The document outlines strategies for transforming mainframe data for the cloud using Apache Kafka, focusing on application transformation, cloud connections, and real-time data pipelines. Key ingredients for successful migration include log-based data capture, flexibility in replication options, and resilient data delivery. A case study highlights how an Australian financial services company improved client engagement by modernizing its transaction monitoring capabilities through real-time insights and Kafka integration.

1. Transform Your
Mainframe Data for the
Cloud with Precisely
and Apache Kafka

2. Agenda
• Beginning your cloud transformation
journey
• Connecting the mainframe to the cloud
• Ingredients for success of mainframe to the
cloud
• Stark Denmark, a story of transformation
with mainframe, Apache Kafka and the
cloud

3. Companies typically waste
an average of 35% of their
cloud budget on
inefficiencies.
PwC, 2021

4. Beginning
your cloud
transformation
journey
Focus on
application
transformation first
Consider
containerization
for hybrid and
multi-cloud
deployments
Understand on-
prem data
pipelines
Determine what
on-prem solutions
need to open to
cloud

5. Connecting mainframe
to the cloud
Bring rich transaction data to
the cloud
Improve cloud analytics and
insights
Speed delivery of information
Scale with next-generation
initiatives

6. Ingredients for success of mainframe
to the cloud

7. Ingredient 1: Log-based data capture
Did you know?
• Connect CDC can leverage
published Log or Journal
standards to identify and
capture the change before
copying to the Share Queue.
• The Connect CDC Queue
ensures that data integrity is
maintained and zero data loss
occurs in the event of a dropped
connection during file
transmission.
1
1
2
3
4
Changed data
Source DBMS
Change selector
Log/Journal Queue Retrieve/Transform/Send
Apply
1. Use of transaction logs or triggers eliminates the
need for invasive actions on the DBMS
2. Selective extracts from the logs and a defined
queue space ensures data integrity
3. Transformation in many cases can be done off
box to reduce impact to production
4. The apply process returns acknowledgement to
queue to complete pseudo two-phase commit
Target DBMS

8. Ingredient 2: Real-time data
pipelines
• Stream real-time application data from relational database, such as
DB2 for IBM i to mission critical business applications and analytics
platforms
• Other systems examples: mainframes, EDWs
• Business application examples: fraud detection, hotel reservations,
mobile banking, etc.

9. Ingredient 3: Flexible replication options
One Way Two Way
Cascade
Bi-Directional
Distribute
Consolidate

10. RDBMS
EDWs
Data Streams
Strategic Projects:
Real-time analytics, AI
and machine learning
Targets
Connect CDC is
cloud platform
enabled for
Ingest and Stream
Ingredient 4:
Precisely Connect
Other
Db2 (IBM i,
z, LUW)
Sybase
Oracle Informix PostgreSQL
MySQL
MS SQL Server
Flat Files
(delimited)
Mainframe
IMS Db2 for z/OS
VSAM
Precisely Connect

11. Scale to meet the needs of
your business
Self-service data integration through browser-based interface
• Design, deploy and monitor real-time change replication from a variety of traditional
systems (mainframe, IMS, RDBMS, EDW) to next-generation distributed streaming
platforms like Apache Kafka
• Enable the construction of real-time data pipelines
Resilient data delivery
• Fault tolerant – resilient to network/source/target/application server outages
• Protects against loss of data if a connection is temporarily lost
• Keeps track of exactly where data transfer left off and automatically restarts at that
exact point – no manual intervention
• No missing or duplicate data!
Maintain metadata integrity
• Integrates with Kafka schema registry
• On-demand metadata-driven pulls of data from a variety of database systems to next
generation data stores like the cloud and cluster

12. Customer Story
• Connect leverages transactional CICS events (committed changes)
within the mainframe, replicating the changes to Latitude’s
Confluent-based Kafka event bus
• Connect performs one-way, resilient replication to Confluent Kafka,
maintaining transactional integrity and ensuring proper data
delivery
• Simplified data transformation, COBOL copybook mapping,
REDEFINE handling, and more so that data is intelligible in Kafka
• Improved customer engagement and clearer insight into client
behavior
About
Australian financial services company with
headquarters in Melbourne, Victoria. Core business
is in consumer finance through a variety of services
including unsecured personal loans, credit cards,
car loans, personal insurance and interest free
retail finance. It is the biggest non-bank lender of
consumer credit in Australia.
Problem
Sought to modernize their transaction monitoring
capabilities in order to improve their client
engagement. This goal required gaining real-time
insight into client behavior, so that they could
provide alerting, notifications, offers, and reminders
as events happened. Unlocking this data from
mainframe VSAM was a critical step to achieving
success.
Solution
Precisely Connect
Confluent Kafka

13. Questions