The Power of Tecton for Batch Machine Learning

In the dynamic landscape of machine learning (ML), understanding the multi-faceted functionality of a platform like Tecton can be a game-changer. While Tecton shines for real-time use cases, it’s designed to handle the requirements of both batch and real-time use cases.

Tecton is a powerful platform for providing features to your real-time models and updating them with fresh data as events stream in, but it’s also important to note that not all ML features require immediate updates. Tecton is designed from the ground up to unify the processing of batch, streaming, and real-time data to support the unique requirements of machine learning use cases. If your models don’t need streaming or real-time data, Tecton will still provide significant advantages when dealing with batch data sources. Let’s pull back the curtain on Tecton’s full potential and look into its handling of batch online and batch offline scenarios.

The overlooked heroes: batch online and offline scenarios

The terms “batch online” and “batch offline” might not sound exciting, but they play a critical role in ML model development. To put it simply:  

• “Batch online” involves building features from batch data and making these features available online for real-time processing or inference
• “Batch offline” involves building features from batch data and making these features available only for batch processing or inference

Tecton’s toolkit, beyond its real-time processing prowess, is specifically designed to handle these scenarios efficiently.

Tecton serves as a facilitator, streamlining the training and prediction processes of ML models. It’s like having an incredibly efficient assistant at your disposal, ensuring every stage runs like clockwork. From fetching the required data from the Tecton offline store to processing this data, the Tecton SDK ensures all steps are carried out seamlessly. This feature may not make headlines, but it undoubtedly enhances your productivity. Who wouldn’t appreciate that?

Peeking under the hood: Tecton’s batch transformations

One of the ways Tecton outshines other feature stores is how it optimizes data processing. Tecton’s aggregations operate on deltas, resolving values at the time of request. This mechanism leads to significant efficiency gains. For instance, if only 1% of your customers made a transaction yesterday, Tecton writes new records for only that 1%. In contrast, most other feature stores would rewrite records for all customers, even when no changes occurred. This latter approach wastes time and resources, while Tecton’s method drastically reduces unnecessary computations, storage, and retrieval.

Let’s illustrate this with a concrete example. Imagine a situation where you need to calculate a customer’s age in days from their birth date and update this information daily. As mentioned above, most feature platforms aren’t efficient in handling this process. If they were to calculate age daily and save it to the feature store for a million customers, it would be expensive but wasteful. This approach would generate 365 million records per year, despite the fact that the computed age in years for one million customers only truly changes a million times over the same period.

With Tecton, an ”on-demand feature view” creates a single birth date record per customer, amounting to a million records in total. Then, whenever a model needs age data, Tecton simply calculates the age by subtracting the birth date from the date of the feature request. This method eliminates the need to constantly write and update age data. 

Here’s where it gets even more interesting: Tecton’s batch pipelines interact with the customer’s data warehouse or data lake, enabling data retrieval and transformation within the customer’s chosen environment. And the best part? Tecton can handle both Snowflake and Spark setups. Whether you prefer Snowflake’s integrated compute-storage system or Databricks’ decoupled architecture with a data lake and a Spark cluster, Tecton ensures you get the information you need, when you need it.

Maximizing batch data transformations with Tecton: More than just a feature store

Batch data transformations might not have the same razzle-dazzle but, due to complete data access, they allow for more complex operations compared to stream processing, which works with small data snippets. In this space, where batch transformations introduce intricate orchestration potential, Tecton is far more than just a traditional feature store. Let’s explore how, for batch-batch transformations, it goes beyond the base-level utility of a conventional feature store or platform.

Easily create time-window-based features

Manually computing time-window-based features is fraught with the risk of errors. Computing time-window-based features, such as sales over a 7-day, 30-day, and 60-day period, can be a tedious and error-prone task if done manually. With Tecton, however, this process is simplified. All the user needs to do is provide the desired time intervals—7, 30, and 60 days—and Tecton generates the corresponding sales features. This removes the need for the user to grapple with any complex windowing logic and dramatically reduces the risk of mistakes.

Backfills, solved

Populating historical feature values—a process called backfilling—can be tough. It’s easy to make mistakes (like using the wrong data subsets), which can waste resources and potentially overload the system. In some cases, failures might even need a total do-over, which can be costly and time-consuming. Tecton, however, offers a simple and precise solution to these problems. One simply needs to instruct Tecton on feature creation, then specify the feature_start_time, and Tecton will handle loading the historical values. If the batch features are used in a real-time inference use case, Tecton will also prime the online store with the most recent values. In short, Tecton makes the hard task of backfilling manageable, ensuring your data is accurate, up-to-date, and ready to power your models with trustworthy signals.

Mitigating skew traps & improving model precision

Tecton’s robust framework significantly eases the creation of features and execution of point-in-time accurate joins, substantially reducing the risks of skew traps. These traps often surface when users leverage handwritten code without a feature store, potentially compromising results. Tecton effectively circumvents these issues, ensuring data results of the highest reliability and accuracy. By adeptly managing skew, it ensures your data models are a close reflection of reality, leading to dependable insights and more accurate predictions, improving the real-world precision of data models. 

Superior data quality monitoring

Who doesn’t appreciate a good bar chart showcasing the quality and reliability of their data? Tecton’s data quality monitoring (DQM) capabilities provide users with exactly that. This feature allows users to trust their data, communicate their findings effectively, and promptly address any issues that might crop up.

Feature sharing, discoverability & reuse, simplified

Tecton is also your reliable tool for effortless feature creation, management, and reuse. Picture a clean repository where you can store and reuse feature definitions, and a distinct store for your finished creations—quite appealing, isn’t it? This streamlined system fosters collaboration and productivity, making the sharing and reuse of features across different models and teams second nature. Plus, it guarantees uniformity in model behavior. With Tecton on your side, expect swift deployment across various batch use cases, quicker iterations, improved feature discoverability, and enhanced reusability of features.

Robust performance & security

Tecton prioritizes robust security and scalable performance, making it a preferred choice among large organizations. Tecton maintains control over data while delivering the agility of a SaaS solution, integrating seamlessly with Single Sign-On systems, ensuring extensive access control, and using AWS-standard encryption and IAM permissions. Importantly, it aligns with industry standards, being SOC 2 Type 2 compliant and enabling adherence to GDPR requirements.

Unleashing Tecton’s true potential: The complete feature platform for production ML

Due to the aforementioned advantages, Tecton users are able to experiment, iterate, and fail faster than with any other approach. The swift transition from data pipeline creation to model deployment that Tecton enables means capturing value from models more rapidly, thereby accelerating overall ML operations.

For organizations dealing primarily with batch-batch use cases, Tecton is more than just a feature store. It offers an expansive range of capabilities, including efficient feature creation, robust security measures, and advanced data quality monitoring, fundamentally transforming your approach to batch data transformations. 

As you explore and transition into streaming and real-time data processing, the true power and potential of Tecton’s features will become even more apparent. No matter where your organization is on its ML journey, Tecton enables ML teams to embrace more advanced, efficient, and flexible data processing workflows.