Warehouse User Guide > Sparkforce Platform > Sparkforce Platform > System Architecture > Components
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Components
The Sparkforce platform integrates with the following components to provide a comprehensive data development environment:
Component
Purpose
Apache Spark
A distributed data processing engine that exposes dataframe and SparkSQL APIs.
Spark
A thin RPC-client architecture designed to decouple client applications from the Spark cluster.
Actian Analytics Engine
A high-performance analytics engine integrated through the Spark Catalog API, but also accessible via JDBC/ODBC
MLflow
A tool for managing the machine learning lifecycle, including experiment tracking, model registries, and model serving.
Code Server
A browser-based VS Code IDE that includes preinstalled Python and PySpark libraries.
Data lake connectors
Preconfigured storage drivers supporting GCS, AWS, Azure, Iceberg, and Delta.
MLlib
Apache Spark's built-in machine learning library.
Note:  All external connections to the platform are secured with Transport Layer Security (TLS) and authenticated by using JSON Web Token (JWT) bearer tokens.
Last modified date: 06/02/2026