In this episode of the AWS Mainframe Modernization Broadcasting Channel webinar series, you will discover how enterprises are breaking down data silos by migrating mainframe data to Snowflake on AWS. In a real-world case study, see how Treehouse Software handles the complexities of VSAM, Db2, and Adabas data structures to deliver clean, queryable data, ready for modern analytics. Find out how the Treehouse Dataflow Toolkit (TDT) works with virtually any mainframe data replication tool to provide a fully automated approach for rapid and comprehensive data transfer from Kafka streaming pipelines to Snowflake and other Analytics/AI/ML-friendly targets on AWS–AI-ready, with all target resources automatically created.
View the webinar recording here…
Contact Treehouse Software today to request a product demonstration or discuss your data modernization needs…
Discover how enterprises are breaking down data silos by migrating mainframe data to Snowflake, enabling unified analytics across legacy and modern systems. In a real-world case study, see how Treehouse Software handles the complexities of VSAM, Db2, and Adabas data structures to deliver clean, queryable data ready for modern analytics.
Meet your presenters…
Sunil Divvela is a Worldwide Specialist Solutions Architect for Mainframe Modernization at AWS. He partners with customers and partners to accelerate their mainframe modernization journeys, leading initiatives from portfolio assessment through post-migration support leveraging Generative AI and Agentic AI. Prior to AWS, Sunil served as a Senior Technology Architect at Infosys, where he led multiple mainframe transformation programs.
Ellie Savova is a Cloud Solutions Architect at Treehouse Software, where she develops and architects AWS-cloud-native SaaS applications focused on enterprise data migrations. She works closely with customers to design secure, scalable cloud-native solutions across hybrid environments, integrating legacy data systems with modern analytics platforms. Ellie also leads customer implementations and innovation initiatives focused on cloud architecture, security, automation, and next-generation data-platform capabilities.
by Joseph Brady, Director of Business Development at Treehouse Software
Treehouse Software’s Treehouse Dataflow Toolkit (TDT) is currently in production at a large auto manufacturer as their key component for replicating dealership and vehicle order management data from multiple disparate mainframe databases to Snowflake on AWS.
The TDT solution, along with Treehouse’s decades of mainframe expertise, and our Cloud Engineers’ deep skills with multiple top-level AWS certifications accelerated the customer’s critical data move to Snowflake. Thanks to the Treehouse data delivery architecture, the customer’s data scientists and analysts can now access analytics-ready data through Snowflake. This enables sub-second query performance for rapid, intensive analytical tasks, and data sharing in real time, eliminating the need to move or copy data, thus enabling immediate insights across divisions and subsidiaries. The analytics teams can also make plans to easily add the latest AWS-based Analytics/AI/ML-friendly offerings, such as Amazon Redshift, Amazon Athena/S3, Amazon SageMaker AI, Amazon Bedrock, as well as any yet-to-be-developed Cloud services!
Since TDT is much more than a mere “connector,” the customer was able to eliminate months of development time and costs by using the tool to quickly and automatically prepared the full infrastructure needed for Snowflake data loading. As shown in the following architectural diagram, TDT is taking the customer’s mainframe data that was pumped into Amazon MSK (Managed Streaming for Kafka) by Rocket Data Replicate and Sync (RDRS) and lands it into Snowflake. TDT not only delivers the customer’s data, but its advanced crawler functions automatically prepare landing tables, views, and staging infrastructure for Snowflake. Additionally, when needed, TDT now stands ready to generate an archiving infrastructure and create Apache Iceberg tables for enhanced data management.
The Treehouse solution is enabling the customer to quickly move away from slow, on-premises, batch-oriented processes to their new scalable, highly available, and secure Cloud-native system. They are also better positioned to innovate for future data needs and strategies on a flexible and easily customizable architecture.
Customer Benefits
The customer’s data scientists and analysts can now access analytics-ready data faster and ever before through Snowflake
The new architecture easily allows testing and adding other Cloud-based Analytics/AI/ML-friendly targets and services.
From a data scientist’s perspective, effective dataflow tooling with TDT has delivered tangible benefits:
Fewer reconciliation issues with other divisions
Greater confidence in analytical results
Faster turnaround on urgent questions from leadership and multiple divisions
TDT’s auto scaling and parallelizing Lambda framework allows many parallel selects to all run at once, thus loading large tables with minimal latency.
Since TDT is built in alignment with AWS’s and Snowflake’s best practices, proper security and performance is ensured.
TDT is delivered via CloudFormation Templates, which automated and accelerated the process of installing and configuring the complete TDT application (including AWS Lambda functions and numerous other AWS resources, all wrapped in a well-architected security framework) in their AWS account. This allowed the customer to be up and running with a fully preconfigured implementation of the new data transfer pipeline in minutes.
The customer now has lasting compatibility with emerging Cloud technologies. As AWS and Snowflake introduce new features, TDT readily integrates them, staying ahead of the curve, keeping data pipelines modern and efficient.
In short: better data → better analytical judgment.
Visit Treehouse Software on the AWS Marketplace for all of our Cloud offerings…
by Joseph Brady, Director of Business Development at Treehouse Softwareand Eleonora Savova, Cloud Solutions Architect at Treehouse Software
Every major insurance policy begins with the same foundation: understanding historical claims data to determine how often claims occur, what they cost, and which factors drive risk.
Data analytics has always been the insurance industry’s lifeblood, with actuarial efforts relying on sophisticated analysis of historical data – an approach that, in many ways, resembles how modern machine learning (ML) techniques are used to quantify and manage risk and uncertainty.
All insurance companies hold decades of valuable data across legacy mainframe and non-mainframe systems. Actuaries, data scientists, and other analysts need data delivered into modern analytics environments in a reliable way. Treehouse Software addresses this challenge in the most straightforward and efficient manner by connecting legacy data to today’s top analytics platforms. Our Treehouse Dataflow Toolkit (TDT) enables rapid bulk load and change data capture (CDC) from multiple data sources into Snowflake and AWS. As part of this process, TDT automatically provides the required target infrastructure, making the data immediately available for Analytics, BI, and AI/ML operations.
TDT serves as a key component within AWS-based actuarial analytics architectures, enabling insurance organizations to move away from slow, on-premises, batch-oriented processes and toward scalable, cloud-native systems. These architectures leverage high-performance computing, data lakes, and serverless technologies to automate data ingestion, modeling, and reporting, reducing processing times from days to minutes.
With TDT handling data delivery, actuaries and data scientists can access analytics-ready data through platforms such as Snowflake, Amazon Redshift, etc. TDT operates in the background, ensuring that the most recent data is consistently available without manual intervention.
TDT Value and Benefits
Beyond delivering your data, TDT is MUCH more than a mere “connector”. It is a fully configurable end-to-end solution designated to manage the complete data delivery lifecycle. TDT’s advanced crawler capabilities automatically prepare landing tables, views, and staging infrastructure required by the target.
TDT is built in alignment with AWS’s and Snowflake’s best practices, ensuring proper security and performance. This consistent adherence to best practices is a key differentiator that sets TDT apart from many other “connectors” on the market.
From an actuary’s perspective, effective dataflow tooling with TDT delivers tangible benefits:
Fewer reconciliation issues with Finance
Greater confidence in analytical results
Faster turnaround on urgent questions from leadership
In short: better data → better actuarial judgment.
Treehouse provides highly-detailed CloudFormation Templates which automate and accelerate the process of installing and configuring the complete TDT application (including AWS Lambda functions and a number of other AWS resources) in your AWS account(s). The TDT CloudFormation Templates create stacks consisting of all principal framework components, along with related IAM policies and roles which are carefully engineered to comply with “best practices” (such as a “least privileges” approach to permissions).
The TDT CloudFormation Templates also optionally provide for automatic creation of a VPC, its subnets, and all required standard VPC-oriented resources, as well as optional creation of a source database cluster (consisting of either a sample database provided by Treehouse for a quick trial/POC, or your own database and data).
Simply put, TDT is a Cloud-native, turnkey solution that can eliminate months (or even years) of research and development time and costs and allow customers to be up and feeding data to an actuarial analytics architecture in minutes.
Visit Treehouse Software on the AWS Marketplace for all of our Cloud offerings…
by Joseph Brady, Director of Business Development at Treehouse Software, Inc.; Dan Vimont, Director of Innovation at Treehouse Software, Inc.; and Eleonora Savova, Cloud Solutions Architect at Treehouse Software
Today’s infectious enthusiasm about Machine Learning (ML) and Artificial Intelligence (AI) has become one of the prime motivators for customers wanting to move enterprise data to the Cloud. Snowflake is the platform of choice for many of these enterprises looking for a Cloud platform onto which they can mobilize data at near-unlimited scale and performance, and tap into advanced ML/AI.
Snowflake’s unified platform is designed for secure development and deployment of Machine Learning (ML) and Large Language Models (LLMs). Here are some of the most exciting new Snowflake AI/ML technologies on offer…
Snowflake Cortex AI: Efficiently process data at scale using cutting-edge models in Snowflake Cortex AI. Using serverless SQL and Python functions, it is easy to process data using fine-tuned models or foundation models such as Snowflake Arctic, Meta Llama 3, Mistral Large, Reka Core, and more.
Snowflake Cortex AI Chat Services: Get answers from analytical tables such as sales transactions without writing any SQL with Cortex Analyst (public preview) text-to-answer service. Quickly find answers hidden among a large set of documents using Cortex Search (public preview) fully managed hybrid search and retrieval service.
Snowflake ML: Run distributed feature engineering and custom model training using popular python libraries. Manage features and models at scale with Snowflake Feature Store (public preview) and Model Registry. Democratize predictive model development by using SQL functions that abstract complexity of ML algorithms.
Snowflake also provides optimized storage that includes unstructured, semi-structured, and structured data together with near-infinite scale. The platform gives fast and efficient access, optimized compression, and secure data — all automated. Customers can work with data on-premises, or in open table formats, thus removing lock-in, which allows adaptation to any current or future architectural patterns.
Video: Using AI Within Snowflake For Everyday Analytics…
First things, first…
Before you can start using Snowflake’s AI, ML, and LLM capabilities, you must first load your data into Snowflake. This is where Treehouse Dataflow Toolkit (TDT) comes in with a state-of-the-art, fully automated offering. Working in tandem with Rocket Data Replicate and Sync (RDRS), TDT assures highly-available, auto-scalable, and event-driven data transfers from Kafka pipes to Snowflake, delivered as a set of proprietary microservices. TDT stands out with its strict adherence to Snowflake’s and AWS’s recommended best practices for massive data loading, making it MUCH more than just a “connector”.
The greatest “value added” our customers see among all TDT’s functions and features is our Snowflake connectivity. TDT’s innovative Lambda-based microservices approach loads data into Snowflake tables, which are architected to retain the entire history of source data ever since the source-to-target synchronization began (perfect for time-based trend/predictive/prescriptive analytics).
Simply put, TDT is the self-contained, turn-key solution that gets your valuable data into Snowflake today, eliminating months, or even years of research and development time/costs. TDT’s high-speed, massive data movement to Snowflake only takes minutes to ramp up.
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