Data warehouse vs feature store
How data warehouse is different from database?
A database stores the current data required to power an application.
A data warehouse stores current and historical data from one or more systems in a predefined and fixed schema, which allows business analysts and data scientists to easily analyze the data..
Is a feature store just a database?
Internally, existing feature stores are all dual-database systems, containing a columnar data store to store historical (offfline) feature data, and an online (low-latency) row-oriented data store to serve precomputed features to online applications..
What is a data feature store?
Feature stores capture features from enterprise data warehouses or streaming applications in an online and offline store, syncing the values between the two stores.
Those features can then be retrieved and served for model training jobs or inference applications..
What is data warehouse and features?
A data warehouse is a location where businesses store critical information holdings such as client data, sales figures, employee data, and so on. (DW) is a digital information system that links and unifies massive amounts of data from numerous sources..
What is the difference between data warehouse and 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.Apr 4, 2023.
What is the difference between data warehouse and operational data store?
Operational data stores contain only the most current operational data, providing a useful snapshot of business operations as they are in the moment.
Data warehouses are designed to store massive amounts of historical data useful for performing large-scale analysis on complex data sets..
What is the difference between feature store and data pipeline?
Transform: In machine learning applications, data transformation pipelines absorb raw data and transform it into usable features.
Feature Stores are responsible for managing and orchestrating them..
- Feature store and vector database are ultimately two very different pieces of infrastructure.
Features stores tend to be a repository for curated features, while vector databases are for large-scale similarity searches. - Feature stores are partly data marts for Features — from the perspective of a Data Scientist.
However, they typically also serve features at low latency to online applications.
That functionality is not found in your data warehouse.
Apr 4, 2023Data warehouse platforms mainly support the Structured Query Language (SQL) application programming interface (API), while feature store
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.
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.
The Data Warehouse Is One Input to The Feature Store
Both platforms are a central store of curated data used to generate insights into the data. Both platforms have pipelines (ETL and feature pipelines Feature Store as A Dual Database
The main architectural difference between a data warehouse and a feature store is that the data warehouse is typically a single columnar database Detailed Comparison
In the table below, we see an overview of the main architectural differences between feature stores and data warehouses Feature Data Should Be Validated Before Ingestion
The table also shows the differences in the types of data stored, as well as how the data is stored, validated, and queried Using The Feature Store to Create train/test Data
Data scientists are one of the main users of the feature store Online Feature Store
Online applications use the online feature store to retrieve feature values with low latency to build feature vectors that are sent to models for predictions Feature Statistics to Monitor For Feature Drift and Data Drift
Descriptive statistics (e.g., mean, standard deviation) for features are also useful when identifying data drift in online models Time-Travel
Temporal databases support time-travel: the ability to query data as it was at a given point-in-time or data changes in a given time-interval Feature Pipeline
Data warehouses typically have timed triggers for running ETL jobs (or data pipelines) to ingest the latest data from operational databases, message queues What is a feature store in data science?
The feature store is the data warehouse for Data Science — it is a central vault for storing documented, curated, and access-controlled features that can be used across many different models
The feature store ingests data from the Enterprise’s many different sources after transforming, aggregating, and validating the data
What is the difference between a data warehouse and a feature store?
The main architectural difference between a data warehouse and a feature store is that the data warehouse is typically a single columnar database, while the feature store is typically implemented as two databases:
What is the difference between a data warehouse and data Lakehouse?
Both platforms can be designed to scale-out on commodity hardware and store large volumes of data, although typically a data warehouse stores only relevant to analysis (modern data lakehouses are designed to store large volumes of data more cost efficiently)
×A feature store is a data warehouse of features for machine learning. It is dual-database, with one serving features at low latency to online applications and another storing large volumes of features. A data warehouse, on the other hand, is a central repository of data that is used for reporting and data analysis. 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.,A feature store is a data warehouse of features for machine learning. Differently from a data warehouse, it is dual-database: one serving features at low latency to online applications and another storing large volumes of features. Learn how Data Scientists leverage this capability in production-deployed models. commentsHowever, 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.
