TRAC 0.3.1

This is a security patch release to address vulnerabilities in the Log4j library:
https://www.ncsc.gov.uk/news/apache-log4j-vulnerability

• Update Log4j to version 2.16.0
• Update all other Java dependencies to latest stable versions

Netty and many other Java libraries have released their own fixes to address this issue, which are included in their latest stable versions.

TRAC 0.3.0

TRAC 0.3 adds core data capabilities to the platform. The data engine is based on Apache Arrow, it provides storage abstraction, format translation and data transfer using gRPC streaming methods. Data updates and versioning are supported. Client apps can store, retrieve and manipulate data using standard data formats. This release also includes automated checks for security vulnerabilities and licensing, to let the platform pass typical enterprise software compliance scans.

The 0.3 release series is not metadata-compatible with the 0.2 series.

• TRAC data API definition and service implementation
• Core data engine based on Apache Arrow
• Supported data formats: Arrow streams, Arrow files, CSV, JSON
• Supported storage engines: Local storage
• TRAC plugin API, for adding data formats and storage engines
• Data capabilities added to the web API package
• Documentation, examples and tutorials for application development
• Automated compliance scans for security vulnerabilities and licensing
• Add support for Python 3.10 in the model runtime

TRAC 0.3.0 beta 2

This beta adds automated compliance checks for security vulnerabilities and licensing. The release is to prove the compliance publishing workflow and provide a set of compliance reports for review. The reports will then be generated and published with every subsequent release.

TRAC 0.3.0 beta 1

This beta is a preview of the 0.3 release series, which provides core data capabilities to the TRAC platform. The core data engine is based on Apache Arrow, it provides storage abstraction, format translation and data transfer using gRPC streaming methods. Data updates and versioning are supported. Client apps can store, retrieve and manipulate data using standard data formats.

The 0.3 release series is not metadata-compatible with the 0.2 series.

• TRAC data API definition and service implementation
• Core data engine based on Apache Arrow
• Supported data formats: Arrow streams, Arrow files, CSV, JSON
• Supported storage engines: Local storage
• TRAC plugin API, for adding data formats and storage engines
• Data capabilities added to the web API package
• Documentation, examples and tutorials for application development

TRAC 0.2.0

TRAC 0.2 adds the model runtime API and executor framework for Python, which allows model components to be build and tested. Data is exposed using the Pandas stack. It also includes a structured web API for building client applications and additions in the Gateway to support gRPC and gRPC-Web clients as well as basic HTTP routing. Pre-built packages are now available for the runtime and web API, documentation is published on readthedocs.io.

Version 0.2 is not metadata-compatible with the 0.1.x series.

• TRAC model API for Python
• TRAC runtime executor framework for Python models
• Support for non-partitioned datasets as Pandas data frames
• Support for dev-mode, which allows running models using concise, developer-friendly config
• Published trac-runtime package to PyPI
• Add trac-web-api package as a structured API for building web applications
• Extended the gateway to support multiple API formats: gRPC, gRPC-Web and JSON/REST
• Extend the gateway to support simple HTTP proxy routes and configurable routing
• Initial platform documentation, available on readthedocs.io
• Metadata changes:
  • DataDefinition split into data, schema and storage
  • Standard naming and abbreviation between metadata and API packages

TRAC 0.2.0 beta 2

The second beta of the 0.2 series adds an API package for building web applications using the existing platform functionality. This package is intended for building statically-compiled web apps that can communicate directly with the TRAC platform, without needing an additional application server, proxies or any other intermediate components. The package is available via NPM.

• Add trac-web-api package as a structured API for building web applications
• Extended the gateway to support multiple API formats: gRPC, gRPC-Web and JSON/REST
• Extend the gateway to support simple HTTP proxy routes and configurable routing

TRAC 0.2.0 beta 1

This first beta of the 0.2 series is to provide early sight of the Python model runtime. It includes initial versions of the core elements of the executor framework and will run the Pandas example model included in the repository doc folder. PySpark support is not included and there are gaps around validation and type handling. Documentation is limited.

• TRAC model API for Python
• TRAC runtime executor framework for Python models
• Support for non-partitioned datasets as Pandas data frames
• Support for dev-mode, which allows running models using concise, developer-friendly config
• Metadata updates:
  • Added StorageDefinition to describe physical storage of data items (physical and logical attributes evolve separately)
  • Removed physical storage attributes from DataDefinition, replaced with a reference to StorageDefinition
  • Changed DataDefinition to describe data items using part/snap/delta structure (only a single root partition is used)

TRAC 0.1.1

TRAC 0.1.1 adds a number of core API and metadata enhancements to the metadata service. The intention is to provide core metadata features and structure as early as possible in the development timeline, to make them available for building other components and reduce disruption from late metadata changes. This release mainly addresses the tagging layer of the metadata model, object definitions will be addressed as the platform capabilities enabled by those definitions are added.

Version 0.1.1 is not metadata-compatible with version 0.1.0.

• Tag update API - Use a series of TagUpdate operations to add, update and remove tag attributes (replaces the "PUT resource" style API in version 0.1.0)
• Universal definition of time - Timestamps are assigned to every object and tag, "as-of" criteria are available for every read and search operation
• Add tag selectors - Selectors can be used to address any object/tag using explicit version numbers, as-of times or the latest version
• IDs and headers - ID/header information is moved into tags, so objects can be created cleanly before their ID is known
• Add MAP type to the available metadata value types

TRAC 0.1.0

First release of Accenture TRAC, intended for reference and experimentation.

• First draft of TRAC metadata structures (.proto files)
• First draft of service API for the metadata service (.proto files)
• Metadata service implementation using gRPC
• Metadata DAL implementation using JDBC, initial support for 6 SQL dialects:
  MySQL, MariaDB, PostgreSQL, Microsoft SQL Server, Oracle, H2
• Platform Gateway - Proxies the metadata API, exposes REST / json over HTTP/1.1
• Shared ConfigManager component, loading is delegated to config loader plugins
• Config loader implementation for loading from local files
• Logging and error reporting (standard errors reported over gRPC and REST)
• Deploy tool for managing schema and tenants in the metadata database
• Initial documentation included in README.md, .proto files and doc/examples
• Unit and integration testing using GitHub actions
• Licensed under the Apache license, version 2.0