in more than
100
2,000
employees
customers
12,000
countries
“Precisely is able to understand and
engage with enterprise customers where
they are, and assist them in their journey
to where they want to be, which sets this
company apart from its competition.”
Stewart Bond & Carl Olofson
IDC
5

6. Connect today’s
infrastructure with
tomorrow’s technology
to unlock the potential of
all your enterprise data
Integrate
Understand your data and
ensure it is accurate,
consistent and complete
for confident
business decisions
Verify
Analyze location data for
enhanced and
actionable business
insights that drive
superior outcomes
Locate
Power enhanced decision
making with expertly
curated, up-to-date
business, location, and
consumer data
Enrich
The four elements of data integrity
D A T A I N T E G R I T Y
6

7. Get data from legacy sources into the data
lakehouse!
Want your data from legacy, mainframe and IBM i is loaded as-is to the cloud?
• Easily create an exact bit-for-bit copy of
mainframe and IBM i data on the Cloud
• Work with that data in Spark – native Spark
integration
• Map data directly to copybook in Spark
• End-to-end managed approach for
offloading data
• Directly access and understand VSAM,
mainframe fixed and variable files, and
Db2 data
• Take a design once, deploy anywhere
approach to data integration
• Transform data on the fly – no staging
• Import hundreds or Db2 tables to your
data lakehouse with a few mouse clicks
• High-performance, self-tuning sorts,
joins, aggregation, merges, and look-
ups optimized to run natively in the
Databricks run-time
• Dynamically optimize loads for extreme
data volumes
• Break down data silos in minutes
7

8. ▪ Global company with over 5,000 customers and 450+ partners
▪ Original creators of popular yib data and machine learning open source projects
A unified data analytics platform for accelerating innovation across
data engineering, data science, and analytics

9. Unlocking business value: Four
challenges
Data is messy,
siloed and slow
Lack of enterprise
readiness
ML is hard,
Production is
harder
Data Scientists
in Business
Data
Engineers
in IT
BI is limited to a
fraction of data
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001000010111000100101
010000111100101010011
111100111001110101000
111001100011000110001
000100010000101110001
001010100001111001010
Fragmented
security
Poor
reliability
Disjointed
governance
1 2 3 4
Warehouses
Streams
Lakes

10. Make all your data ready for analytics and
ML
Data is messy,
siloed and slow
Your Existing Data Lake
Open High Quality Fast
BI
Reporting
Machine
Learning
Azure Data Lake
Storage
Amazon S3
Unified Data Service
Build open, reliable, fast data lakes with all your data
Unified
Engine
1
Warehouses
Streams
Lakes
Business Data
Big Data
Applications

11. ML is hard,
production is
harder
Data Science and ML Workspace2
Unify data and ML across the full lifecycle
Tracking
Experimental Staging
DeploymentBuilding Models
Databricks Runtime
for ML
Data
Standardize ML lifecycle from experimentation to production
Parameters Metrics
Models
ProductionBusiness and
Big Data
Data Scientists
in Business
Data
Engineers
in IT

12. BI is limited to a
fraction of data
BI Integrations for data lake3
Enable analytics directly on all your source
data
Reports Dashboards Ad hoc data science
Applications Files
All your data with high quality and great performance
Data stores
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000010111000100101010000
111100101010011111100111
001110101000111001100011
000110001000100010000101
110001001010100001111001
010

13. Lack of enterprise
readiness
Databricks Enterprise Cloud Service4
Leverage cloud native platform for
enterprise grade solution
Your
cloud
account
Your
identity
provider DATA
SCIENTISTS
ML
ENGINEERS
DATA
ANALYSTS
DATA
ENGINEERS
Highly reliable, secure
managed service
Azure
Data Lake
Storage
Amazon
S3
All
your
data
1000’s
of
users
Fragmented security
Poor reliability
Disjointed governance

14. Data science, ML, and
analytics on one cloud
platform
BIG DATA & BUSINESS DATA
DATA SCIENTISTS ML ENGINEERS DATA ANALYSTSDATA ENGINEERS
ENTERPRISE CLOUD SERVICE
A simple, scalable, and secure managed service
UNIFIED DATA SERVICE
High quality data with great performance
DATA SCIENCE WORKSPACE
Collaboration across the lifecycle
BI
INTEGRATIONS
Access all your data
UNIFIED DATA ANALYTICS PLATFORMDatabricks Unified Data Analytics
Platform
Access all business and big
data in open data lake
Securely integrates with your
cloud ecosystem

15. Precisely & Databricks: liberate
legacy data
• Experts in liberating data from
legacy data sources
• Build visual streaming data
pipelines
• Modernize ETL processes and
scale with high-performance
engine
• Capture changes to data in
real-time
• 10-100x faster than Open
Source Spark with Delta as the
core engine for PB scale
processing
• Lowest TCO through auto-
scaling and auto-configuration
capabilities
• Unified, collaborative
experience for data engineers
& data scientists on one
platform
15

16. Connect and Databricks
Mainframe
,
IBM i
Relational
databases,
EDW, DBMS
Flat files,
XML, JSON
Ingest
&
Stream
Data
Integrate,
Prepare,
Load,
Cleanse,
Transform
Unified Data Analytics Platform
Reporting and BI
Deliver
Data
Data Sources
Hadoop,
HDFS
Connect’s ETL capabilities
and Databricks eliminate data
silos across your business
16

17. Demo:
Legacy data to Databricks

18. Questions?