Businesses today increasingly rely on artificial intelligence (AI) and machine learning (ML) to drive growth and profitability. Unsurprisingly, the global ML market has a compound annual growth rate (CAGR) of 39.2%. Also, a survey by McKinsey shows that 50% of respondents use AI in at least one business area.
However, as the complexity and volume of data increase, ensuring that investments in AI and ML pay off is becoming ever so challenging. Organizations struggle to maintain customer expectations as the demand for low-latency and high-quality applications increases.
Consequently, the need for real-time data processing makes building, maintaining, and deploying ML applications more demanding as organizations come to grips with intricate Extract, Transform, and Load (ETL) pipelines. In addition, they must create automated and collaborative frameworks to bring disparate teams together to ensure effective collaboration and quick deployment.
Two crucial components of such integrated systems are data warehouses and feature stores. This article discusses what they are and their differences in detail. The information will provide better insights into how they are helping mitigate the challenges companies face when dealing with ML and AI projects.
A feature store is a data system that acts as a central repository for all critical features that data scientists may use to train their models or serve critical applications in real-time to deliver immediate customer results.
Data scientists use features or variables to build predictive ML models. For example, a product recommendation application may use a customer’s purchases made in the last week as the primary feature to predict what the customer will buy in the future.
Feature engineering is the process through which data scientists compute such variables from raw data for model-building. Commonly, they use automated ETL pipelines to fetch relevant data and apply transformations to derive feature values.
Feature stores are essential as they help data scientists store complex features and quickly retrieve them when required for model training. Also, ML systems in production require access to relevant features to serve customers.
For example, a recommendation system needs access to the latest feature set that tells crucial customer behavior information. Computing the features manually every time a customer uses the application will result in poor service as it will take time to recalculate everything from scratch. The ML application can quickly access the feature values with a feature store and provide relevant recommendations in minimal time.
A data warehouse is a central repository that stores structured data from specific sources, such as an enterprise resource planning (ERP) or a customer relationship management (CRM) system. Commonly, the data is domain-specific. For example, a warehouse may only contain marketing data imported from the company’s CRM system in a pre-defined format.
Data engineers may use complex ETL pipelines to transform raw data into meaningful information before storing it in the warehouse. Since the data is already clean and usable, users can directly extract relevant data to create analytical reports for decision-making.
Now that we know what data warehouses and feature stores are, let’s look at the critical differences between them to understand better how they work together to deliver value in ML projects.
From the above definitions, we can infer that data warehouses act as inputs to feature stores. The data you need to build features comes from the warehouse. Of course, it would involve additional transformation pipelines to convert data in a warehouse into specific features. As such, both components use ETL pipelines to function. Also, they both act as a repository with relevant metadata to store critical information and allow data shareability across teams.
However, the two components differ in terms of end users, data types, types of ETL pipelines, platforms, architecture, monitoring and validation methods, access management, type of metadata, and governance.
Although data warehouses and feature stores retain crucial information, they differ in their purpose. Data warehouses commonly serve analysts who create detailed business reports as part of a company’s business intelligence (BI).
In contrast, feature stores serve data scientists who make predictive ML models for several functions. For example, a data scientist may create a model to predict sales in the next quarter. They would require specific customer and sales-related variables for training their models. They can fetch such variables from a feature store without building them from scratch.
However, data scientists can also use a data warehouse to get additional data on a specific subject for better insights. In fact, anyone who wants to analyze a particular problem can use the warehouse as their source. As such, a data warehouse has a broader user base than a feature store.
As discussed earlier, feature stores contain data related to specific variables important for ML model training, while data warehouses store domain-specific data. However, the difference runs deeper in terms of structure.
Data warehouses store data in relational databases with a well-defined schema. For example, a data warehouse with financial data may mostly contain numerical values in tables with specified columns. The clean structure gives analysts the luxury of quickly querying and finding relevant information.
Feature stores contain feature values that can be quantitative or categorical. For example, gender can be a relevant categorical variable for customer segmentation. Feature values can be strings, such as “male” and “female.” Usually, a feature store’s output is a vector or tensor containing multiple features for model training.
ETL pipelines for data warehouses primarily extract data from CRM or ERP systems, apply the relevant transformations to ensure the data format agrees with the schema defined for the warehouse, and finally load the clean dataset into the warehouse.
Feature stores also use ETL pipelines that extract data from the warehouse or any other source system, apply transformations, and load the features into the store. However, the nature of feature store transformations differs slightly from a data warehouse.
Transformations for a feature store may involve computing aggregates or other sophisticated computations to create variables as part of the feature engineering process. Transformations in data warehouses ensure that the data is clean, accurate, and understandable. Although transformation pipelines for data warehouses may still apply aggregations, their complexity is likely lesser than feature store aggregations.
As data warehouses and feature stores differ in architecture (discussed later in the article), so do the platforms used to build them. Some popular data warehouse platforms include Google BigQuery, Amazon Redshift, and Snowflake.
Popular feature store platforms include the open-source utility called Feast. Also, Airbnb’s Zipline and Uber’s Michelangelo Palette are robust feature store platforms best suited for low-latency systems where you require quick feature serving to feed online applications, such as recommendation apps. Qwak’s feature store is also a robust feature development and deployment platform.
Data warehouse platforms mainly support the Structured Query Language (SQL) application programming interface (API), while feature store platforms support Python, SQL, and Java/Scala. Feature stores may also use domain-specific language (DSL) to apply transformations.
Although feature stores act as data warehouses for features, they differ starkly from an actual warehouse in terms of architecture.
As discussed, a data warehouse consists of external sources such as a CRM or ERP system, which acts as the input layer to the warehouse. Next, you have the integration layer, where relevant ETL pipelines channel the data from source systems to the warehouse. Lastly, users access the data for analytical purposes from the warehouse in the data mining layer.
Also, organizations commonly use a data warehouse to maintain several data marts - subsets of data related to a specific business function. For example, a data warehouse can contain financial and marketing data. As such, an organization can create two data marts (separate physical databases) - one to store financial data and the other for marketing data. The practice ensures that a specific team can quickly access relevant information for decision-making.
However, feature stores have the data warehouse as the input layer from which data scientists fetch data for feature engineering. The integration layer is where ETL pipelines extract data from a warehouse, apply transformations to convert raw data into meaningful features, and load them into the feature store.
Additionally, a feature store has an offline store that contains features for use by data scientists for ML model training. Also, it has an inference store that serves features to online models for making instant predictions. Different tools exist for building offline and inference stores. Commonly, Redis is a popular choice for an inference store, while tools like Amazon S3 are suitable for an offline store.
Monitoring a data warehouse involves keeping track of ETL pipelines to ensure they’re working as expected and building robust alarm systems to notify administrators whenever a job fails.
In contrast, feature store monitoring is more complex, requiring users to maintain feature relevance and ensure transformation pipelines perform efficiently. More precisely, you must monitor features to check data or concept drift.
Data drifts when feature values show statistical discrepancies, such as different distributional properties and patterns, indicating a fundamental change in a feature’s behavior. For example, customer sales data may show a different pattern in different seasons.
Concept drift occurs when the relationship between the feature input and the outcome you want to predict no longer holds or changes in a certain way. For example, a customer’s average purchases may no longer predict future sales.
Such changes degrade an ML model’s performance if the data scientist fails to re-train the model with new feature values. Effective feature store monitoring involves building notification systems to monitor data and concept drift.
Validation methods for data warehouses include checking data completeness and accuracy. You can perform such checks by extracting a subset of data and running queries to see if the results agree with specific standards. For example, a financial analyst may run simple SQL queries to check historical revenue figures and see if it complies with other records present in the ERP system.
Feature store validation includes verifying the statistical properties of feature values and determining whether they fall within a specified range. You can run validation checks using tools like Great Expectations, Deequi, TensorFlow Data Validation (TFDV), etc. You should also check for missing and null values, as they can damage model performance significantly.
Organizations must strike a balance between accessibility and privacy. As data warehouses can have several users from different teams, accessibility protocols must ensure that only relevant groups can access specific data. For example, sensitive financial data shouldn’t be accessible to the marketing or sales team.
Access permissions for feature stores allow data scientists to access the offline and online stores as they are the primary users. However, administrators may restrict access for some members to specific features containing sensitive information.
Metadata for a data warehouse provides information regarding data types, owners, data lineage, data lengths, primary keys, etc. Also, it may include domain-specific information so other teams can understand what a specific table is about. For instance, metadata for financial data tables can explain what each column in a particular table means. As data warehouses focus on increasing collaboration across teams, such metadata is crucial for gaining domain-specific knowledge.
Metadata in the feature store can include information about a feature’s owner, creation date, purpose, model results, version, etc. Versioning is essential as it provides data scientists with vital information regarding the feature’s relevance, historical changes, and creators.
The above discussion highlights the differences between a data warehouse and a feature store. It also refers to the importance of the two components in any ML development lifecycle. In particular, a feature store with efficient monitoring mechanisms can significantly boost your ML development efforts and reduce deployment issues.
Qwak is a state-of-the-art ML engineering platform that bridges the gap between model-building and deployment. Its robust feature store ensures re-usability, consistency, and accuracy across offline and online applications. Its built-in transformation engine lets you quickly perform feature engineering by easily defining transformation pipelines to automate the ML development process.
It also features a model registry that maintains critical information regarding the models you deploy and comes with a monitoring system, allowing you to identify and fix issues proactively.
So download Qwak now and boost your returns on investment (ROI) from ML projects.