Transform Your Mainframe with Microsoft Azure
0 likes186 views
The document discusses the challenges of integrating legacy mainframe data with Microsoft Azure for improved cloud performance and data management. Key issues include data complexity, governance, and the need for high-quality data across the cloud lifecycle, while also emphasizing the benefits of leveraging Syncsort's products and services. Solutions such as streaming platforms and change data capture are suggested to enhance real-time data delivery and efficiency.
Transform Your Mainframe with Microsoft Azure
- 1. Transform Your Mainframe with Microsoft Azure A Syncsort and Microsoft Partnership Overview Ashwin Ramachandran
- 2. Data Scaling Data is delivered on a delay causing loss in efficiency and inability to deliver at speed of business Data Bottlenecks Loading large data sets to the cloud can drag down performance and increase time to insight Data Governance Copybooks no longer match data, metadata unreadable, record descriptors and data copies lost Data Complexity Mainframe data is not readily compatible with cloud ecosystems Top Challenges of Legacy Data in Cloud Ecosystems 2
- 3. Data Complexity, ALL Your Data to the Cloud • Consider the speed in which you need you are able to unlock legacy data for use in your cloud environments • Understand how the solutions you are using today handle hierarchical data – are they slowing down processing? • Determine if open-source frameworks will work for your business – mainframe data is not readily compatible with open system data formats in the cloud • Do you have legacy data expertise in-house or will you have to seek external resources to access data? 3 Legacy data can provide a treasure-trove of information that can transform your business when used within cloud ecosystems
- 4. Data Governance Must Cover Entire Data Lifecycle • Data lineage is a foundational element for a data governance strategy for legacy data • Understand how when moving data to the cloud, most tools will unpack, expand, and convert data to readable formats • Determine how you can use existing metadata including copybooks to meet tightening SLAs 4 Moving to the cloud means you must guarantee that high data quality exists throughout the complete lifecycle of the data.
- 5. Data Bottlenecks and Single Points of Failure • Parallelize your data ingest across a distributed compute platform in the cloud • Utilize change data capture to cut down on data volumes and eliminating bottlenecks that may occur in slow, spotty networks 5 A single point of failure and throughput forces performance and reliability of your data that is being connected to the cloud
- 6. Data Scaling for Real-Time Business Needs • Consider that faster data delivery may break current data pipeline structures • Look for solutions that insulate your organization against the underlying complexities of your technology stack • Select solutions that guarantee data delivery and have reliable transfer of information 6 Tracking and detection of data changes needs to be presented to as close to real-time as possible • Look at how streaming platforms such as Kafka on Azure could be leveraged to feed real-time delivery • Assess how your overall cloud strategy can support real-time data delivery
- 7. 7
- 8. Syncsort Connect Product Family 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 including 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 Expertise for Driving Azure Initiatives 8
- 9. 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 Syncsort Connect + Microsoft Azure Ecosystem 9