When your enterprise has data silos, expanding data volumes and incompatible data formats, you risk missing critical elements in your analytics, AI, and ML projects. The success of these projects relies on complete and accurate views of all enterprise data. Learn how the Syncsort Connect product family helps businesses unlock mainframe data for use within Databricks. Key takeaways from this webinar are: • How Syncsort Connect builds links between the mainframe and Databricks • Applications of mainframe data for advanced analytics and artificial intelligence within Databricks • How to best scale ETL processes for the Databricks ecosystem

1. Accelerate Innovation with
Databricks and Your Mainframe Data
A Syncsort and Databricks Partnership Overview
Ashwin Ramachandran

2. 2
Data is the hardest part of AI and ML projects
Data is Incomplete
Disparate and unreliable
data - major contributing
factors of ML and AI project
failures
Data is Trapped
Inconsistent adoption of
various integration tools
and third-party embedded
integration utilities
Data is Complex
Legacy data is not readily
compatible with
frameworks and platforms
for AI and ML

3. 3
Complete Data shows full
business picture
• Look at ways to simplify the creation of real-time analytical
applications by cleansing, pre-processing and transforming
data in motion
• Consider your current data integration architecture, was it
built to support timely data delivery from a variety of sources
• Strategies in place for data delivery of mainframe and IBM i
data to business applications and analytics
• What are you lacking for a complete data picture – skills, tools,
or time?
Improve business decision making the Databricks Unified
Analytics Platform accessible to all enterprise data

4. 4
Don’t leave legacy data in a silo
• Consider the speed in which you need you are able to
unlock legacy data for use in AI and ML projects
• Determine how you will directly access and understand
mainframe data
• How will you optimize loads for extreme data volumes?
• Do you have legacy data expertise in-house or will you
have to seek external resources to access data?
Break down data silos by improving mainframe
accessibility for AI and ML projects

5. 5
Consider future-proofing
environments
• Look for solutions that insulate your organization against
the underlying complexities of your technology stack
• Consider that data delivery requirements for AI and ML
may break current data integration methods
• Select solutions that guarantee data delivery and have
reliable transfer of information
• Assess how your overall cloud strategy can support real-
time data delivery to next wave platforms
Eliminate lock-in to cloud vendors and legacy technology

6. 6

7. • The market experts in Market experts in legacy data of all types
• 50+ years of mainframe expertise
• High-performance connectivity to Databricks Unified Data Analytics Platform
• Break down data silos, connect mainframe investments to Databricks Unified Data Analytics
Platform in minutes
• New connections with minimal effort with design once, deploy anywhere approach
• No staging required - access, re-format, and load data directly into Databricks United Analytics
Platform
• Easily move applications from standalone server environments and leverage scalability of elastic
Databricks cluster with no coding
• Future-proof applications for emerging changes to the Databricks Unified Data Analytics Platform,
batch and streaming data
Syncsort Connect Drives
Databricks Project Success
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8. Azure Analysis
Service
Power BIAzure Databricks
(Python, Scala, Spark SQL,
Sparkfl, Spark MI, SparklyR)
Azure Data
Lake Storage
SQL
Azure SQL Data
Warehouse
Mainframe
Ingest Store Prep and Train
PolyBase
Model and Serve
Relational databases and
Enterprise Data
Warehouses (EDW)
Flat files, XML, JSON
Databricks on Azure and Syncsort Connect
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