Databricks (Spark SQL) — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Databricks is a data and AI company that provides a cloud-based data platform. Spark SQL is a module within Apache Spark that integrates relational processing with Spark's functional programming API, allowing users to query structured data using SQL or a familiar DataFrame API. Databricks heavily leverages and optimizes Spark SQL as a core component of its Lakehouse Platform.

Domain Classification & Tags

Parallel Processing

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Varies significantly based on cluster size, data volume, and query complexity. Can range from thousands to hundreds of thousands of rows/sec or MBs/sec for analytical queries.

Payload Size: Typically large, ranging from MBs to GBs per query result set.

Processing Latency: Highly variable, from sub-second for simple queries on small datasets to minutes for complex analytical queries on petabytes of data. Often 1-10 seconds for typical interactive queries.

Write Performance Profile

Throughput: Varies significantly based on cluster size, data volume, and write operation (e.g., INSERT, MERGE). Can range from thousands to hundreds of thousands of rows/sec or MBs/sec.

Payload Size: Typically large, ranging from MBs to GBs per write operation.

Processing Latency: Highly variable, from seconds for small writes to minutes for large-scale ETL operations. Often 5-30 seconds for typical batch writes.

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

open_in_new Standalone Tab

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Databricks (Spark SQL).

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.