Microsoft Azure Synapse Analytics — Technology Profile & Architecture

Core Information

#core_information

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

Overview & Role

Azure Synapse Analytics is an enterprise analytics service that accelerates time to insight across data warehouses and Big Data systems. It brings together enterprise data warehousing, Big Data analytics, and data integration into a single unified experience. Synapse Analytics allows users to query data on their terms, using either serverless or dedicated resources at scale.

Domain Classification & Tags

Performance Profile

#performance_profile

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

Read Performance Profile

Throughput: Varies greatly, up to petabytes per second for analytical queries on large datasets. Dedicated SQL pools can achieve hundreds of thousands of queries per hour, while serverless SQL pools scale based on data size and complexity.

Payload Size: Typically large, ranging from MBs to GBs or even TBs for analytical queries. Individual row reads can be KBs.

Processing Latency: Typically seconds to minutes for complex analytical queries on large datasets. Sub-second latency for simple point queries on indexed data.

Write Performance Profile

Throughput: Varies greatly. Dedicated SQL pools can ingest hundreds of MB/s to GB/s using PolyBase or COPY statement. Serverless SQL pools are optimized for querying data in place, not high-volume writes to the pool itself.

Payload Size: Typically large, ranging from MBs to GBs for bulk loading operations.

Processing Latency: Seconds to minutes for bulk loading operations, depending on data volume and complexity.

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 Microsoft Azure Synapse Analytics.

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.