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Release · v2026.06-rc4

FINOS-CCC/CCC.DataWar.CP Capability Catalog

FINOS-CCC/CCC.DataWar.CP

Capabilities for Data Warehouse technologies, as defined by the FINOS Common Cloud Controls project.

Published by FINOS Common Cloud Controls

Install

OCI v1.1
$grcli unpack --repository finos-ccc/ccc.datawar.cp --tag v2026.06-rc4
Coordinate
oci.grc.store/finos-ccc/ccc.datawar.cp:v2026.06-rc4
Manifest digest
sha256:0e9c85f7375b7d422fe455b1f9ce17c5eda1ad456435a32b6dc1ca7cb0fc0add

Provenance

1 layer
Digest Media type Size
c17f7fdcfa6b… application/vnd.gemara.artifact.v1+yaml 8.7 KiB
Bundle config blob 
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CCC Data Warehouse Capabilities

Capabilities for Data Warehouse technologies, as defined by the FINOS Common Cloud Controls project.

ID
CCC.DataWar.CP
Version
v2026.06-rc4
Gemara version
v1.2.0
Author
FINOS Common Cloud Controls

Data Resilience

The Data Resilience group covers entries related to ensuring data availability, integrity, and sovereignty across its lifecycle. This includes replication, backup, recovery, region restrictions, and protection against data loss or corruption.

  1. CCC.DataWar.CP01 Centralized Data Repository

    Acts as a centralized repository where data from various sources is consolidated, making it easier to manage and analyze large volumes of data.

  2. CCC.DataWar.CP02 Optimized Query Performance

    Handles complex queries on large datasets efficiently using techniques such as indexing and partitioning.

  3. CCC.DataWar.CP04 Column Storage

    Stores data in columns rather than rows for efficient data retrieval.

  4. CCC.DataWar.CP05 SQL Based Querying

    Supports SQL based querying on the data sets with specific enhancements and optimization for data warehousing.

  5. CCC.DataWar.CP06 Data Types

    Ability to store processed structured and semi-structured data optimized for querying and analysis.

  6. CCC.DataWar.CP08 Materialized Views

    Ability to store results of a query into physical tables for faster data retrieval and improved query performance for complex queries.

  7. CCC.DataWar.CP15 Cross-Region Replication

    Ability to replicate data to multiple regions for high availability, disaster recovery and low-latency access.

  8. CCC.DataWar.CP16 View Creation and Access

    Supports the creation of views (can be logical or material) to abstract and simplify access to underlying data. Views can be created with custom queries to expose subsets of data. These views are accessible by users and applications with appropriate permissions.

Resource Management

The Resource Management group covers entries related to the lifecycle, configuration, and operational integrity of cloud resources. This includes resource exhaustion, tag manipulation, version rollback, scaling, and cost management.

  1. CCC.DataWar.CP03 Scalability

    Ability to scale with growing data volumes and handle multiple queries simultaneously without compromising the performance.

Data Processing

The Data Processing group covers entries related to transforming, enriching, and moving data through pipelines. This includes ETL/ELT, stream and batch processing, data lineage, schema evolution, and workflow orchestration for data workloads.

  1. CCC.DataWar.CP07 Massively Parallel Processing (MPP)

    Distributes queries across multiple nodes for increased performance.

  2. CCC.DataWar.CP12 Integration with ETL

    Integration with services that perform extract, transform and load data from various sources into the data warehouse. Unstructured data in transformed to structured or semi-structured data before ingestion to the data warehouse using ETL tools.

Access Control

The Access Control group covers entries related to authentication, authorization, and trust perimeter enforcement. This includes multi-factor authentication, least privilege access, network access rules, and prevention of unauthorized access or reconnaissance.

  1. CCC.DataWar.CP09 Column-Level Security

    Allows setting access policies at the column level to restrict access to sensitive data fields within tables.

  2. CCC.DataWar.CP10 Row-Level Security

    Enables setting access policies at the row level to control access to subsets of data within a table based on user roles.

Ingestion

The Ingestion group covers entries related to how a service receives or retrieves data, inputs, or commands for processing. This includes both active (pull-based) and passive (push-based) ingestion patterns.

  1. CCC.DataWar.CP11 Integration with Data Sources

    Seamless integration with various data sources such as object storage, relational and non-relational databases, data streams and data lakes.

Machine Learning

The Machine Learning group covers entries related to building, training, deploying, and managing ML models and AI systems. This includes development environments, experiment tracking, model registries, inference, generative AI, prompt engineering, and model governance.

  1. CCC.DataWar.CP13 Integration with ML

    Build-in integration with machine learning services for enhanced processing of large volumes of complex data with ML models for predictive analytics, automated insights and more. ML can be used in data cleansing and transformation for improved data quality as well.

Observability

The Observability group covers entries related to logging, monitoring, metrics, alerting, and event publication. This includes audit trail integrity, enumeration detection, and protection against tampering or unauthorized access to operational telemetry.

  1. CCC.DataWar.CP14 Real-time Metrics Publication

    Ability to continuously track and analyze data as it is ingested, processed and stored to ensure data quality, operational efficiency, scalability and security.