Databricks — 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 unified platform for data engineering, machine learning, and data warehousing. It offers services like Foundation Model APIs for LLM inference with provisioned throughput and supports various data types for SQL operations.

Domain Classification & Tags

Analytics Cloud Azure AWS Big Data

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: 32,941,245 QphDS @ 100TB (TPC-DS benchmark, query throughput)

Payload Size: up to 100TB (data warehousing benchmark)

Processing Latency: 3-30 minutes (ad-hoc queries), ~3 seconds (some specific queries)

Write Performance Profile

Throughput: 90% reduction in data processing time

Payload Size: scales to 140x original volume

Processing Latency: improved from nightly batch to near real-time

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

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.

Associated with 1 verified reference architecture blueprint:

Fraud Detection and Security Logging

Cassandra's capability to ingest and store massive amounts of event data, such as transaction logs and security events, makes it a strong candidate for fraud detection systems and security information and event management (SIEM) solutions that require real-time analysis of large datasets.

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.