Should You Stay, or Should You Go? You Can Do Both by Incrementally Replicating Your Mainframe Data on the Cloud While Keeping Both Sides Synchronized

by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software, Inc.

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Many of Treehouse Software’s enterprise customers are not close to considering the retirement of their mainframe systems, but instead have long-term data replication projects, or want to indefinitely have their legacy systems co-exist with a new Cloud platform. These organizations are looking for solutions that allow their legacy mainframe environment to continue while replicating data – in real time and bi-directionally – to take advantage of the latest Cloud services, such as analytics, auto scaling, machine learning and artificial intelligence (AI), high availability, advanced security, etc., or move data to a variety of newer Cloud databases, streaming services, container services, and more.

The Transition Doesn’t Have to be a Sudden Big Bang

Much of an enterprise’s mission critical mainframe data is stored in legacy mainframe databases, and the cost to maintain these databases is high.  An added complication is that the data is utilized by many interlinked and dependent programs that have been in place for many years, and sometimes decades. Unlocking the value of this legacy data is also difficult due to many very different types of mainframe databases (e.g., Db2, Adabas, CA Datacom, CA IDMS, etc.).

Immediate data replication on the Cloud is enabling government, healthcare, supply chain, financial, and a variety of public service organizations to meet spikes in demand for vital information, especially in times of crisis. The globalization of markets, increase of data volumes, 24×7 operations, changing business conditions, and high demand for up-to-date information also requires new data transfer and exchange solutions for heterogeneous IT architectures.

The Data-First Solution

Treehouse Software is here to help enterprise mainframe customers accelerate digital transformation and successfully leverage Hybrid Cloud initiatives on the IBM Z platform, storing sensitive data on a private Cloud or local data center and simultaneously leveraging leading technologies on a managed public Cloud. Our tcVISION replication 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. The customer moves only the right data to the right place at the right time – as much, or as little as they want.

The tcVISION replication solution has a modular design, which enables it to support mass data load from one source to one or more targets, as well as continuous data exchange processes in realtime via CDC. This modular architecture and the provided APIs gives customers unlimited potential for growth and use of new technologies.

tcVISION allows bi-directional, real-time data synchronization of changes on either platform to be reflected on the other platform (e.g., a change to a PostgreSQL table is reflected back on mainframe). The customer can then modernize their application on the cloud, open systems, etc. without disrupting the existing critical work on the legacy system.

In the following example high level architecture diagram, bi-directional data replication between Db2 z/OS and AWS using tcVISION is shown:

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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 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. In the following example, the mainframe can be seen communicating to an Amazon EC2-based tcVISION replication manager. The tcVISION Control Board shows the user a graphical representation of this replication:

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Additionally, tcVISION supports complex data replication scenarios between multiple data sources and targets, as seen here:

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With tcVISION, data replication projects can be implemented within a few of months, depending on the complexity of the project.  This includes the proof of concept and design/architecture stages.  After these stages are complete, the customer can start the first production implementation sprint, immediately providing business value.  We suggest successive agile sprints to allow for incremental deployment of additional file replication, sprint by sprint.

Supported Sources and Targets

tcVISION supports a vast array of integration scenarios throughout the enterprise, providing easy and fast data replication for Mainframe-to-Cloud and Open Systems application modernization projects.


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Contact Treehouse Software for a tcVISION Demo Today…

Just fill out the Treehouse Software tcVISION Demonstration Request Form and a Treehouse representative will contact you to set up a time for your tcVISION demonstration. This will be a live, on-line demonstration that shows tcVISION replicating data from the mainframe to a Cloud target database.

Mainframe-to-Cloud Data Replication with tcVISION: Recommendations for Roadmapping Your Deployment on a Cloud Environment

by Joseph Brady, Director of Business Development and Cloud Alliance Leader at Treehouse Software

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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.
  • Example: https://docs.aws.amazon.com/whitepapers/latest/disaster-recovery-workloads-on-aws/introduction.html

Artificial Intelligence and Machine Learning

  • 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:

More About tcVISION from Treehouse Software…

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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.


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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.

The Mainframe-to-Hybrid Cloud Wave has Arrived, and Treehouse Software is Helping Customers Begin the Ride!

by Joseph Brady, Director of Business Development / AWS and Cloud Alliance Lead at Treehouse Software and Andy Jones, Certified AWS Solutions Architect at Treehouse Software

There are many pioneering organizations with mainframe systems that have already begun their movement to the Cloud, and are now taking advantage of the new and powerful technologies delivering significant business benefits and competitive advantage, including:

Trade Capital Expense for Variable Expense – Instead of having to invest heavily in data centers and servers before you know how you’re going to use them, you can pay only when you consume computing resources, and pay only for how much you consume. https://aws.amazon.com/pricing/

Global Deployments – The AWS Cloud spans 22 geographic regions globally. Enterprises can easily deploy applications in multiple regions around the world with just a few clicks. This means you can provide lower latency and a better experience for your customers at minimal cost. https://aws.amazon.com/about-aws/global-infrastructure/regions_az/

Economies to Scale – By using Cloud computing, you can achieve a lower variable cost than you can get on your own, because usage from hundreds of thousands of customers is aggregated in the Cloud, providers such as AWS can achieve higher economies of scale, which translates into lower pay as-you-go prices. https://aws.amazon.com/economics/

Scale of Services – Amazon Web Services offers a broad set of global Cloud-based products including compute, storage, databases, analytics, networking, mobile, developer tools, management tools, IoT, security, and enterprise applications. These services help organizations move faster, lower IT costs, and scale. https://aws.amazon.com/products/

World Class Security – AWS security compliance is second to none and complies with the most stringent government and private sector requirements. https://aws.amazon.com/compliance/programs/

Extreme High Availability (HA) – The AWS Cloud spans 69 Availability Zones within 22 geographic Regions around the world https://aws.amazon.com/about-aws/global-infrastructure/regions_az/.  By designing your services and applications to be redundant across AWS availability zones or regions, HA is enhanced far beyond a single on premises data center. https://aws.amazon.com/marketplace/solutions/infrastructure-software/high-availability 

Testing at Scale – AWS servers and services can be created and charged on demand for a specific amount of time.  This allows customer to create temporary large-scale test environments prior to deployment that are not practical for on premises environments.  Large scale testing reduces deployment risks and helps to provide a better customer experience. https://aws.amazon.com/  

Auto Scaling and Serverless Deployments – AWS has many serverless and autoscaling options available, allowing for scalable computing capacity as required.  For example, AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume – there is no charge when your code is not running. Another example is Amazon Aurora Serverless, which is an on-demand, auto-scaling configuration for Amazon Aurora (MySQL-compatible edition), where the database will automatically start up, shut down, and scale capacity up or down based on your application’s needs. https://aws.amazon.com/serverless/

Customer Agility and Innovation – In a Cloud computing environment, new IT resources are only a click away, which means that you reduce the time to make those resources available to your developers from weeks to just minutes. This results in a dramatic increase in agility for the organization, since the cost and time it takes to experiment and develop is significantly lower. https://aws.amazon.com/architecture/

Infrastructure as Code – AWS CloudFormation provides a common language for you to describe and provision all the infrastructure resources in your Cloud environment. CloudFormation allows you to use programming languages or a simple text file to model and provision, in an automated and secure manner, all the resources needed for your applications across all regions and accounts. This gives you a single source of truth for your AWS resources. https://aws.amazon.com/cloudformation/ 

However, compared to the number of mainframe shops in enterprises that are just beginning to plan their moves to the Cloud, existing adopters’ numbers are still relatively small.

As an example of the current boom in overall Cloud growth, the worldwide public sector Cloud market will be growing to a staggering $331B by 2022 according to Gartner. By the end of 2019, more than 30% of technology providers’ new software investments will shift from Cloud-first to Cloud-only, further reducing license-based software spending and increasing subscription-based Cloud revenue. (Source: Forbes) 

Treehouse Software is a Trusted Partner on Your Mainframe-to-Cloud Journey

Treehouse Software is a well-established company serving mainframe customers since 1982. We are currently developing a strong presence in the emerging Cloud market space related to mainframe data migration, primarily through our partnership with Amazon Web Services (AWS).  AWS is aware that most large enterprises use mainframe systems that are housing vast amounts of data encompassing historical, customer, logistics, etc., and they have helped us bring our tcVISION solution to the AWS Marketplace. tcVISION provides real-time replication between a variety of mainframe and non-mainframe sources, including (Mainframe): VSAM, IMS, Db2, CA Datacom, Adabas, CA IDMS, and Flat Files; and (Non-mainframe): AWS RDS databases, AWS Aurora, AWS S3, AWS Kinesis,  PostgreSQL, MySQL, Kafka, MongoDB, Hadoop, Oracle, Microsoft SQL Server, IBM Db2 LUW and Db2 BLU, IBM Informix, SAP Hana, and many more. 

AWS sales and technical leaders within various verticals (GovCloud, Nonprofit, K12/Higher Ed, Automotive, etc.) are also beginning to engage with Treehouse Software to learn more about our unique skills and solution that can help them tap into this potential goldmine of massive amounts of legacy data that needs to be moved to AWS. 

Treehouse Software Helps Customers Begin Moving Their Mainframe Data to the Cloud Immediately

Treehouse Software specializes in providing data replication for enterprise customers who want a fully developed and automated way to move data from their mainframe systems to the Cloud. Treehouse Software’s tcVISION is a low risk option that allows customers to immediately begin moving data to the Cloud while they work on the sometimes massive complexity of application migration. Our experience has shown that projects can become stalled while the application side is being figured out. For example, Treehouse Software recently became involved in a project with a government agency that was facing the complexity of a “big bang” migration, which is slowing the project. We are now presenting them with our tcVISION data replication solution option, where they can replicate data to AWS while maintaining their current environment for modernization and migration of their applications. 

Additionally, Treehouse Software’s decades of experience developing software and working in the IBM mainframe environment, in addition to selling and supporting a comprehensive automated data replication product, is making us a desirable partner for AWS and many Cloud migration companies.  

AWS recently published a blog about tcVISION, our Mainframe-to-Cloud data replication product: https://aws.amazon.com/blogs/apn/real-time-mainframe-data-replication-to-aws-with-tcvision-from-treehouse-software/ 

Additionally, here is a blog about Treehouse Software’s extensive mainframe experience: https://treehousesoftware.wordpress.com/2019/09/12/treehouse-softwares-differentiator-weve-been-helping-enterprise-mainframe-sites-since-1982/

If your enterprise is planning on riding the wave with a Mainframe-to-Cloud migration project, we would welcome the opportunity to help get you moving immediately with an online presentation and demonstration of our tcVISION data replication solution. Contact Treehouse Software today!

U.S. Government Customers Can Now Use Treehouse Software’s tcVISION Mainframe Data Replication on the AWS GovCloud

Treehouse Software’s tcVISION Mainframe-to-AWS data replication product is now available in the AWS GovCloud (US) Region of the AWS Marketplace.

The AWS GovCloud (US) Region is designed to host sensitive data, regulated workloads, and address the most stringent U.S. government security and compliance requirements.

The AWS GovCloud (US) is available to government customers, organizations in government-regulated industries, and other private entities that meet AWS GovCloud (US) requirements.

AWS GovCloud (US) gives government customers and their partners the flexibility to architect secure cloud solutions that comply with: the FedRAMP High baseline, the DOJ’s Criminal Justice Information Systems (CJIS) Security Policy, U.S. International Traffic in Arms Regulations (ITAR), Export Administration Regulations (EAR), Department of Defense (DoD) Cloud Computing Security Requirements Guide (SRG) for Impact Levels 2, 4 and 5, FIPS 140-2, IRS-1075, and other compliance regimes.

From Personally Identifiable Information (PII), sensitive patient medical records, and financial data to law enforcement data, export controlled data and other forms of CUI, AWS GovCloud (US) Regions can help customers address compliance at every stage of their cloud journey.


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The tcVISION Amazon Machine Images (AMIs) are available on the AWS Marketplace. A tcVISION AMI automatically sets up the product on the AWS Cloud and allows customers to use it with either backup/recovery mainframe files or Windows files, or to set up connectivity for real-time, on-premises mainframe data replication to AWS. An enterprise with mission critical data in a mainframe environment can rapidly deploy databases globally within minutes, and with minimal administration requirements. U.S Government sites can simply choose the region “AWS GovCloud (US)” under “Pricing Information” when choosing and AMI.

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There are three versions of the tcVISION AMI on the AWS Marketplace, and #1 and #3 require two-week trial license keys provide by Treehouse Software:

1. tcVISION Mainframe Batch Integration (MBI) for AWS – Bring Your Own License (Customers must contact Treehouse Software for license key to run the software): This AMI does not require an active connection to the mainframe, because the existing backup and recovery files from the mainframe, such as Db2 imagecopies, z/OS archive log files or IMS Unloads, Adabas unload, and PLOG files can be used.

2. tcVISION Distributed Database Integration (DDI) for AWS – Pay as You Go on AWS: This AMI license installation includes security keys for Amazon S3 access and a license to use one source (PostgreSQL, Oracle, MySQL, MariaDB; MSSQL on Windows OS only) and one target (AWS S3, AWS Aurora, RDS – PostgreSQL, Oracle, MySQL, MariaDB; MSSQL on Windows OS only).

3. tcVISION Enterprise Change Data Capture (CDC) Integration for AWS – Bring Your Own License (Customers must contact Treehouse Software for license key to run the software): This AMI allows transfer of mainframe data to AWS targets continuously and in real-time. Using this tcVISION AMI, a customer can deploy databases globally within minutes.


About the AWS Marketplace…

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The AWS Marketplace is an online software store that helps customers find, buy, and immediately start using the software and services that run on AWS.


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Visit the Treehouse Software website for more information on tcVISION, or contact us to discuss your needs.