Architecting solutions that best take advantage of today's technology is an ongoing and evolving skill. New languages, frameworks, and software solutions will always provide us with more options, and it's up to us to find the best fit. We must also consider just how data is accessed in our solutions as this is a key aspect of speed, efficiency, scalability, and security. For a modern look at data on z, be sure to check out the enable modern access to IBM Z data pattern. This covers a high-level overview of SQL based query and REST API data access. These methods allow for a rapid application integration while addressing issues such as: data integrity, data quality, cost, preserving data management, and recovery processes, and allowing for higher performance while accessing data on z. Additionally, the virtualized IBM Z data pattern outlines just how in multiple data sources, such as: VSAM, IMS, and Adabas can be accessed through a simple relational view for modern data acess using those SQL and API methods we just mentioned. This gives us a single view of disparate data without data movement and allows us to manage our data with less complexity and risk of error. If your data sources are many, but data access needs to be simplified and streamlined, virtualizing data with IBM Data Virtualization Manager for z/OS is built specifically for what you want. For improving application response times and scalability, optimized copies of z data can free up compute resources by offloading application logic to specialize caching layers rather than copy, or extract transform and load, or ETL data using traditional non optimized methods. Caching support on IBM Z remains resilient against high, unpredictable volume, massive sharp spikes in activity, complexity and implementation and difficulties in data currency. IBM Db2 Analytics Accelerator, IBM Z Digital Integration Hub, IBM Db2 for z/OS Data Gateway, IBM Z Table Accelerator, and IBM Data Virtualizer for z/OS with Cache Option are products that can help optimize IBM Z data and you can learn more about that specifically in the cache IBM Z data pattern. When an alternative typically remote copy of valuable system of record data is needed in a solution, data replication is often used to make that data available. Software replication adds both near real-time replication with recovery point objectives; RPOs near zero, and continuous availability with recovery time objectives; RTOs near zero. This allows for mission critical workload level continuous availability at distance to support both planned and unplanned outages. Data replication environments can be heterogeneous, but in continuous availability solutions they are typically homogenous. Db2 to Db2, IMS to IMS, VSAM to VSAM. Optionally, IBM Z GDPS Continuous Availability is available to augment replication with a centralized control plane, intelligent routing, automation, and monitoring. To learn more about data replication refer to the Replicate IBM Z data pattern. When the solution calls for incrementally building new modernized systems of record data stores, the Transform IBM Z data pattern covers how you can transform system of record data by software processes to create a wholly new data set. On z/OS it's typical to have solutions that maintain logically-related data across different stacks such as: IBM Db2, IMS, VSAM, or sequential files. The ability to view this data through federated queries or from an aggregate copy has value in and of itself. Add the ability to extend the aggregation to derive data like summations and transformations, and add to sources like distributed databases and external feeds. The value of the original z/OS system of record data grows without distributing the original workloads. There are numerous advantages and considerations when it comes to data transformation, and you can learn more about them. That's right, in the pattern. Keeping up with modern access to IBM Z data means taking a fresh look at all your data from all angles and perspectives. Keeping these patterns close at hand, you'll be able to identify opportunities to better access, virtualize, cache, replicate, and transform your IBM Z data.
