Highlightdbt Labs

dbt measures are column aggregations, now migrating to simple metrics

dbt documentation for measures as aggregations on model columns, with deprecation toward type: simple metrics.

Measures aggregate columns and can stand alone or feed complex metrics. Parameters include name, agg (sum, max, min, average, median, count_distinct, percentile, sum_boolean), expr, non_additive_dimension, and related fields. The new spec deprecates measures in favor of simple metrics under metrics.

Based on: Measures | dbt Developer Hub · dbt Labs

HighlightConfluent

Confluent Schema Registry centralizes Kafka schema validate-and-evolve rules

Confluent documentation overview of Schema Registry for managing, validating, and evolving schemas on Kafka topics.

Schema Registry is a centralized store for topic message schemas and for serialize/deserialize over the network. Producers and consumers use it for consistency and compatibility as schemas change; Confluent frames it as a data-governance component covering quality, standards, lineage visibility, audit, and collaboration, on Cloud and Platform.

Based on: Schema Registry for Confluent Platform | Confluent Documentation · Confluent

HighlightBitol (LF AI & Data)

ODCS v3.2.0 standardizes the sections of a producer–consumer data contract

Bitol’s Open Data Contract Standard defines a YAML-oriented structure for agreements between data producers and consumers.

The Open Data Contract Standard (ODCS) v3.2.0, under Apache 2.0 from Bitol at LF AI & Data, describes how to structure a data contract. Contracts cover schema, quality, SLA, roles, infrastructure, and related sections, with JSON Schema for YAML validation and media type application/odcs+yaml;version=3.2.0.

Based on: GitHub - bitol-io/open-data-contract-standard: Home of the Open Data Contract Standard (ODCS). · Bitol (LF AI & Data)

HighlightGoogle Cloud

LookML puts join structure and query content in one reusable semantic model

Google Cloud docs introduce LookML as Looker’s modeling language for dimensions, aggregates, joins, and SQL generation.

LookML (Looker Modeling Language) is a dependency-style language for building semantic data models over SQL databases. Models and views define joins and calculations once; Looker generates dialect-independent SQL so analysts avoid repeating expressions and business users query without writing SQL.

Based on: Introduction to LookML | Looker | Google Cloud Documentation · Google Cloud

HighlightDatabricks

Unity Catalog: one governance layer under Databricks data and AI assets

Databricks docs on Unity Catalog—access control, lineage, and auditing for data and AI via a three-level object model.

Unity Catalog is Databricks’ unified governance layer for data and AI. When enabled, it enforces access control, tracks lineage, and logs activity under workspace interactions. Assets are securable objects in a catalog.schema.object namespace; tables and volumes may be managed or external. An open-source implementation also exists.

Based on: What is Unity Catalog? | Databricks on AWS · Databricks

HighlightPact Foundation

Pact contract tests: verify service messages without burning the house down

Pact introduction: code-first contract testing for HTTP and message integrations between services.

Pact is a code-first tool for testing HTTP and message integrations with contract tests. Contract tests check that messages between applications match a shared understanding documented in a contract, as an alternative to expensive, brittle end-to-end integration tests. The approach fits especially well when many services must communicate.

Based on: Introduction | Pact Docs · Pact Foundation

Highlightdbt Labs

dbt model contracts: build-time guarantees on column shape before consumers break

dbt Labs docs on model contracts—upfront shape guarantees verified at build, plus governance caveats and support limits.

A dbt model contract is a set of upfront guarantees on the shape of a model’s dataset. At build time dbt checks that the transformation matches the contract or fails. Governance features add trust but can harden rollbacks if adopted too early, and contracts apply only to supported SQL materializations—not Python models, ephemeral models, or several other resource types.

Based on: Model contracts | dbt Developer Hub · dbt Labs

HighlightSalesforce

Salesforce describe API: pull object fields, URLs, and relationships as metadata

REST reference for sObject Describe—full object metadata via GET, with conditional If-Modified-Since headers.

The sObject Describe resource returns complete metadata for a named Salesforce object, including fields, URLs, and child relationships. It is a GET endpoint returning JSON or XML, authenticated with a Bearer token. Optional If-Modified-Since and If-Unmodified-Since headers support conditional responses, including 304 when nothing changed.

Based on: sObject Describe | Reference | REST API Developer Guide | Salesforce Developers · Salesforce

Highlightmartin.kleppmann.com

Why Thrift, Protobuf, and Avro must survive schema change, not only serialize bytes

Martin Kleppmann on how teams reach schema-based binary formats and why schema evolution is the overlooked requirement.

Kleppmann traces a common path from language-native serialization to JSON to custom binary formats, then to Thrift, Protocol Buffers, or Avro for schema-driven, cross-language encoding. He argues that comparisons often skip what happens when the schema changes. All three formats support evolution so producers and consumers on different versions can still interoperate.

Based on: Schema evolution in Avro, Protocol Buffers and Thrift Martin Kleppmann's blog · martin.kleppmann.com

HighlightConfluent

Compatibility types that keep producers and consumers in sync as schemas change

Confluent Schema Registry docs on schema evolution and backward, forward, full, and transitive compatibility modes.

Schema evolution means changing schemas over time while keeping producers and consumers compatible. Schema Registry compares new versions to prior ones using configurable compatibility types, with BACKWARD as the default. Rules differ by format (Avro, Protobuf, JSON Schema) and by how fields were originally defined.

Based on: Schema Evolution & Compatibility Types | Backward, Forward, Full, Transitive | Confluent Documentation · Confluent

Highlightw3.org

SHACL 1.2 Rules

This document defines SHACL Rules, a language for describing the structure of RDF graphs.

SHACL 1.2 Rules is a specification that defines a language for describing the structure of RDF graphs and provides inferencing with the generation of new RDF data from a combination of rules and a base data graph. The document defines the syntax and semantics of rule-based inference, including basic patterns, recursion, filtering, negation, assignment, and importing rules. It also covers the evaluation of a rule set and the relationship between SHACL Rules and SPARQL.

Based on: SHACL 1.2 Rules · w3.org

Highlightfrontiersin.org

SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs

A visualization workflow for exploring SHACL violations in large knowledge graphs.

The paper presents SHACLens, an interactive visualization workflow that links ontology, instance data, and violation reports across multiple coordinated views. The workflow is designed to help analysts identify co-occurring errors and their likely upstream causes. An evaluation of the workflow using a transcriptomics dataset showed that it efficiently surfaced repeated sets of errors due to missing objects and schema inconsistencies.

Based on: Frontiers | SHACLens: a visualization workflow for SHACL violation exploration in knowledge graphs · frontiersin.org