Today’s businesses are leveraging Microsoft Azure to modernize operations, transform customer experience, and increase profit. However, if the rich data generated by the mainframe applications is missed in the move to the cloud, you miss the mark.
Without the right solutions in place, migrating mainframe data to Microsoft Azure is expensive, time-consuming, and reliant on highly specialized skillsets. Precisely Connect can quickly integrate mainframe data at scale into Microsoft Azure without sacrificing functionality, security, or ease of use.
View this on-demand webinar to hear from Microsoft Azure and Precisely data integration experts. You will:
- Learn how to build highly scalable, reliable data pipelines between the mainframe and Microsoft Azure services
- Understand how to make your Microsoft Azure implementation ready for mainframe
- Dive into case studies of businesses that have successfully included mainframe data in their cloud modernization efforts with Precisely and Microsoft Azure
2. The
importance
of legacy
data sources
of executives say their
customer-facing applications
are completely or very reliant
on mainframe processing.
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
$1.65trillion
invested by enterprise IT
to support data warehouse &
analytics workloads over the past
decade
Forrester Consulting, 2019
Wikibon “10-Year Worldwide Enterprise IT Spending 2008-2017”
BMC, 2019
Mainframe Modernization with Precisely and Microsoft Azure
3. Mainframe Modernization with Precisely and Microsoft Azure
Mainframe data helps to enhance a variety of
projects
Improved BI and
analytics
Data visualization Modernization Next-gen projects
– AI and ML
4. Mainframe Modernization with Precisely and Microsoft Azure
Why Data Strategy is P0 (First Priority)?
# of App users 100
App Line of code 20,000
Data size 1 GB
Migration Cost App Vs Data $$$ Vs $
# of App users 500
App Line of code 25,000
Data size 10 GB
Migration Cost App Vs Data $$ Vs $$$
<Now> - 10 Years Now
# of App users 2,500
App Line of code 30,000
Data size 100 GB
Migration Cost App Vs Data $ Vs $$$$
<Now> + 10 Years
Application Data
Application
Data
Application
Data
5. Mainframe Modernization with Precisely and Microsoft Azure
Data Migration Use cases
Schema & Data Migration
One time
1
Data Replication and Sync
For Batch/Near Real Time Sync
2
Change Data Capture
Real Time Sync
3
Source
Target
6. Mainframe Modernization with Precisely and Microsoft Azure
SSMA For Db2
Assessment
To assess the Db2 project and generate conversion report.
Migrate Schema
Data objects(Tables, views, procs etc.) are migrated to SQL platform.
SSIS packages can be generated to execute in Visual Studio/ADF.
Sync with Target Database
Migrated objects are synced with the SQL database platform.
Migrate Data
Data are moved out from Db2 to SQL database platform.
7. Mainframe Modernization with Precisely and Microsoft Azure
Data Integration
DRDA
TCP/IP
TDS
TCP/IP
COBOL
CICS
TSO
PL1
Java
Converts DB2 network protocol and
formats into Microsoft ADO.NET
framework Data Provider for SQL
Server commands and data types
Protocol and format conversion
Allows legacy IBM DB2 client
applications to connect to
Microsoft SQL Server
databases
Host-initiated processing
Enables phased migration from
legacy platforms to Microsoft
Server infrastructure
Workloadmigration
READ
8. Why stream data?
• Power business decision-making with real-time data
• Consistent view of the data across the enterprise and keeping business in sync
• Keep data lakes fresh including transactional systems
• Enable timely reporting and meet tightening SLAs
• Migrate and modernize with zero downtime for database/application upgrades
and system re-platforming
• Support data governance and security requirements
9. Legacy system expertise for driving Azure
initiatives
• Connect offers:
• 50 years+ of Mainframe expertise
• No installation of software on the mainframe needed to restructure mainframe and IBM i data into readable
formats for use with Azure services
— VSAM connections via: FTP, FTPS, Connect:Direct
— Db2/z connections via: ODBC or JDBC
• Leverage existing metadata locked in copybooks to meet tightening SLAs
• Convert packed decimal, zoned decimals to readable formats
• On ingestion, handle COBOL high/low values
• Quickly and easily convert EBCDIC to Unicode
• Handle REDEFINEs in COBOL copybooks
• Ingest OCCURS DEPENDING ON variable length arrays
Mainframe Modernization with Precisely and Microsoft Azure
10. Precisely, your choice for Microsoft Azure
• Build critical links between your legacy systems and Microsoft Azure services, including:
— Cloud data warehouses (Azure SQL Data Warehouse and Snowflake on Azure)
— Compute clusters (Azure Databricks, Azure HDInsight and Hadoop on Azure)
— Azure SQL Database
— Object storage (BLOB, ADLS Gen1 and ADLS Gen2)
— Distributed messaging systems (Kafka on Azure)
• Design once, deploy anywhere approach to data integration architectures
• Move workflows at the click of a button:
— Development to production
— on-premises to cloud
— From one cloud to another
• Microsoft Gold Cloud Platform Partner
Mainframe Modernization with Precisely and Microsoft Azure
11. Connect and Microsoft Azure Ecosystem
Data Lake
Analytics
Azure Analysis
Azure Databricks
(Python, Scala, Spark SQL,
Sparkfl, Spark MI, SparklyR)
Azure Data
Lake Storage
Azure Synapse
Analytics
Mainframe
Ingest Store Prep and Train
PolyBase
Model and Serve
Business/custom
apps(structured)
1 2
4
5
Logs, files, and media
(unstructured)
3
SQL
Mainframe Modernization with Precisely and Microsoft Azure
13. Mainframe Modernization with Precisely and Microsoft Azure
Benefits
1. All historical sales data archived and encrypted securely on
inexpensive cloud infrastructure.
2. Real-time retrieval of all data in business Cloud app for sold cases,
rejects and quotes.
About
GEICO writes private passenger automobile
insurance in all 50 U.S. states and the District of
Columbia. The insurance agency sells policies
through local agents, called GEICO Field
Representatives, over the phone directly to the
consumer via licensed insurance agents, and
through their website.
Problem
• Mainframe costs were skyrocketing, so this
insurance company decided to retire their
mainframes.
• They had sales data going back to 1998. 97
terabytes from an IMS database.
• Much of the data was on virtual tape,
unreadable by most ETL software
• This single dataset had 40 different record types,
each of which had 6 – 10 copybooks. About 350
copybooks all together.
Solution
Precisely Connect
Microsoft Azure
14. • One year of sales data available to key business apps stored on
expensive DASD storage.
• 100 TB of historical data stored on unreadable, inaccessible
virtual tape.
• No access of key business applications to historical data.
• Precisely Connect used over 300 copybooks to translate
mainframe variable data into human readable text files
• Microsoft Azure Data Import Service put all 100 TB in Cloud
• Key business applications moved to the Cloud.
• All sales data encrypted securely in the Cloud.
• Business has instant access to all 100 TB of data since 1998.
Mainframe Modernization with Precisely and Microsoft Azure
Before
Current data on expensive mainframe DASD.
Older data on inaccessible virtual tape.
After
with Precisely Connect & Azure
Cloud App
Checks sold cases, rejects
and quotes.
Instant access to all data.
Virtual Tape
18 Years of
Sales Data
Mainframe
1 Year of Sales Data
NO
ACCESS
Mainframe App
Checks sold cases,
rejects and quotes.
Geico Insurance archives data to Azure Cloud
Mainframe Modernization with Precisely and Microsoft Azure
17. Mainframe Modernization with Precisely and Microsoft Azure
Resources
Azure Migration
• https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/mainframe-azure-file-
replication
• Azure Database Migration Guides | Microsoft Docs
Precisely Connect
• https://www.precisely.com/solution/microsoft-azure
• https://www.precisely.com/resource-center/productsheets/transform-mainframe-investments-with-
precisely-connect-and-microsoft
Editor's Notes
Most large enterprises have made major investments in data environments over a period of many years - legacy data can provide a treasure-trove of information that can transform your business when leveraged via a streaming paradigm
These environments contain the data that these business run on and that today power the strategic initiatives driving the business forward – machine learning, AI and predictive analytics
Legacy platforms (mainframe and IBM i) continue to adapt with each new wave of technology and are not going away anytime soon
Integrating legacy data into your projects brings several advantages such as:
Connect applications together, leveraging the existing transactional capabilities of the current application platform, and the wealth of new capabilities of the cloud
Feed analytics with up-to-date information so your business runs on current insight
Port workloads to less-expensive, strategic platforms
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices
Centralized business insights – central management of business insights, helps to shift insights from one offs in isolation to
Data discovery - business end-users can work with large data sets and get answers to questions they are asking.
Data Discovery is helping the enterprise lose some of the bulk when it comes to running analytics.
Data democratization – enables more users to have autonomy with data but without the risk of exposing sensitive data in a way that could violate regulations or internal best practices