by Joseph Brady, Director of Business Development / Cloud Alliance Leader at Treehouse Software, Inc.
Many Treehouse Software customers have discovered that they can save weeks, or months in their mainframe modernization initiatives by doing a tcVISION Proof of Concept (POC) for Mainframe-to-Cloud data replication. Depending on the complexity of the customer’s project, a tcVISION POC generally lasts as little as 10 business days after the product is installed and all connectivity is set up between the mainframe and Cloud environments. Treehouse Software provides documentation beforehand that outlines all of the requirements and agenda for the POC, and Treehouse technicians assist in downloading and installing tcVISION.
The customer provides a representative subset of z/OS or z/VSE mainframe data (e.g., Db2, Adabas, VSAM, IMS/DB, CA IDMS, CA DATACOM, etc.), use case, and goals for the POC, and the Treehouse team mentors the customer’s technical team via remote screen sharing sessions. The application is executed on customer facilities, in a non-production environment, and a limited-scope implementation of a tcVISION application is conducted to prove that the product meets the customer’s desired use case.
By the end of the POC, customers will have replicated mainframe data on their Cloud target, tested out product capabilities, and demonstrated a successful, repeatable data replication process, with documented results. After the tcVISION POC, the customer has all the connectivity and processes in place to begin setting up the production phase of their mainframe data modernization project. The minimal cost, in terms of human resources and time makes a tcVISION POC a valuable ROI in the customer’s mainframe modernization journey.
A key advantage for customers is once tcVISION is up and running, their legacy mainframe environment can continue as long as needed, while they replicate data – in real time and bi-directionally – on the new Cloud platform. Now the enterprise can quickly take advantage of the latest Cloud services, such as analytics, machine learning and artificial intelligence (AI), etc., as well as move data to a variety of highly available and secure databases and data stores.
About tcVISION…
Many Cloud and Systems Integration partners are recommending tcVISION from Treehouse Software for Mainframe-to-Cloud modernization projects. tcVISION focuses on changed data capture (CDC) when transferring information between mainframe data sources and Cloud targets. Through an innovative technology, changes occurring in any mainframe application data are tracked and captured, and then published to a variety of RDBMS and other targets.
Additionally, tcVISION utilizes a Windows-based GUI Control Board, which is ideal for non-mainframe programmers. While mainframe experts are required in the design/architecture phase during the POC and occasionally during implementation, the requirement for their involvement is limited. The tcVISION Control Board acts as a single point of administration, data modeling and mapping, script generation, and monitoring. Comprehensive monitoring and logging of all data movements ensure transparency across all data exchange processes.
Further reading…
Treehouse Software is an AWS Technology Partner and tcVISION is a Validated AWS Qualified Software. The AWS Partner Network published a blog about tcVISION, which describes how tcVISION allows legacy mainframe environments to continue, while replicating data on highly available and secure AWS targets.
Contact Treehouse Software for a tcVISION Demo Today…
Simply fill out our tcVISION Demonstration Request Form and a Treehouse representative will be contacting you to set up a time for your requested demonstration.
Treehouse Software is the worldwide distributor of tcVISION, the leading tool for using changed data capture (CDC) when transferring information between most mainframe data sources (IBM Db2, IBM VSAM, IBM IMS/DB, Software AG Adabas, CA IDMS, CA Datacom, or even sequential files) and Cloud and open systems-based databases and applications.
The following video takes a quick look at how tcVISION’s repository is used to import mainframe IDMS schema and builds out a target system on AWS:
Interested in seeing a live, online demo of tcVISION?
Treehouse Software is the worldwide distributor of tcVISION, the leading tool for using changed data capture (CDC) when transferring information between most mainframe data sources (IBM Db2, IBM VSAM, IBM IMS/DB, Software AG Adabas, CA IDMS, CA Datacom, or even sequential files) and Cloud and open systems-based databases and applications.
The following video briefly takes a look at how the tcVISION Control Board (a Windows-based GUI interface) functions as a central point of administration, data mapping and modeling, script generation, and overall monitoring. In this example, we show how to manage connectivity and data replication between a Mainframe database and AWS PostgreSQL using the tcVISION Control Board:
Further reading: tcVISION is featured on the AWS Partner Network Blog showing a walk-through of data replication between Mainframe and Amazon Aurora…
Treehouse Software is the worldwide distributor of tcVISION, the leading tool for using changed data capture (CDC) when transferring information between most mainframe data sources (IBM Db2, IBM VSAM, IBM IMS/DB, Software AG Adabas, CA IDMS, CA Datacom, or even sequential files) and Cloud and open systems-based databases and applications. Changes occurring in the mainframe application data are then tracked and captured, and published to a variety of targets.
This video demonstrates real-time data replication between Db2 and PostgreSQL on AWS:
This video demonstrates real-time data replication between Db2 and Google Cloud SQL:
This video demonstrates real-time data replication between Db2 and Azure SQL:
Further reading: tcVISION is featured on the AWS Partner Network Blog showing a walk-through of data replication between Mainframe DB2 z/OS and Amazon Aurora…
by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software, Inc.
Treehouse Software has been helping enterprise mainframe customers since 1982, and in recent years, we have been developing a strong presence in the Mainframe-to-Cloud data replication market space. This blog takes a quick look at three of the most popular Treehouse-supported Cloud-based SQL database services…
Amazon RDS, a collection of managed services that makes it simple to set up, operate, and scale databases in the Cloud. Users can control the type of database, as well as where data is stored. Specific database formats that are supported include Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server:
Google Cloud SQL, a fully managed relational database service for MySQL, PostgreSQL, and SQL server. You can connect with nearly any application, anywhere in the world. Cloud SQL automates backups, replication, and failover to ensure your database is reliable, highly available, and flexible to your performance needs:
Microsoft Azure SQL, a part of the Azure SQL family, Azure SQL Database is an always-up-to-date, fully managed relational database service built for the Cloud:
Wherever you want to target your mainframe data on the Cloud, Treehouse Software helps to make the process easy…
Treehouse Software is the worldwide distributor of tcVISION, the leading tool for using changed data capture (CDC) when transferring information between most mainframe data sources (IBM Db2, IBM VSAM, IBM IMS/DB, Software AG Adabas, CA IDMS, CA Datacom, or even sequential files) and Cloud and open systems-based databases and applications. Changes occurring in the mainframe application data are then tracked and captured, and published to a variety of targets.
Additionally, tcVISION supports bi-directional data replication, where changes on either platform are reflected on the other platform (e.g., a change to a PostgreSQL table in the Cloud is reflected back on mainframe), allowing the customer to modernize their application on the Cloud or open systems without disrupting the existing critical work on the legacy system. tcVISION’s bi-directional replication writes directly to the mainframe database, thereby bypassing all mainframe business logic, so this architecture requires careful planning, as well as thorough and repeated testing.
Sales and technical leaders at the major Cloud platform companies, as well as systems integrators are engaging with Treehouse Software to take advantage of our tcVISION data replication solution to help them tap into the mainframe data that customers want to be made available on new technologies.
Further reading: tcVISION is featured on the AWS Partner Network Blog showing a walk-through of data replication between Mainframe DB2 z/OS and Amazon Aurora…
tcVISION is a data replication solution that provides an IBM mainframe integration solution for mainframe data replication, data synchronization, data migration, and change data capture (CDC) to multiple Azure data platform services.
Contact Treehouse Software Today…
Treehouse Software is the worldwide distributor of tcVISION, a software product that allows immediate data replication between many Mainframe sources and Cloud and Open Systems targets, enabling government, healthcare, supply chain, financial, and a variety of public service organizations meet spikes in demand for vital information. No matter where you want your mainframe data to go – the Cloud, Open Systems, or any LUW target, tcVISION from Treehouse Software is your answer.
The Bundesministerium der Finanzen (BMF) is Germany’s Ministry of Finance and establishes sustainable fiscal policy that ensures financial empowerment of the federal budget. From tax policy via development of federal budget, to regulation of national and international financial markets – for these and other fiscal and economic questions of principle, the BMF creates strategies and concepts, and implements them. The Federal Tax Administration is part of BMF, and controls not only the cross-border goods traffic, but acts against illegal employment and other crimes. The tax administration also imposes consumer taxes (e.g., energy and tobacco tax, car tax, etc.). Financial relations between federation, countries, and communities are also coordinated by BMF.
Department II (federal budget) is part of the German government in charge of establishing the budget and financial planning of the federation. Throughout the year, it monitors execution of the budget for eventual intervention (e.g., with a budget freeze, or supplementary budget). After closing the fiscal year, the budget and balance sheet will be presented. The budget is a supplement of the budget act, legally binding.
The central service organization of BMF is the Informationstechnikzentrum Bund – ITZBund (Information technic center).
BUSINESS BACKGROUND
Drawing up the budget is a yearly, highly time consuming, and formalized business process. All departments are involved in nearly every sub-process, and budgeting and financial planning is supported by the application, “Haushaltsaufstellung / Budgetgeneration”. Using the generated reports, various addressees/receivers are supported (e.g., German Federal Government, German Federal Parliament, Federal Council of Germany, finance department in BMF, the employees in the departments, and the public).
Technically, the budget plan of the federation is based on technologies, including the IBM Mainframe with z/OS running Adabas and Natural.
The challenge was to provide an environment for employees in all departments that enables them to do their work quickly, easily, and efficiently. In the BMF, users must have an editorless, end-user driven, and real-time creation of ready-to-print products. An informative description of the workflow is shown on the website of the BMF.
The federal budget is available as download, or one can directly navigate through the data using the online application.
BUSINESS ISSUE
Some time ago, BMF decided to re-engineer the application for budget planning and port it to Open Source. To guarantee a seamless transition, the first step is propagation of data out of Adabas on z/OS to PostgreSQL, concluding with permanent synchronization.
The difficulties of this task are the complexities of setting up data definitions for the data structures in Natural and the propagation of data from Adabas on z/OS to PostgreSQL.
TECHNOLOGY SOLUTION: tcVISION
After an analysis of the project, Treehouse Software proposed creating an extension to tcVISION’s change data capture (CDC) functionality for integration, so that tcVISION could enable BMF to continue using the implemented data definitions in a format suitable for the RDBMS.
The extension was developed within a few days, and a two-day on premise test demonstrated the solution fit the requirements of BMF.
BMF can now provide its data definitions from Natural LDA to the extension of tcVISION, and after the transformation, onto the PostgreSQL load process for processing. Another advantage of the tcVISION solution is that when needed, other targets can be integrated for propagation of data from the mainframe (e.g., Kafka, which BMF indicated is a future target environment).
Additionally, bi-directional propagation can be added in budget planning when BMF is ready.
Data structures are held in LDA, because this provides the advantages of higher flexibility in development and the adaption of new requirements to the data definitions. If definitions would have to be ported manually, in part, to PostgreSQL, it would have been a much bigger and error-prone effort.
Subsequent changes to Adabas structures can now use tcVISION’s newly developed extension to easily regenerate and load the correct definitions to the RDBMS, and tcVISION completely covers the customer’s requirements for special usage of *PEs and *MUs.
After thorough preparation and extensive testing, the solution was released to selected users first, then made available to all users.
* PEs and MUs are special Adabas formats for definition of tables. PE = Periodic Group, MU = Multiple Value Field.
Contact Treehouse Software for a Demo Today…
No matter where you want your mainframe data to go – the cloud, open systems, or any LUW target – tcVISION from Treehouse Software is your answer.
by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software
Careful planning must occur for a Mainframe-to-Cloud data modernization project, including how a customer’s desired Cloud environment will look. This blog serves as a general guide for organizations planning to replicate their mainframe data on Cloud platforms using Treehouse Software‘s tcVISION.
A successful move to the Cloud requires a number of post-migration considerations and solutions in order to modernize an application on the Cloud. Some examples of these considerations and solutions include:
Personnel Resource Considerations
Staffing for Mainframe-to-Cloud data replication projects depends on the scale and requirements of your replication project (e.g., bi-directional data replication projects will require more staffing).
Most customers deploy a data replication product with Windows and Linux knowledgeable staff at varying levels of seniority. For the architecture and setup tasks, we recommend senior technical staff to deal with complex requirements around the mainframe, Cloud architecture, networking, security, complex data requirements, and high availability. Less senior staff are effective for the more repeatable deployment tasks such as mapping new database/file deployments. Business staff and system staff are rarely required but can be necessary for more complex deployment tasks. For example, bi-directional replication requires matching keys on both platforms and their input might be required. Other activities would be PII consideration, specifics of data transformation and data verification requirements.
An example of staffing for a very large deployment might be one very part-time project manager, a part-time mainframe DBA/systems programmer, 1-2 staff to setup and deployment the environment and an additional 1-2 staff to manage the existing replication processes.
Environment Considerations
As part of the architecture planning, your team needs to decide how many tiers of deployment are needed for your replication project. Much like with applications, you may want a Dev, QA, and Prod tier. For each of these tiers, you will need to decide the level of separation. For example, you might combine Dev and QA, but not Prod. Many customers will keep production as a distinct environment. Each environment will have its own set of resources, including mainframe managers (possibly on separate LPARs), Could VMs (e.g., EC2) for replication processing, and for managed Cloud RDBMSs (such as AWS RDS).
After the required QA testing, changes are deployed to the production environment. Object promotion test procedures should be detailed and documented, allowing for less experience personnel to work in some testing tasks. Adherence to details, processes, and extended testing is most import when deploying bi-directional replication, due to the high impact of errors and difficult remediation.
Rollout Planning
A data replication product is typically deployed using Agile methods with sprints. This allows for incrementally realized business value. The first phase is typically a planning/architecture phase during which the technical architecture and deployment process are defined. Files for replication are deployed in groups during sprint planning. Initial sprint deployments might be low value file replications to shield the business from any interruptions due to process issues. Once the team is satisfied that the process is effective, replication is working correctly, and data is verified on the source and targets, wide scale deployments can start. The number of files to deploy in a sprint will depend on the customer’s requirements. An example would be to deploy 20 mainframe files per 2–3-week sprint. Technical personnel and business users need to work together to determine which files and deployment order will have the greatest business benefit.
Security
For security, both on-premises and to the major Cloud environments, there are several considerations:
Data will be replicated between a source and target. The data security for PII data must be considered. In addition, rules such as HIPPA, FIPS, etc. will govern specific security requirements.
The path of the data must be considered, whether it is a private path, or if the data transverses the internet. For example, when going from on-premises to the Cloud the major Cloud providers have a VPN option which encrypts data going over the internet. More secure options are also available, such as AWS Direct Connect and Azure ExpressRoute. With these options, the on-premises network is connected directly to the Cloud provider edge location via a telecom provider, and the data goes over a private route rather than the internet.
Additionally, Cloud services such as S3, Azure Blob Storage, and GCP buckets default to route service connections over the internet. Creating a private end point (e.g., AWS PrivateLink) allows for a private network connection within the Cloud provider’s network. Private connections that do not traverse the Internet provide better security and privacy.
Protecting data at rest is important for both the source and target environments. The modern Z/OS mainframe has advanced pervasive and encryption capabilities: https://www.redbooks.ibm.com/redbooks/pdfs/sg248410.pdf. The major Cloud providers all provide extensive at-rest encryption capabilities. Turning on encryption for Cloud Storage and databases is often just a parameter setting and the Cloud provider takes care of the encryption, keys, and certificates automatically.
Protecting data in transit is equally important. There are often multiple transit points to encrypt and protect. First, is the transit from the mainframe to on-premises to the Cloud VM instance. A mainframe data replication product should provide protection employing TLS 1.2 to utilize keys and certificates on both the mainframe and Cloud. Second is from the Cloud VM to the Cloud target database or service. Encryption may be less important since often these services are in a private environment. However, encryption can be achieved as required.
High Availability
During CDC processing, high availability must be maintained in the Cloud environment. The data replication product should keep track of processing position. The first can be a Restart file, which keeps track of mainframe log position, target processing position, and uncommitted transactions. The second can be a container stored on Linux or Windows to store committed unprocessed transactions. Both need to be on highly available storage with a preference for storage across Availability Zones (AZs), such as Elastic File System (Amazon EFS) or Windows File Server (FSx).
The Amazon EC2 instance (or other Cloud instance) can be part of an Auto Scaling Group spread across AZs with minimum and maximum of one Amazon EC2 instance.
Upon failure, the replacement Amazon EC2 instance of the replication product’s administrator function is launched and communicates its IP address to the product’s mainframe administrator function. The mainframe then starts communication with the replacement Amazon EC2 instance.
Once the Amazon EC2 instance is restarted, it continues processing at the next logical restart point, using a combination of the LUW and Restart files.
For production workloads, Treehouse Software recommends turning on Multi-AZ target and metadata databases.
Scalable Storage
With scalable storage provided on most Cloud platforms, the customer pays only for what is used. The data replication product should require file-based storage for its files that can grow in size if target processing stops for an unexpected reason. For example, Amazon EFS, and Amazon FSx provide a serverless elastic file system that lets the customer share file data without provisioning or managing storage.
Analytics
All top Cloud platform providers give customers the broadest and deepest portfolio of purpose-built analytics services optimized for all unique analytics use cases. Cloud analytics services allow customers to analyze data on demand, and helps streamline the business intelligence process of gathering, integrating, analyzing, and presenting insights to enhance business decision making.
A data replication product should replicate data to several data sources that can easily be captured by various Cloud based analytics services. For example, mainframe database data can be replicated to the various Cloud ‘buckets’ in JSON, CSV, or AVRO format, which allows for consumption by the various Cloud analytic services. Bucket types include AWS S3, Azure BLOB Data, Azure Data Lake Storage, and GCP Cloud storage. Several other Cloud analytics type services also support targets including Kafka, Elasticsearch, HADOOP, and AWS Kinesis.
Kafka has become a common target and can serve as a central data repository. Most customers target Kafka using JSON formatted replicated mainframe data. Kafka can be installed on-premises, or using a managed Kafka service, such as the Confluent Cloud, AWS Managed Kafka, or the Azure Event Hub.
Monitoring
Monitoring is a critical part of any data replication process. There are several levels of monitoring at various points in a data replication project. For example, each node of the replication including the mainframe, network communication, Cloud VM instances (such as EC2) and the target Cloud database service all can require a level of monitoring. The monitoring process will also be different in development or QA vs. a full production deployment.
A data replication product should also have its own monitoring features. One important area to measure is performance and it is important to determine where any performance bottleneck is located. Sometimes it could be the mainframe process, the network, the transformation computation process, or the target database. A performance monitor helps to detect where the bottleneck is occurring and then the customer can drill down into specifics. For example, if the bottleneck is the input data, areas to examine are the mainframe replication product component performance, or the network connection. The next step is to monitor the area where the bottleneck is occurring using the data replication product’s statistics, mainframe monitoring tools, or Cloud monitoring such as AWS CloudWatch.
A data replication product should also allow the customer to monitor processing functions during the replication process. The data replication product should also have extensive logs and traces that allow for detailed monitoring of the data replication process and produce detailed replication statistics that include a numeric breakdown of processing statistics by table, type of operation (insert, update delete), and where these operations occurred (mainframe, or target database).
CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing customers with a unified view of AWS resources, applications, and services that run on AWS, and on-premises servers. You can use CloudWatch to set high resolution alarms, visualize logs and metrics side by side, take automated actions, troubleshoot issues, discover insights to optimize your applications, and ensure they are running smoothly.
Some customers are satisfied with a basic monitoring that polls every five minutes, while others need more detailed monitoring and can choose polls that occur every minute.
CloudWatch allows customers to record metrics for EC2 and other Amazon Cloud Services and display them in a graph on a monitoring dashboard. This provides visual notifications of what is going on, such as CPU per server, query time, number of transactions, and network usage.
Given the dynamic nature of AWS resources, proactive measures including the dynamic re-sizing of infrastructure resources can be automatically initiated. Amazon CloudWatch alarms can be sent to the customer, such as a warning that CPU usage is too high, and as a result, an auto scale trigger can be set up to launch another EC2 instance to address the load. Additionally, customers can set alarms to recover, reboot, or shut down EC2 instances if something out of the ordinary happens.
Disaster Recovery
IT disasters such as data center failures, or cyber attacks can not only disrupt business, but also cause data loss, and impact revenue. Most Cloud platforms offer disaster recovery solutions that minimize downtime and data loss by providing extremely fast recovery of physical, virtual, and Cloud-based servers.
A disaster recovery solution must continuously replicate machines (including operating system, system state configuration, databases, applications, and files) into a low-cost staging area in a target Cloud account and preferred region.
Unlike snapshot-based solutions that update target locations at distinct, infrequent intervals, a Cloud based disaster recovery solution should provide continuous and asynchronous replication.
Consult with your Cloud platform provider to make sure you are adhering to their respective best practices.
Many organizations lack the internal resources to support AI and machine learning initiatives, but fortunately the leading Cloud platforms offer broad sets of machine learning services that put machine learning in the hands of every developer and data scientist. For example, AWS offers SageMaker, GCP has AI Platform, and Microsoft Azure provides Azure AI.
Applications that are good candidates for AI or ML are those that need to determine and assign meaning to patterns (e.g., systems used in factories that govern product quality using image recognition and automation, or fraud detection programs in financial organizations that examine transaction data and patterns).
The list goes on…
Treehouse Software and our Cloud platform and migration partners can advise and assist customers in designing their roadmaps into the future, taking advantage of the most advanced technologies in the world.
Successful customer goals are top priority for all of us, and we can continue to work with our customers on a consulting basis even after they are in production.
Of course, each project will have unique environments, goals, and desired use cases. It is important that specific use cases are determined and documented prior to the start of a project and a tcVISION POC. This planning will allow the Treehouse Software team and the customer develop a more accurate project timeline, have the required resources available, and realize a successful project.
Your Mainframe-to-Cloud Data Migration Partner…
Treehouse Software is a global technology company and Technology Partner with AWS, Google Cloud, and Microsoft. The company assists organizations with migrating critical workloads of mainframe data to the Cloud.
Further reading on tcVISION from AWS, Google Cloud, and Confluent:
tcVISION supports a vast array of integration scenarios throughout the enterprise, providing easy and fast data migration for mainframe application modernization projects. This innovative technology offers comprehensive abilities to identify and capture changes occurring in mainframe and relational databases, then publish the required information to an impressive variety of targets, both Cloud and on-premises.
tcVISION acquires data in bulk or via CDC methods from virtually any IBM mainframe data source (Software AG Adabas, IBM Db2, IBM VSAM, CA IDMS, CA Datacom, and sequential files), and transform and deliver to a wide array of Cloud and Open Systems targets, including AWS, Google Cloud, Microsoft Azure, Confluent, Kafka, PostgreSQL, MongoDB, etc. In addition, tcVISION can extract and replicate data from a variety of non-mainframe sources, including Adabas LUW, Oracle Database, Microsoft SQL Server, IBM Db2 LUW and Db2 BLU, IBM Informix, and PostgreSQL.
Contact Treehouse Software for a tcVISION Demo Today…
Simply fill out our tcVISION Demonstration Request Form and a Treehouse representative will be contacting you to set up a time for your requested demonstration.
by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software, Inc.
Treehouse Software was recently invited by Microsoft Azure Mainframe Modernization technical teams to do a presentation and demonstration of tcVISION, our innovative Mainframe-to-Cloud data replication software product.
In this video, we show an overview of the product, then demonstrate replication of mainframe data on Azure SQL:
by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software, Inc.
tcVISION allows enterprise customers to replicate data between mainframe, Cloud, or Hybrid Cloud while maintaining their legacy environments.
We are currently working with a government site to architect bi-directional mainframe data replication on Azure SQL. One of the customer’s requirements is for tcVISION to provide real-time data synchronization of changes on either platform reflected on the other platform (e.g., a change to an Azure SQL table is reflected back on mainframe). This way, the customer can modernize their application on the Azure Cloud without disrupting the existing critical work on their legacy system.
VIDEO: See how tcVISION easily connects mainframe systems to Azure SQL…
The tcVISION solution focuses on changed data capture (CDC) when transferring information between mainframe data sources and modern databases and applications. Through an innovative technology, changes occurring in any mainframe application data are tracked and captured, and then published to a variety of targets.
Azure SQL is a supported target in tcVISION, and in this instructional video, tcVISION is shown synchronizing data in real-time between Db2 on z/OS and Azure SQL:
Contact Treehouse Software Today…
No matter where you want your mainframe data to go – the Cloud, Open Systems, or any LUW target – tcVISION from Treehouse Software is your answer.
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