The Future of Mainframe Data is in the Cloud
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As organizations make the move to cloud platforms to support modern analytics and applications, they often need to move more than their on-premises data warehouses and lakes. Organizations often have legacy mainframe systems that run their mission-critical and revenue-generating applications, and that should be included in modern cloud projects. Yet it can be difficult to replicate mainframe data onto cloud platforms to make it a part of your data-driven initiatives, due to the data’s complex format. When moving data to the cloud it needs to maintain accuracy, consistency, and context to be considered trusted data. At the same time, the data needs to be available in real time to cloud-based data management and analytics applications. Join this session for a discussion with experts from Precisely and AWS about how to bring your data to applications and services on the cloud. Topics include: The importance of mainframe data for organizationsHow to migrate/replicate mainframe data to cloud platformsHow to ensure trusted data on cloud platforms
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The Future of Mainframe Data is in the Cloud
- 1. The Future of Mainframe Data is in the Cloud Ashwin Ramachandran, VP Product Management Data Integration, Precisely Maggie Li, Principal Engineer, Amazon Web Services
- 2. Housekeeping Webinar Audio • Today’s webcast audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions Welcome • Submit your questions at any time during the presentation using the Q&A box. If we don't get to your question, we will follow-up via email Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides
- 3. 3 Mainframes host the most critical applications 92 Top world banks 10 of the world’s top insurers 23 of the top 25 US retailers $2.5 bn Transactions / day / per MF 71% of the fortune 500 $2.9 bn Mainframe market by 2025
- 4. of organizations will embrace a cloud-first principle by 2025 85% of leaders site data modernization as the reason for their shift to cloud 55% of wasted migration spend is expected over the next three years $100 bn Approximately
- 5. Inefficient migration comes at a heavy cost 5 75% of cloud migration projects are overbudget 38% of cloud migration projects run behind schedule
- 6. © 2023, Amazon Web Services, Inc. or its Affiliates. Need for mainframe modernization Modernization Drivers Mainframe Questions Increase Agility Reduce costs Mitigate risks Complex dependencies Slow release cycles Escalating costs Skills shortage Locked data Vendor lock-in Technical debt Siloed platform Tightly coupled Limited capacity Little innovation Long procurement Outdated interfaces Legacy components Undocumented code Performance? Resiliency? Security? Business case?
- 7. © 2023, Amazon Web Services, Inc. or its Affiliates. “AWS Mainframe Modernization service is a cloud native platform to modernize, migrate, run, and operate mainframe applications.”
- 8. © 2023, Amazon Web Services, Inc. or its Affiliates. Mainframe analytics using AWS Mainframe Modernization Data Replication with Precisely AWS Cloud Corporate data center Data files Transaction manager Batch subsystem Monitoring Scheduler COBOL, PL/I, JCL Mainframe operating system Database Mainframe Data Integration Data Stores Data Analytics Data Capture AWS Mainframe Modernization Data Replication Streaming Processing Data warehouse Data lake Business intelligence Machine learning Interactive queries Real-time search
- 9. © 2023, Amazon Web Services, Inc. or its Affiliates. AWS Cloud Corporate data center Data files Transaction manager Batch subsystem Monitoring Scheduler Applications Mainframe operating system Database Mainframe AWS Mainframe Modernization service Load Balancer API Gateway File storage Web Messages Screens Messages Messaging Managed runtime Refactored application Screens Managed runtime Replatformed application Data Replication New functions New channels Relational database NoSQL database Mobile Large migration leveraging AWS Mainframe Modernization Data Replication with Precisely
- 10. © 2023, Amazon Web Services, Inc. or its Affiliates. New functions and channels using AWS Mainframe Modernization Data Replication with Precisely AWS Cloud Corporate data center Data files Transaction manager Batch subsystem Monitoring Scheduler COBOL, PL/I, JCL Mainframe operating system Database Mainframe Data Integration Data Stores New functions Data Capture AWS Mainframe Modernization Data Replication Streaming Messaging Relational database NoSQL database Web applications Functions APIs Mobile Real-time replication Codepage conversion Parallel streams
- 11. © 2023, Amazon Web Services, Inc. or its Affiliates. AWS and Precisely help drive customer success - Refactor - Replatform - Augment - Managed runtimes Cloud-native fully-managed aws Mainframe Modernization service aws professional services Delivery Kits aws Mainframe Modernization Methodology AWS Assess – Mobilize – Migrate & Modernize AWS Mainframe MAP (Migration Acceleration Program) aws professional services delivery practices AWS mainframe-dedicated organizations Orgs: Sales, Tech Sales, Partner, Product, ProServe Sectors: Commercial, Public sector, FSI Geos: NAMER, LATAM, EMEA, APJ Publications Service documentation, AWS Prescriptive Guidance articles, Blog posts aws training & certification mainframe curriculum Modernize mainframe applications aws Competency 6 Software partners aws Competency 11 Services partners aws partner network 62+ technology partners aws partner network delivery practices 57+ offerings aws well-architected AWS Compliance shared responsibility
- 12. OBJECTIVE One of the nation’s oldest financial institutions with $188.4 billion in assets offers a broad range of retail and commercial banking products and services. They are known to offer tailored advice, ideas, and solutions based on their customers needs. Looking to modernize their mainframe while also improving customer satisfaction and the overall “digital bank” experience. CHALLENGES • Struggling to deliver consistent, accurate data to customers across channels leading to customer churn • Missed opportunities to sell additional services resulting in lost revenue • $1 million in costs to provide real- time data from the mainframe BENEFITS • Reduced MIPS usage, lowering mainframe operational costs • Improved customer experience resulting in less churn and use of lower cost customer service channels • Faster time to market than competitive alternatives (150-250 days) • Reduction of manual and redundant system processing SOLUTION: Precisely Connect for VSAM to Kafka on AWS North American Bank overall reduction in costs reduction in MIPS usage
- 13. OBJECTIVE Moving clients’ mission-critical core banking applications to the AWS cloud (payments, transfers, etc.) for more modern user experience, scalability, and future maintainability. Deliver data with extremely low latency from backend mainframes to front end applications running on AWS. CHALLENGES • Real-time data delivery without mainframe impact • Need a repeatable process to deploy integration on a client-by- client basis • Wanted an organization that would partner on their needs BENEFITS • Ability to scale, delivering data in near-real-time (hundreds of milliseconds) • No backpressure on the mainframe ensuring core banking systems continue to run without negative performance impact • Utility-like operation simplifies DevOps and client onboarding SOLUTION: Precisely Connect for IMS, VSAM, and Db2 to Kafka on AWS Payments processor in North America
- 14. Precisely addresses customer modernization challenges You’ve struggled with traditional solutions. We have a new vision for modern data integration. Data access owned by IT Collaboration between IT and business data users Massive, loosely integrated solutions Just the scalable, interoperable capabilities you need Data must be brought to the solution Workflows designed for the cloud that run alongside data Slow, batch ETL processes Streaming data pipelines to the cloud Separate business and IT metadata Scalable, shared catalog of business & technical metadata Rules-based data management AI-driven quality rules, alerts, and data enrichment
- 15. Data Integration benefits Accelerate application modernization Extend the value of your mainframe data Future-proof your architecture Integrate complex data formats Democratize your data
- 16. Cloud / VPC / On-Premises Data Integration Data Observability Data Quality Geo Addressing Spatial Analytics Data Governance Data Enrichment APIs and SDKs Enterprise Business Systems • Enterprise apps • Analytics tools • Precisely industry apps • BI dashboards • AI/ML Enterprise Data Sources • Business Intelligence • CRM • Workforce mgmt. • Data warehouse • ERP • Billing Data Integrity Services Data Integrity Foundation Data catalog Intelligence Agents
- 17. Key takeaways • Your data strategy needs to include mainframe and cloud • Modernization does not equal migration • Solid integration strategy is a key to success
- 18. Questions?
Editor's Notes
- Mainframes are still the backbone for the biggest organizations in the world 71% of the fortune 500 rely on the mainframe for their mission critical transactional systems and they are present in every vertical from FinServ to Insurance to Retail. When talking to these organizations, it’s not unusual to hear that up to 80% of their corporate data originates on the mainframe and that business is growing. The mainframe market is expected to grow to $2.9 billion by 2025. 90% of credit card transactions happen on mainframe systems Worldwide, mainframe systems handle 68% of information technology workloads. This accounts for only 6% of the total IT spending. Hence, usage of mainframe systems is still more cost-effective than other solutions Mainframe computers can withstand bizarre natural calamities like an earthquake of up to 8.0 magnitude A 2020 Deloitte-run survey on companies that use mainframes systems revealed the following interesting data: 74% of the survey-participant businesses consider mainframe systems important for their operations though they actively subscribe to cloud computing services. Businesses mainly consider mainframe systems as strategic technology solutions for long-term survival and operation. 91% of the participants expressed interest in expanding their big iron footprint as a moderate to critical priority in the upcoming 12 months. 72% of businesses who took the survey revealed that they are in conversation with IBM for system upgrades in the next three years.
- Research shows that: 85% of organizations will embrace a cloud-first principle by 2025 55% of leaders site data modernization as the reason for their shift to cloud Approximately $100 billion of wasted migration spend is expected over the next three years So despite the excitement around getting to the cloud, organizations need to be careful…
- Because moving to cloud comes with a heavy cost if not done strategically. McKinsey & Company recently conducted a study that shows that: Over 75% of cloud migration projects are overbudget 37% is spent on systems integrators since they do not have the cloud skills in house to manage these projects 15% on decommissioning costs for other platforms 38% of cloud migration projects run behind schedule Companies are looking to staff 50% of their cloud talent in house so they do not need to rely as heavily on third parties (If you are interested in learning more about this topic, all of this data came from this McKinsey Study https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/cloud-migration-opportunity-business-value-grows-but-missteps-abound)
- Inefficient migrations can be costly. Choosing the right solutions, services and people is critical. Recently, AWS was named a Leader in the 2023 ISG Provider Lens for Mainframe Application Modernization. Why us? At Amazon, we work backwards from customer needs. Our customers told us that migrating mainframe workloads to the cloud was a priority and a challenge. We committed to helping them address this by building strong mainframe-to-cloud capabilities. Customers modernize mainframes with us for 3 main reasons: Agility. Agility is a business’s ability to respond quickly and inexpensively to change. We provide agility for mainframe workloads in many ways, e.g. DevOps, elasticity, APIs, and managed services. Cost reduction. Customers have seen between 60% and 90% cost savings when migrating mainframe workloads to our cloud. We also offer services and mechanisms to track and optimize costs further. Our transparent, cost-aware architecture helps customers control spending and align costs with needs. And Savings can fund further modernization and innovation. Risk mitigation. Customers want to avoid risks like mainframe specialists retiring, slow change due to complexity, siloed platforms, and vendor lock-in. In summary, We deliver agility, cost reduction, and risk mitigation. Let's look at the technical approach to achieving this through AWS mainframe modernization service.
- AWS Mainframe Modernization service is a cloud native platform to modernize, migrate, run, and operate mainframe applications. We accelerate modernization and innovate continually for customers. Our service provides the capabilities to analyze, transform, develop and operate mainframe workloads. It leverages cutting-edge toolchains, AWS and partner expertise, and a dynamic, unified platform. There are four steps in the journey and we carefully chose in each step. The service supports four popular patterns: The automated Refactoring pattern and replatforming pattern are used for application migration. Available as fully managed runtime environments and pay-as-you-go services. The Data replication pattern and file transfer pattern are for data modernization. Available through integrations. Today I am going to introduce The Data Replication pattern. It Replicates mainframe data to AWS in real-time with the integrated Precisely replication unleashing data-based innovations and use-cases, leveraging AWS Mainframe Modernization service and the wide choice of AWS services. It also enables advanced analytics, machine learning, seamless data migration, and new functions and channels. There are three major use cases for the AWS Mainframe Modernization Data Replication pattern with Precisely.
- The first use case augments mainframes with agile AWS analytics. Mainframes hold decades of business and IT operations data. Using our AWS mainframe modernization data replication, customers replicate this data to AWS in near real-time or batch. We offer end-to-end data management: ingestion, processing, storage, analysis, visualization and automation. The replication service copies mainframe relational, hierarchical or legacy file data to AWS data lakes, date warehouse or data stores in near real-time or batch. The mainframe remains the source of record. And the real-time replication keeps data fresh, enabling up-to-date analytics and dashboards. With our AWS analytics, Customers can create data warehouse and data lakes to combine structured and unstructured data, leverage AI/ML and other advanced technologies, gaining new insights from core mainframe data.
- The second use case leverages our AWS Mainframe Modernization Data Replication with Precisely for large-scale migrations. A mainframe typically comprises multiple interdependent workloads. Each workload may have data stores (e.g. DB2, VSAM) and a transaction system (e.g. CICS). Based on business and IT needs, workloads follow different migration paths - there is no one-size-fits-all approach. For large workloads We Break the mainframe down into individual workloads and start migrating them one by one to AWS. This approach allows to gain experience and momentum rather than attempting a challenging "big bang" migration of the entire mainframe at once. Each workload is migrated separately to AWS according to its business and technical requirements. Since the various workloads on a mainframe often share data, customers can leverage our data replication service to synchronize that data in near real-time between the mainframe and AWS. This ensures that workloads on AWS have the access to the current data, even as some workloads remain on the mainframe.
- The third use case involves building new channels and functions. Data can be replicated bidirectionally between mainframes and AWS. On AWS, customers can innovate quickly to develop new functions like mobile or voice apps using microservices and machine learning. Since mainframe development is typically slow, customers choose AWS to build services rapidly. These new AWS services access real-time mainframe data in AWS data stores - either relational or NoSQL databases. This resembles the first use case but the data is used not for analytics but for new communication channels and functions for end users. The agile AWS functions augment mainframe applications. This avoids increasing expensive mainframe resources by deploying new channels on AWS instead.
- We work closely with partners like Precisely to drive customer success. We have curated Mainframe Competencies with partners who have deep expertise in AWS mainframe modernization and cloud services. Beyond our mainframe modernization service and partners, We also uniquely support mainframe application modernization in several ways: Dedicated mainframe modernization teams for sales, product, marketing across commercial, public sector, and FSI in North America, Europe, LatAm and APJ. No other provider invests so heavily in mainframe modernization skills. The Migration Acceleration Program (MAP) offers guidance, tooling and investment for mainframe modernization. AWS Professional Services help customers adopt and implement the latest mainframe modernization offerings. We are uniquely positioned to support mainframe workload migrations to the cloud through dedicated teams, programs, services and partners with proven expertise. Together with partners like Precisely, we provide a comprehensive solution for mainframe modernization.
- You’ve likely been working on this for a long time, but legacy solutions aren’t serving you today. We have a vision for delivering data with integrity to your business.
- Another key module of the Data Integrity Suite that I’m excited to introduce is Data Observability. This ensures data reliability by monitoring your organization’s data with added context, performing analysis to determine potential adverse data events, and alerting those who need to resolve the issues. For example, if you view a sales pipeline report, you may find that the report doesn’t have new sales opportunities added in recent weeks. This is data freshness. Or there are missing opportunities for a product line that were there last week, which is called data drift. Through a combination of profiling and automated anomaly detection, you can more quickly and accurately surface issues before they get into the hands of your business users. _____ Automated monitoring, alerting & triaging critical data quality issues, based on historical trends e.g. Freshness: changes in data update frequency Volume: changes in number of records added/deleted Data drifts: changes in value ranges, distributions, patterns, completeness, etc. Schema: changes in new/deleted columns Lineage: assess impact of data changes on upstream/downstream pipelines
- The modular, interoperable Precisely Data Integrity Suite contains everything you need to deliver accurate, consistent, contextual data to your business - wherever and whenever it’s needed. Data Integration: Break down data silos by quickly building modern data pipelines that drive innovation Data Observability: Proactively uncover data anomalies and act before they become costly downstream issues Data Governance: Manage data policy and processes with greater insight into your data’s meaning, lineage, and impact Data Quality: Deliver data that’s accurate, consistent, and fit for purpose across operational and analytical systems Geo Addressing: Verify, standardize, cleanse, and geocode addresses to unlock valuable context for more informed decision making Spatial Analytics: Derive and visualize spatial relationships hidden in your data to reveal critical context for better decisions Data Enrichment: Enrich your business data with expertly curated datasets containing thousands of attributes for faster, confident decisions