the database for real-time decisions

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FeatureBase powers real-time analytics and machine learning applications, making data immediately accessible, actionable, and reusable. By eliminating time-consuming and costly pre-aggregation, FeatureBase unlocks your data to drive instant decisions.

Using FeatureBase—

Purpose-built feature storage that automatically converts all of your data into a machine-native format.

Topic Stream Data Source Real-time Decisioning Application Analytics Model Training Feature Storage Store + Manage Features Monitoring and Observability Auth + User Management Feature Exploration (Python, SQL, Go) Feature Extraction Store + Manage Features Consume/Output Feature Extraction Feature Extraction TAP TAP

Extract Features

Our taps extract features from raw data at the source — even if your data is in a multitude of data centers — and pull those features into FeatureBase. Feature extraction can be executed server-side or client-side, while client-side extraction reduces the amount of data transferred. FeatureBase can ingest features through continuous updates (streaming) and batch, pulls from sources such as Kafka, and ingests data formats such as CSV and JSON.

Store + Manage Features

Features are stored in-memory in a high performance machine-native format. Users experience low-latency exploration of features and are able to manage users and infrastructure via the control plane. FeatureBase employs a purpose-built feature storage solution rather than cobbling together existing data-centric technologies.

Consume Features

Leverage our APIs—like HTTP, gRPC (Python), and Postgres Wire Protocol (SQL)— for end-user applications including real-time decisioning, analytical applications, and model training. Queries and transforms happen within this step in your model or code.

Product Overview

Core Technology

FeatureBase simplifies, accelerates, and improves control over data to power real-time analytics and AI. FeatureBase is an overlay to conventional big data systems that automatically extracts features, not data, from each of the underlying data sources or data lakes and stores them in one centralized feature storage platform. FeatureBase maintains up-to-the-millisecond data updates with little to no upfront data preparation. This is achieved by reducing the dimensionality of the original data, effectively collapsing conventional data models (such as relational or star-schemas) into a highly optimized format.


Bring on the Big Data

Enables ultra low-latency, petabyte-scale analytics.

No More Pre-Aggregation

Allows real time many-to-many JOINs without precomputation.

No More Batch

Enables transformations directly in your model, code, or queries.

Make Decisions On Your Most Current Data

Supports near-instant updates.

Stop Managing Copies

Extracts features automatically without moving data.

Say "Goodbye" to In-Memory Limitations

Provides tiered storage for different data SLAs.

Massive Resource Savings

Reduces data footprint, often by 10x or more.


FeatureBase is beneficial for organizations that need to access large quantities of real-time data events each day joined with many fragmented, terabyte-scale data sources. FeatureBase excels at complex analytical workloads where source data is fragmented across silos, and where a user or machine wants to apply a number of filters or criteria to a query.


We have a variety of ingest plugins including bulk SQL loaders, Kafka connecters (supporting Avro and the confluent schema registry), and change data capture (CDC) plugins. We are constantly adding new ingest plugins, so please ask us about our latest additions. Our experienced customer engineering managers are trained to bring these integrations, customized for your complex data environment, to production during the implementation period.


FeatureBase stores data in a format that extracts features at the original data source and then homomorphically compresses them for transmission and storage. The core format allows for granular scans at a feature-by-feature level rather than a columnar or tabular data format. This enables breakthrough analytical performance, allowing for unprecedented iteration speed in feature engineering on the totality of large data sets.


FeatureBase can be deployed in hybrid environments, and is priced based on your organization’s unique consumption demands.

Molecula also offers a Managed Service option for FeatureBase. This offering is designed for companies that need fast, scalable analytics and don’t have the bandwidth to learn and implement a new solution.

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Ingest data from a Kafka topic into FeatureBase

Kafka Connect

Ingest data via Kafka Connect into FeatureBase


Ingest data from MySQL database into FeatureBase


Ingest data from SQL Server database into FeatureBase


Ingest data from Snowflake data warehouse into FeatureBase


Ingest data from Cassandra database into FeatureBase


Ingest data from Teradata data warehouse into FeatureBase


Ingest Spark data streams into FeatureBase


Ingest Parquet files into FeatureBase


Ingest files from your S3 instances into FeatureBase

Big Query

Ingest data from your Big Query data warehouse into FeatureBase

ODBC Driver

Ingest data from any database into FeatureBase via ODBC


Monitor your instance with Prometheus


Monitor your instance with Splunk


Monitor your instance with Jaeger


Collect metrics about your instance with StatsD


Monitor your instance with OpenTracing


Monitor your instance with Datadog

Jupyter Notebook

Query your FeatureBase data from your Jupyter Notebook


Create Pandas data frames from FeatureBase


Query your FeatureBase data using Snowflake


Query your FeatureBase data using RStudio


Query your FeatureBase data using RAPIDS


Leverage our GRPC API to connect custom applications



Visualize and query your real-time, mission-critical data



Explore, analyze, and share real-time business analytics

Read more about our product and related resources—

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Calculate Your TCO with Molecula—

Molecula's novel approach to data access is game changing for your machine-scale analytics and AI. By simplifying real-time analytics and AI infrastructure Molecula can reduce footprint by 60-90% and save you time, resources, and headaches. Come see for yourself.