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Mainframe Modernization with Precisely and Microsoft Azure

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Mainframe Modernization with Precisely and Microsoft Azure

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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

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

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Mainframe Modernization with Precisely and Microsoft Azure

  1. 1. Mainframe Modernization with Precisely and Microsoft Azure Vipin Gupta | Sr Engineering Architect, Microsoft Ashwin Ramachandran | Director of Product Management, Data Integration, Precisely
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  12. 12. Customer story
  13. 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. 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
  15. 15. Demo
  16. 16. Q&A
  17. 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

Notas del editor

  • 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

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