TDT: Much more than a mere “data connector” for Snowflake

by Joseph Brady, Director of Business Development at Treehouse Software, Inc. and Dan Vimont, Director of Innovation at Treehouse Software, Inc.

Over the past few months, we have been rolling out information on Treehouse Dataflow Toolkit (TDT), a state-of-the-art, fully automated offering for data transfer from Kafka pipes to Analytics/ML/AI frameworks.  TDT is a set of proprietary microservices that assures highly-available, auto-scalable, and event-driven data transfers to your data science teams’ favorite analytics frameworks, such as Snowflake, Amazon Redshift, Amazon Athena/S3, Amazon S3 Express One Zone Buckets, as well as Amazon Aurora PostgreSQL, all the while adhering to AWS’s and Snowflake’s recommended best practices for massive data loading. Make no mistake, TDT is MUCH more than merely a “connector”.

In this blog, we will focus on how TDT handles data transfers to perhaps the most complex environment: Snowflake.  Out of all TDT functions and features, our Snowflake connectivity offers the biggest “value added” to customers, because Snowflake has quickly become a top choice for enterprises looking for a Cloud platform onto which they can mobilize data at near-unlimited scale and performance, and bring advanced ML/AI capabilities.

Snowflake overview video…

Connectivity using Snowflake’s best practices vs. traditional ODBC…

TDT’s innovative Lambda-based (microservices) approach enables faster data flow than any conceivable ODBC-based solution, which is the standard tool used for most “roll your own” approaches, or “we have a connector for that” offerings.  

To load massive quantities of data to a target, TDT uses Snowflake’s (hugely scalable) bulk load utilities—not ODBC. It is vital to note that Snowflake is NOT a relational (OLTP) database, so doing CDC transfers to these targets via ODBC (with update, insert, delete transactions) goes directly against “best practices” advice from Snowflake, and would almost assuredly result in unwieldy bottlenecks.

TDT loads data into Snowflake’s “delta tables”, which inherently retain the entire history of source data ever since the source-to-target synchronization began (perfect for time-based trend/predictive/prescriptive analytics). Again, TDT adheres to Snowflake’s best practices recommendation for pulling data from S3 for bulk loading massive quantities of data…

Publishing both bulk-load and CDC data to a reliable and scalable framework like Kafka allows you to maintain a broad array of options to ultimately feed your legacy data to any number of JSON-friendly ETL tools, target data stores, and data analytics packages (some of which have not even been invented yet!). 

The “build vs buy” question is put to rest…

The Snowflake-proprietary target DDL/metadata/resources that TDT automatically produces for the staging of data in Snowflake are of such complexity that it is easy to justify the “buy” option in the “build vs buy” conversations customers have. A decision by an enterprise not to use TDT, but instead to build its own Kafka-to-Snowflake solution, could result in any or all of the following:

• accumulation of technical debt
• extensive/unpredictable time to production
• ongoing resource planning to maintain home-grown technologies
• potential vendor lock for maintenance of custom-made technologies designed and developed by consultants
• managing a mix of manual and automated functions
• tracking cobbled together components created by multiple staff and consultants
• limited agility for future customization and innovation
• problems adhering to evolving best practices over time
• higher costs for future growth/scaling
• potential lack of proper security/ongoing security updates
• your organization has now become an enterprise software development company, whether or not you intended that, and whether or not you realized that!

Simply put, TDT is a self-contained, turn-key solution that can eliminate months, or years, of research and development time and costs. With TDT, high-speed and massive data movement to Snowflake takes minutes to ramp up.

Download the TDT AWS Partner Solution Brief to share with your team…

DOWNLOAD…

Treehouse Dataflow Toolkit (TDT) is Copyright © 2024 Treehouse Software, Inc. All rights reserved.

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Contact Treehouse Software for a Demo Today!

Contact Treehouse Software today for more information or to schedule a product demonstration.

So, You’ve Managed to Start Streaming Your Legacy Data into Kafka Pipelines… Now What?

by Joseph Brady, Director of Business Development at Treehouse Software, Inc. and Dan Vimont, Director of Innovation at Treehouse Software, Inc.

Treehouse Software is helping customers modernize their valuable enterprise data on Cloud and Hybrid Cloud environments without disrupting the existing critical work on their legacy systems. However, a new strategic imperative has been added to the modernization game—the requirement to utilize today’s advanced Analytics/AI/ML-friendly platforms, such as Amazon Redshift, Snowflake, Amazon Athena/S3, Amazon S3 Express One Zone Buckets, as well as Amazon Aurora PostgreSQL, where an ever-expanding array of AI/ML tools are available to generate vital insights from the customer’s data. Many of these customers are already using software tools provided by Treehouse, or other vendors to replicate their data into various target data stores, but also more crucially into Kafka pipelines (i.e., Amazon MSK, Confluent, etc.). Kafka is now the top choice for high-speed streaming of massive volumes of mission critical data, providing stable performance under extreme loads. This is especially valuable for enterprises that require up-to-the-second data delivery for use cases that include e-commerce, financial services, logistics, telecommunications, and government IT.

Traditionally, Treehouse customers utilized our data replication technologies to load legacy data into Kafka pipelines, and that was where our involvement generally ended…

However, once Kafka is designated as a target in the customer’s architecture, we have increasingly become involved in two questions: “What now?”, and/or “What is the best mechanism for us to rapidly transfer data from Kafka to advanced analytics platforms?” Our answer: Look no further than Treehouse Software!

Treehouse Software brings a state-of-the-art, fully automated offering for data transfer from Kafka pipes to Analytics/ML/AI frameworks: the Treehouse Dataflow Toolkit (TDT).  TDT is a set of proprietary microservices that assures highly-available, auto-scalable, and event-driven data transfers to your data science teams’ favorite analytics frameworks, all the while adhering to AWS’s and Snowflake’s recommended best practices for massive data loading, thus assuring shortest and surest loads. Additionally, TDT provides a frictionless and instant implementation, accelerating your path to deep data insights for optimizing business processes.

Why do AWS’s and Snowflake’s best practices recommend against using ODBC?

Your data science teams need large quantities of the very latest data in near-real-time, and ODBC doesn’t really do the job, offering only single-threaded, difficult to scale pipes. By contrast, TDT’s approach not only keeps things up-to-date faster than any conceivable ODBC-based solution, but the “delta tables” into which it loads data also inherently retain the entire history of source data ever since the source-to-target synchronization began (perfect for time-based trend/predictive/prescriptive analytics).  To load massive quantities of data to a target, TDT uses the target vendors’ (massively scalable) bulk load utilities—not ODBC. It’s vital to note that Snowflake and Redshift are NOT relational (OLTP) databases, so doing CDC transfers to these targets via ODBC (with update, insert, delete transactions) goes directly against “best practices” advice from the vendors, and would almost assuredly result in unwieldy bottlenecks.

What if my data is not on a mainframe?

No worries. Treehouse Software’s messaging is primarily mainframe-centric, since that has been our area of expertise and bread-and-butter for over 40 years. However, data movement is data movement, and if your mainframe, or non-mainframe, data is being pumped to a Kafka pipeline, TDT will take it from there. When a data replication tool publishes both bulk-load and CDC data in JSON format to a reliable and scalable framework like Kafka, it sets the stage for TDT to feed legacy data to any number of JSON-friendly ETL tools, target data stores, and the latest (or yet to be invented) data analytics packages. TDT is the turn-key solution for the easiest and fastest implementation of Kafka data transfer…

TDT allows you to quickly ramp up your data analytics game by providing a rapid flow of data fresh off your enterprise data systems.

Download: TDT AWS Partner Solution Brief to share with your team…

DOWNLOAD…

Treehouse Dataflow Toolkit (TDT) is Copyright © 2024 Treehouse Software, Inc. All rights reserved.

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Contact Treehouse Software for a Demo Today!

Contact Treehouse Software today for more information or to schedule a product demonstration.

Quick Read: AWS Partner Solution Brief – Treehouse Dataflow Toolkit

by Joseph Brady, Director of Business Development at Treehouse Software, Inc.

Treehouse Software and AWS are collaborating on several AWS-centric initiatives in the coming months. The focus of these efforts is to market our new Treehouse Dataflow Toolkit (TDT), a set of microservices that provides the turn-key solution for transferring data from Kafka into advanced Analytics/AI/ML-friendly targets, such as Amazon Redshift, Snowflake, Amazon Athena/S3, Amazon S3 Express One Zone Buckets, as well as Amazon Aurora PostgreSQL. We have worked with an AWS Marketing Manager to create the following TDT AWS Partner Solution Brief downloadable PDF that provides a one-minute overview of TDT, its benefits, and resource links for your team…

DOWNLOAD…

Treehouse Dataflow Toolkit (TDT) is Copyright © 2024 Treehouse Software, Inc. All rights reserved.

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Contact Treehouse Software for a Demo Today!

Contact Treehouse Software today for more information or to schedule a product demonstration.

Just what is the new Treehouse Dataflow Toolkit, and why is it the perfect tool for transferring mainframe data to Cloud-based data analytics and AI/ML frameworks?

by Joseph Brady, Director of Business Development at Treehouse Software, Inc. and Dan Vimont, Director of Innovation at Treehouse Software, Inc.

Introducing Treehouse Dataflow Toolkit…

Many enterprise customers and Cloud platform partners have been coming to Treehouse Software seeking the know-how and technology that enables state-of-the-art transfer of mainframe data to advanced analytics and ML/AI frameworks.  In response to this demand, we have designed the Treehouse Dataflow Toolkit (TDT), a set of proprietary microservices that assures highly-available, auto-scalable, and event-driven data transfers to your data science teams’ favorite analytics frameworks.

These customers either already have, or are in the process of acquiring, software tools that replicate their data into Kafka pipelines (i.e., Amazon MSK, Confluent, etc.). Our new and innovative offering, TDT, provides the turn-key solution for getting this data from Kafka into advanced Analytics/AI/ML-friendly targets, such as Amazon Redshift, Snowflake, Amazon Athena/S3, Amazon S3 Express One Zone Buckets, as well as Amazon Aurora PostgreSQL, all the while adhering to AWS’s and Snowflake’s recommended best practices for massive data loading, thus assuring shortest and surest loads.

Market snapshot… 

For years, Snowflake and Redshift have been providing “old school” data analytics functionality, and now they are both ramping up their support for ML and GenAI functionality.  They are generating the demand (and are doing a good job of it!).

As we’ve been hearing from our customers, it is not a question of, for example: getting their data to either Snowflake OR PostgreSQL OR Redshift, but instead to ALL OF THEM!  Each target environment has its own business justifications and reasoning.  Many sites will want to do this—send data not only to various RDBMS targets, but also to various Data Analytics targets.  The justification for TDT is in a customer’s desire to ramp up its Data Analytics game, quickly and easily with data fresh off the mainframe; and achieving business goals and results faster and at a much lower cost than building a solution themselves.

How does TDT Work?

When a mainframe data replication tool (provided by one of Treehouse’s partners) publishes both bulk-load and CDC data in JSON format to a reliable and scalable framework like Kafka, it sets the stage for TDT to feed legacy data from Kafka to any number of JSON-friendly ETL tools, target datastores, and data analytics packages (some of which may not even have been invented yet!).

1. We start at the source – the mainframe – where an agent (with a very small footprint) extracts data (in the context of either bulk-load or CDC processing).
2. The raw data is securely passed from the mainframe by one of our partner’s data replication tools that transforms the data into Unicode/JSON and publishes the results to a Kafka topic (in our example above, a topic in an Amazon MSK cluster).
3. TDT microservices consume the data from MSK/Kafka and land it in S3 buckets, where TDT’s proprietary crawler technology is used to automatically prepare landing tables, views, and additional infrastructure for various analytics friendly targets.  Then the mainframe data is loaded into Redshift, Snowflake, S3, or PostgreSQL (all the while adhering to AWS’s and Snowflake’s recommended “best practices” for massive data loading, thus assuring shortest and surest loads).  The inherent reliability and scalability of the entire pipeline infrastructure assures near-real-time synchronization between mainframe sources and the target tables, even with huge bulk-loads or transaction-heavy CDC processing.

History is enterprise GOLD…

TDT not only keeps things up to date faster than any conceivable ODBC-based solution, but the “delta tables” into which it loads data also inherently retain the entire history of source data ever since mainframe-to-target synchronization began.  So, for example, after TDT has been syncing a target table for 5 years, a data scientist now has 5 years’ worth of historical data to work with for trend analysis, predictive analytics, prescriptive analytics, ML, etc.

…but you also need the very latest data in near-real-time.

While TDT’s unique “delta-tables” approach offers comprehensive “history” for advanced analytics, the traditional need for up-to-the-second, current snapshots of mainframe datastores is also completely provided for.  Adhering once again to target vendors’ “best practices”, self-materializing views are provided to work with current data, not only in the JSON format in which it is stored, but also in fully-structured views which provide the more traditional look and feel of a SQL database.

Competitive differentiators between TDT and the “connectors”

• TDT provides massive scalability, thanks to the AWS Lambda infrastructure.
• TDT’s delta-table approach means unbeatable throughput (everything is just an INSERT, and it’s all going through the target vendors’ “best-practices” bulk-load utilities).
• TDT’s advanced crawler automatically provides JSON-manipulating VIEWs (often awkward to develop in a SQL context) and other target infrastructure.
• TDT adheres to AWS’s and Snowflake’s recommended best practices for connectivity.
• Other data replication tools that attempt to target Redshift and Snowflake use only generic ODBC connections for data transmission.
  • To load massive quantities of data to a target, TDT uses the target vendors’ (massively scalable) bulk load utilities—not ODBC. (Transaction-based ODBC transmissions afford a single, inherently difficult-to-scale pipe.)
  • Snowflake and Redshift are NOT relational (OLTP) databases, so doing CDC transfers to these targets via ODBC (with update, insert, delete transactions) goes directly against “best practices” advice from the vendors, and will almost assuredly result in unwieldy bottlenecks.
  • For Snowflake’s bulk-load functions to work, the development of additional Snowflake-proprietary objects (in addition to just target tables and views) is required; TDT’s crawler (DDL generator) function for Snowflake automatically generates statements to create these unique objects, along with the standard “create table”, “create view” statements.
• Loading hierarchical data in JSON format (to JSON-friendly environments like Snowflake, Athena/S3, Redshift, and PostgreSQL) is the best methodology for many situations, because it avoids having to split hierarchies out into parent/child/grandchild tables, which have to subsequently be pulled back together again via cumbersome SQL queries in order for the data to be effectively worked with.  NOTE that one of our customers has become so frustrated with working with “split apart” parent/child/grandchild structures in PostgreSQL that they want the ability to send their hierarchical data in JSON format TO POSTGRESQL (hence our recent addition of TDT support for PostgreSQL as a target).
• For users who still want to work with data in structured parent/child/grandchild format (yes, many people still may be reluctant to work with JSON in the context of SQL queries), TDT’s crawler (DDL generator) functions provide user-views that exactly emulate those old-school parent/child/grandchild structures.
• Production environment with TDT can be up and running in 2-4 weeks.
• TDT’s SaaS model advantages include: ease of implementation, shorter time to move into production, reliable uptime, instantaneous upgrades, pay-as-you-go billing based on usage metrics, and ease of integration with other SaaS offerings.

Treehouse Dataflow Toolkit (TDT) is Copyright © 2023 Treehouse Software, Inc. All rights reserved.

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Contact Treehouse Software for a Demo Today!

Contact Treehouse Software today for more information or to schedule a product demonstration.

Treehouse Software and Confluent offer High-Speed Mainframe Dataflow for Cloud-based Advanced Analytics

by Joseph Brady, Director of Business Development at Treehouse Software, Inc.; Dan Vimont, Director of Innovation at Treehouse Software, Inc.; and Ram Dhakne, Staff Solutions Engineer at Confluent

The message is clear from our customers—They want to modernize mainframe data on Cloud and Hybrid Cloud environments without disrupting the existing critical work on their legacy systems. They also want to tap into today’s advanced data analytics platforms such as Amazon Redshift, Snowflake, and Amazon Athena/S3, where an ever-expanding array of machine learning and artificial intelligence (ML/AI) tools are available to generate vital insights from their enterprise’s data.  Your data science teams are eagerly awaiting the arrival of critical data from your mainframes to supercharge their predictive analytics and generative AI frameworks.

Treehouse Software and Confluent: Two companies providing a reliable and scalable solution…

Confluent Cloud Data Streaming Service

As stated on the Confluent website, “Your team has better things to do than fight Kafka fires.” That is why Confluent Cloud was built as a 10x better, fully managed, and truly Cloud-native service for Apache Kafka, powered by Kora engine. Customers can take data streaming to the next level—sans the Kafka management and operational woes.

Confluent Cloud offers enhanced productivity, improved scalability, minimized downtime, and much more—all while reducing total cost of ownership. Confluent Cloud offers:

• Elastic scaling: Scale up and down quickly to meet fluctuating customer demand, without the ops burden that comes with scaling your data infrastructure
• Infinite Storage: Enable powerful use cases by never having to worry about Kafka retention limits again, while only paying for the storage used
• Built-in Resiliency: Ensure high availability and offload Kafka ops with 99.99% uptime SLA, multi-AZ clusters, and no-touch Kafka patches

Treehouse Software Mainframe CDC Data Replication

Enterprise customers have come to Treehouse Software, because the company brings not only proven mainframe data replication tools, but also deep subject matter expertise in mainframe technologies, as well as the know-how to target relevant offerings especially designed for ingesting data for advanced analytics and ML/AI.

The Rocket Data Replicate and Sync (formerly tcVISION) solution from Treehouse allows customers’ legacy mainframe environment to operate normally while replicating data on Cloud and Hybrid Cloud environments. The technology focuses on changed data capture (CDC) when transferring information between mainframe data sources and Cloud-based databases and applications. Through an innovative set of technologies, changes occurring in any mainframe datastore are tracked and captured, and ultimately published to various Cloud targets. Additionally, the Treehouse Dataflow Toolkit (TDT) set of microservices greatly enhances the architecture’s connectivity to high performance, non-relational, massive parallel processing datastores (Amazon Redshift, Snowflake, Amazon Athena/S3) that are primed to supply the most advanced ML/AI tools to data science teams.

Figure 1: In the longer-term picture, an enterprise can now keep its options open by propagating data to the highly reliable, very scalable Confluent Cloud that can be “subscribed to” by any number of current or yet-to-be-invented ETL toolsets and target datastores.

How does it work?

1. We start at the source – the mainframe – where an agent (with a very small footprint) extracts data (in the context of either bulk-load or CDC processing).
2. The raw data is securely passed from the mainframe to Rocket Data Replicate and Sync (RDRS) which speedily transforms mainframe-formatted data into Unicode/JSON and publishes the results to a Kafka topic in Confluent Cloud.
3. The Treehouse Dataflow Toolkit functions consume the data from Confluent and land it in S3 buckets, where Treehouse’s proprietary crawler technology is used to automatically prepare landing tables, views, and additional infrastructure for various analytics friendly targets. Then the mainframe data is loaded into Redshift, Snowflake, or S3 (all the while adhering to AWS’ and Snowflake’s recommended “best practices” for massive data loading, thus assuring shortest and surest loads).  The inherent reliability and scalability of the entire pipeline infrastructure assure near-real-time synchronization between mainframe sources and the target tables.

The very latest data—delivered!

Figure 2: RDRS, Confluent, and TDT work in tandem to easily replicate mainframe data and create target Snowflake resources for a wide variety of end use.

Figure 3: TDT adheres to Snowflake’s recommended “best practices” for bulk loading of mainframe data by using its COPY function to load data from S3

This Treehouse/Confluent framework allows data in staging tables to be constantly accruing the most current data, ideally suited for data scientists looking to do trend analysis, predictive analytics, ML, and AI work.  For business analysts and others who prefer structured data representations of potentially complex hierarchical data, this framework also automatically provides structured user-views, providing the look and feel of a SQL database.

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Contact Treehouse Software for a Demo Today!

Contact Treehouse Software today for more information or to schedule a product demonstration.