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This page describes how to use Snowflake's anomaly detection algorithm to automatically identify data quality issues in data metric functions (DMFs). It explains how to enable anomaly detection for system DMFs like ROW_COUNT and FRESHNESS, adjust sensitivity levels, and identify anomalies using dedicated event tables and views.

The new MATCH_RECOGNIZE feature in BigQuery allows users to perform complex pattern matching on their data directly within their SQL queries, identify sequences of rows that match a specified pattern, and express complex patterns and define custom logic to analyze them within a single SQL clause. This reduces the need for cumbersome self-joins or complex procedural logic and lessens reliance on Python to process data.

Microsoft Fabric Graph is now in public preview, offering a scalable graph solution with data management, analytics, and visualization. It helps model relationships across organizational data for insights in fraud detection, supply chain optimization, and customer 360° views. It also connects data across systems to reveal patterns and enable AI to reason and act effectively.

This article discusses Drasi, an open-source Rust data change processing platform, which has added support for Graph Query Language (GQL), the new international ISO standard for querying property graphs. It explains what GQL means for continuous queries and how GQL support was implemented, highlighting the benefits of using GQL and providing examples of its key features and how it differs from openCypher.

Drasi, an open-source project focused on building change-driven solutions, celebrates its first anniversary with the introduction of Graph Query Language (GQL) support. This enhancement aligns Drasi with the new ISO standard for property graphs, offering developers more flexibility in building continuous queries alongside the existing openCypher support. Drasi has also evolved into a Cloud Native Computing Foundation (CNCF) Sandbox project, marking a significant step in its growth and adoption within the cloud-native ecosystem.

This article provides an overview of Graph in Microsoft Fabric, a scalable solution that transforms disconnected data into AI-powered insights using a labeled property graph model. It highlights the benefits of graph analytics, including uncovering hidden connections and enabling complex queries, and explains how Graph in Microsoft Fabric integrates with the Microsoft Fabric platform, offering a unified experience for modeling, querying, and integrating with other services like OneLake and Power BI. The article also details workspace roles, pricing, region availability and related content.

Microsoft Fabric has launched a native graph data management, analytics, and visualization service, empowering enterprises with a relationship-first approach to model and explore interwoven data. This service leverages OneLake to connect data from various business functions, enabling faster fraud detection, smarter recommendations, and optimized supply chains. Graph in Microsoft Fabric is designed for various roles, including business users, analysts, data engineers, and developers, to participate in connected insights.

This page provides information on existing graph datasets available on the help cluster at https://help.kusto.windows.net within the Samples database. It offers examples of how to query these graphs using the Kusto Query Language (KQL), demonstrating prebuilt graph models without requiring any setup. The datasets include simple educational graphs, LDBC SNB interactive graphs, LDBC financial graphs, BloodHound Entra datasets, and BloodHound Active Directory datasets, each designed for specific use cases like learning graph fundamentals, social network analysis, financial transaction analysis, and security assessments.

This page provides GQL query patterns, examples, and use cases, demonstrating how to use MATCH, WHERE, and RETURN clauses to analyze graph relationships. It covers GQL use cases alongside KQL and shows how to build queries for security, social networks, and organizational analysis with step-by-step examples.

This page provides a reference for Graph Query Language (GQL), covering fundamental concepts, functions, and operators. It explains graph patterns, match modes, path modes, and offers guidance on performance optimization and best practices for querying graph data in Kusto and Microsoft Fabric. The reference also lists core GQL functions and operators with Kusto Query Language (KQL) equivalents and examples.

This article introduces Graph Query Language (GQL), an emerging ISO standard for querying graph databases, allowing users to analyze relationships in their data with SQL-like syntax. It guides users through creating graph references, configuring client request properties, and running GQL queries, and provides examples for basic pattern matching and using labels.

Ramu Murakami Gutierrez discusses how to visualize SQL Property Graphs using Oracle SQL Developer with an extension for Visual Studio Code. The article provides a 5-step guide to install the necessary extensions, create a database connection, and execute SQL graph queries. It also highlights available graph visualization features like fit to screen, sticky mode, grouping, captions, and vertex/edge properties inspection.

This "Weekly Edge" blog post summarizes recent graph technology news, including a Graph Power Hour podcast featuring Paco Nathan discussing GraphRAG with Amazon Neptune and Bedrock, a free "Essential GraphRAG" book, and a blog post on improving Text2Cypher for GraphRAG using schema pruning with Kuzu. It also highlights a new book on GQL and a G.V() release with Oracle Graph support and new data exploration features.

This article explains how to use Snowflake's ASOF JOIN to efficiently join time-series data, even when timestamps don't perfectly align. It provides a visual example with building entries and door openings and discusses a real-world use case of rollback-based pricing for telecom calls. It also compares ASOF JOIN with traditional SQL approaches, highlighting its simplicity and readability.

This article introduces SQL Notebooks, a new feature in the SQL Developer Extension for VS Code that allows users to execute SQL and PL/SQL code within a notebook interface. SQL Notebooks enable a mix of markdown and application code, making them suitable for demonstrating code evolution and sharing narratives. The author provides a walkthrough of how to start and use SQL Notebooks, including adding code blocks, running queries, incorporating markdown, and sharing notebooks with others.

This article discusses how to streamline Snowflake workflows using the flow operator (-\>\>). It contrasts this approach with the traditional RESULT_SCAN method, highlighting the benefits of enhanced readability, simplified debugging, improved reliability, and increased efficiency. The article also provides practical applications, including chaining multiple transformations and combining DML with SELECT statements.

G.V() 3.34.79 introduces support for Oracle Graph (Oracle 23ai) using SQL:2023, enhanced no-code data exploration, and an upgrade to Kuzu 0.11.0 with single file database storage. It also includes improved Gremlin and Cypher syntax handling, Single Sign-On (SSO) and user federation on the web version, and schemaless data support for Google Cloud Spanner Graph, along with various other improvements and bug fixes. The next release will feature RDF/SPARQL support and GQL-native graph database integration.

This tutorial explains how to create a knowledge graph using Oracle Autonomous Database and Property Graph Query Language (PGQL). It covers the basics of graph theory and knowledge graphs, and demonstrates how to implement them using Oracle's managed environment. The tutorial provides a Python implementation that extracts relationships from documents using LLMs and stores them as graph structures in Oracle.

This article introduces Snowflake's new Flow operator and UNION BY NAME features to simplify SQL workflows. The Flow operator allows chaining multiple SQL statements, while UNION BY NAME simplifies combining tables with different schemas. The author provides examples of using these features to analyze sales data and create consolidated reports.

A user is trying to establish a session in Adobe Analytics and is asking about the difference between visid and hitid, and how to identify unique users and sessions, especially across devices. The discussion covers the use of visid_high and visid_low to identify visitors, hitid_high and hitid_low to identify individual hits, and the importance of ECID for accurate cross-device tracking. There is also some discussion of the meaning of the visit_page_num field.