HighlightMicrosoft Learn

Purview and Fabric as one path from source to Power BI lineage

Microsoft Learn article on using Microsoft Purview with Microsoft Fabric for estate-wide governance, classification, and end-to-end lineage.

Microsoft Purview and Microsoft Fabric are presented as parts of the Microsoft intelligent data platform for storing, analyzing, and governing data together. Combined, they are said to cover the estate and lineage from data source to Power BI report without stitching multiple vendors. Purview is described as governance, risk, and compliance coverage across Microsoft 365, on-premises, multicloud, and SaaS.

Based on: Use Microsoft Purview to Govern Microsoft Fabric - Microsoft Fabric · Microsoft Learn

HighlightREDCap Consortium / Vanderbilt

REDCap: consortium survey and database software with FHIR hooks

REDCap Consortium page describing the secure web app for research surveys and databases, including data dictionary design, exports, compliance, and FHIR.

REDCap is a secure web application for building and managing online surveys and databases, free to REDCap Consortium partners. Design can use an Online Designer or an Excel data dictionary upload; features include audit trails, multi-site access, statistical exports, and installs aimed at HIPAA, 21 CFR Part 11, and FISMA. Structured EHR data can be pulled via FHIR with OAuth2 through Clinical Data Interoperability Services.

Based on: Software – REDCap · REDCap Consortium / Vanderbilt

HighlightAirbyte

Airbyte schema-change policies decide how source drift reaches the destination

Airbyte docs on per-connection schema change detection and propagation: new/removed fields and streams, type changes, and breaking cursor/key cases.

Each Airbyte connection can specify how source schema changes are handled. Cloud checks before sync at most every 15 minutes per source; self-managed at most every 24 hours, with manual refresh available. Behaviors cover new and removed columns and streams, type changes, and immediate pause when a cursor is removed.

Based on: Schema change management | Airbyte Docs · Airbyte

HighlightMicrosoft

OneLake catalog centralizes find, govern, and secure for Fabric items

Overview of Microsoft Fabric’s OneLake catalog for discovering items, reviewing governance posture, and managing security roles.

OneLake catalog is a centralized Fabric place to find, explore, and use items and to govern owned data. It is reachable from the Fabric navigation pane and embedded in Teams, Excel, and Copilot Studio; metadata can also be searched via the Catalog Search REST API. Explore, Govern, and Secure tabs cover browsing with filters, governance posture insights, and unified workspace and OneLake security role management.

Based on: OneLake catalog overview - Microsoft Fabric · Microsoft

Highlightdbt Labs

Groups bind DAG nodes to a named owner and private-access boundary

dbt documentation on declaring groups in YAML to organize nodes and restrict access to private models.

A group is a named collection of nodes in a dbt DAG with a required owner. Groups support intentional collaboration by restricting access to private models. Members may include models, tests, seeds, snapshots, analyses, and metrics, but not sources or exposures, and each node belongs to only one group. Groups are declared under a groups key; name and owner are required, with optional description and meta in later versions.

Based on: Add groups to your DAG | dbt Developer Hub · dbt Labs

Highlightdbt Labs

Exposures name the dashboards and apps that depend on your DAG

dbt docs on declaring manual or automatic exposures that link downstream uses to models, sources, and metrics.

Exposures describe downstream uses of a dbt project such as dashboards, applications, or data science pipelines. Defining them lets you run and test feeding resources and publish consumer-facing pages in generated docs. They can be declared in YAML or created automatically for supported integrations and stored in dbt metadata. Required fields include name, type, and owner; depends_on lists refs, sources, and metrics.

Based on: Add Exposures to your DAG | dbt Developer Hub · dbt Labs

Highlightdbt Labs

dbt docs combine YAML descriptions with warehouse metadata

Developer Hub page on generating a documentation site from project descriptions and information-schema queries.

dbt can generate project documentation and render it as a website so downstream consumers can discover curated datasets. Docs cover project information such as model code, DAG, and tests, plus warehouse details like column types and table sizes from the information schema. Authors add description keys on models, columns, sources, and related resources before generating the site.

Based on: About documentation | dbt Developer Hub · dbt Labs

Highlightdbt Labs

Consistent dbt structure frees teams to decide on hard problems

dbt Labs guide arguing that files, folders, naming, and patterns reduce decision fatigue in collaborative projects.

The guide says analytics engineering is collaboration at scale under limited decision bandwidth, so projects need consistent norms for folders, names, and patterns. Structure is how transformations are labeled, grouped, and combined. One shared principle is moving data from source-conformed shapes toward business-conformed ones, with consistency valued over any single style.

Based on: How we structure our dbt projects | dbt Developer Hub · dbt Labs

Highlightdbt Labs

dbt exports materialize saved MetricFlow queries as warehouse tables

dbt docs on exports: run saved Semantic Layer queries via the job scheduler and write results to tables or views.

Exports run saved MetricFlow queries and write output to a table or view in the data platform, using the dbt job scheduler. They give SQL and non–Semantic Layer tools access to metric definitions as ordinary relations; running an export counts toward queried metrics usage, querying the result does not.

Based on: Write queries with exports | dbt Developer Hub · dbt Labs

HighlightarXiv

DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation

A framework for learnable evidence control in multi-hop retrieval-augmented generation.

The paper introduces DynaKRAG, a unified framework that formulates multi-hop evidence acquisition as state-conditioned control over atomic evidence operations. It uses a learned controller to select the next operation and updates the evidence state accordingly. The authors evaluate DynaKRAG on several benchmarks and demonstrate its effectiveness in coordinating retrieval, diagnosis, and gap-directed acquisition.

Based on: DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation · arXiv

HighlightIEEE Communications Magazine

Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication

A framework for efficiently and verifiably updating large language model-enabled semantic knowledge bases.

The authors propose a proof-of-unlearning framework for updating large language models (LLMs) used in semantic knowledge bases. The framework tracks the evolution of unlearning by measuring drifts in the LoRA adapter subspace. Experimental results demonstrate its effectiveness. This work addresses the challenge of removing outdated, malicious, or privacy-sensitive content from LLMs without retraining.

Based on: Proof of Unlearning for Semantic Knowledge Bases in Large Language Models-Enabled Semantic Communication · IEEE Communications Magazine

Highlightopenalex.org

Enhancing Operations at the Columbus Control-Center: A Hybrid Approach Utilizing Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation

A paper investigating a hybrid approach combining Large Language Models with Knowledge Graphs and Retrieval-Augmented Generation.

The paper proposes a hybrid system to enhance operational efficiency at the Columbus Control-Center, leveraging Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation. The system aims to automate routine tasks and provide real-time support for flight control teams. It combines the strengths of LLMs, KGs, and RAG to create a more intelligent and responsive support system.

Based on: Enhancing Operations at the Columbus Control-Center: A Hybrid Approach Utilizing Large Language Models, Knowledge Graphs, and Retrieval-Augmented Generation