# databricks Published articles for databricks. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents DevFeed: [Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents]() Original publisher: [Read original article]() Author: Charlie Klein; Bryan Smith Published: 2026-09-17T17:00:00Z Content type: opinion Language: en Sources: [Databricks]() Topics: [Web](), [Software](), [Claude Code](), [codex](), [API](), [Model Context Protocol](), [databricks]() Tags: [agent](), [api](), [claude-code](), [codex](), [databricks](), [databricks-ai](), [llms](), [mcp](), [orchestration](), [platform](), [product](), [search](), [web]() ### AI overview The article describes an agent that combines company data in Databricks with web-based market signals. Its enrichment logic is rebuilt across Claude Code, Codex, and a direct model API workflow because each provides different tools, search behavior, and configuration requirements. The article argues that inconsistent web search and the lack of a shared layer make reliable account enrichment difficult. ### Source excerpt An agent that needs the outside worldAn engineer at a software company is building... ## Modernizing the Trade Lifecycle With Governed Data and AI DevFeed: [Modernizing the Trade Lifecycle With Governed Data and AI]() Original publisher: [Read original article]() Author: Kim Hatton; Andrea DeSosa Published: 2026-09-17T14:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [data](), [Artificial Intelligence](), [databricks](), [decision-making](), [execution](), [Risk](), [systems]() Tags: [ai](), [data](), [databricks](), [decision-making](), [execution](), [financial-services](), [fragmentation](), [industries](), [modernization](), [real-time](), [risk](), [systems]() ### AI overview Capital-markets firms are modernizing trade lifecycle workflows as data volumes grow, AI initiatives move toward production, settlement cycles shorten, and regulatory scrutiny increases. The article argues that governed, discoverable, reliable data across research, trading, risk, operations, and compliance is more durable than isolated models. ### Source excerpt Capital-markets firms are modernizing the trade lifecycle under pressure from every direction: growing data volumes... ## "Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE DevFeed: ["Regex for Rows": Simplifying Pattern Detection in SQL with MATCH\_RECOGNIZE]() Original publisher: [Read original article]() Author: Kent Marten; Sergei Fedorov Published: 2026-09-16T17:14:05Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [SQL](), [databricks](), [Cybersecurity](), [Security & Privacy]() Tags: [count](), [cybersecurity](), [databricks](), [failed](), [false-positives](), [functions](), [login](), [partition](), [product](), [real-time](), [regex](), [sql](), [window-functions]() ### AI overview This tutorial explains how Databricks supports SQL MATCH_RECOGNIZE for detecting ordered event patterns. It shows how the clause can simplify sequence detection, including identifying repeated login failures followed by a successful login, compared with complex SQL queries and window functions. ### Source excerpt Imagine you work in cybersecurity and you have a table that tracks login attempts... ## How energy teams turn theft detection into governed action with Genie and AI business processes DevFeed: [How energy teams turn theft detection into governed action with Genie and AI business processes]() Original publisher: [Read original article]() Author: Daniel Zoccali; Jack Yallop Published: 2026-09-15T16:50:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Machine learning](), [Artificial Intelligence]() Tags: [databricks](), [energy](), [fraud](), [industries](), [ml](), [model](), [reporting](), [safety](), [speed]() ### AI overview The article explains how energy teams can operationalize energy-theft detection by connecting model-generated risk signals with investigation, field operations, revenue recovery, and reporting in a governed workflow. It presents a Databricks implementation using a Databricks App, Lakebase, and Unity Catalog. ### Source excerpt Energy theft is the deliberate use of gas or electricity without paying for it, typically... ## How Databricks' marketers use data 3x more with Genie, an AI analytics assistant DevFeed: [How Databricks' marketers use data 3x more with Genie, an AI analytics assistant]() Original publisher: [Read original article]() Author: Elizabeth Dobbs; Thomas Russell; Katy Yuan; Sydney Sundell Published: 2026-09-15T00:36:33Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Artificial Intelligence](), [data](), [dashboards]() Tags: [ai](), [best-practices](), [dashboards](), [data](), [databricks](), [enterprise](), [governance](), [marketing](), [metrics]() ### AI overview Databricks describes how its marketing organization unified campaign, sales, CRM, web analytics, advertising, and other data in a governed lakehouse. It built Marge, a Genie Agents-based conversational analytics assistant that answers marketers' natural-language questions using governed enterprise data. The article says this approach helped the marketing department use data three times more often in decisions. ### Source excerpt Most marketing teams aspire to be data-driven. In practice, getting a trusted answer,... ## What Managed Postgres Services Handle for Teams DevFeed: [What Managed Postgres Services Handle for Teams]() Original publisher: [Read original article]() Author: Databricks Staff Published: 2026-09-14T23:36:59Z Content type: article Language: en Sources: [Databricks]() Topics: [PostgreSQL](), [Database](), [Serverless](), [databricks](), [AI Development]() Tags: [data-plus-ai-foundations](), [database](), [databricks](), [developer-tooling](), [migration](), [postgres](), [postgresql](), [recovery](), [security](), [serverless]() ### AI overview The article defines managed Postgres by the operational responsibilities a provider assumes, including patching, scaling, failover, and backups. It discusses how these responsibilities can vary between providers and describes Lakebase Postgres as a serverless offering with automatic scaling, PostgreSQL compatibility, recovery, and Databricks integrations. ### Source excerpt Every Postgres vendor calls itself "managed." Few of them agree on what that word... ## Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks DevFeed: [Announcing On-Demand State Repartitioning for Apache Spark™ Structured Streaming on Databricks]() Original publisher: [Read original article]() Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng Published: 2026-09-14T21:04:30Z Content type: release Language: en Sources: [Databricks]() Topics: [Streaming](), [databricks](), [real-time](), [Amazon S3](), [API]() Tags: [amazon-s3](), [api](), [data-engineering](), [databricks](), [engineering](), [fraud-detection](), [infrastructure](), [monitoring](), [net-11-preview-7](), [production](), [real-time](), [streaming]() ### AI overview Databricks announces on-demand state repartitioning for Apache Spark Structured Streaming in Public Preview, available in Databricks Runtime 18 and later. The capability lets production stateful streaming queries resize their partition count while preserving checkpoint state, supporting workloads such as aggregations, stream-stream joins, deduplication, sessionization, and transformWithState. Coveo reports reducing related Amazon S3 API costs by 40%. ### Source excerpt Anyone running stateful Apache Spark™ Structured Streaming queries in production... ## Automate replenishment with MMF, Databricks Genie, and Amazon Quick DevFeed: [Automate replenishment with MMF, Databricks Genie, and Amazon Quick]() Original publisher: [Read original article]() Author: Venkatavaradhan Viswanathan Published: 2026-09-14T15:42:06Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [databricks](), [Amazon S3 Tables](), [Amazon S3](), [Automation](), [foundation-models](), [API]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-s3-tables](), [api](), [automation](), [databricks](), [foundation-models](), [machine-learning](), [retail](), [technical-how-to]() ### AI overview This technical walkthrough presents an unattended replenishment workflow for retail. Databricks Many Model Forecasting uses Chronos-2 to predict seven-day demand for each SKU, Databricks Genie detects demand surges, and Amazon Quick reconciles those surges with supplier availability in Amazon S3 Tables. The workflow places routine purchase orders through a Supplier Order API and escalates cases without a suitable single supplier for human review. ### Source excerpt Foundation models made catalog-wide demand forecasting easy; the hard part is now acting on the forecast. This post builds a closed detect-decide-act loop on Databricks and Amazon Quick that reconciles demand surges against live supplier availability and places replenishment orders unattended, escalating to a human only when no supplier can cover a surge. ## Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why? DevFeed: [Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why?]() Original publisher: [Read original article]() Author: Aaron Zavora; Jonathan Thompson Published: 2026-09-11T18:26:59Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Conversational AI](), [Artificial Intelligence](), [data]() Tags: [ai](), [conversational-ai](), [dashboards](), [data](), [databricks](), [healthcare](), [healthcare-life-sciences](), [industries](), [metrics]() ### AI overview The article explains how AI can help health plan finance teams move beyond BI dashboards that identify a higher medical loss ratio (MLR) and instead determine the causes and appropriate corrective actions. It describes Databricks and Abacus as combining governed enterprise data with payer-specific data, business context, and operational knowledge, while conversational AI lets finance leaders ask questions in plain language and receive answers more quickly. ### Source excerpt A health plan CFO closes the month after the usual round of extracts, spreadsheets,... ## Unify your marketing data with Lakeflow Connect DevFeed: [Unify your marketing data with Lakeflow Connect]() Original publisher: [Read original article]() Author: Sonia Bendre; Giselle Goicochea Published: 2026-09-11T18:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Software as a service](), [Databases](), [API](), [Automation](), [dashboards](), [hubspot](), [CI/CD]() Tags: [analytics](), [api](), [automation](), [churn](), [ci-cd](), [connectors](), [crm](), [customer](), [customers](), [dashboards](), [data](), [databases](), [databricks](), [engineering](), [marketing](), [product](), [retention](), [saas](), [salesforce](), [series]() ### AI overview This first post in a series presents Lakeflow Connect as a fully managed data-ingestion service for unifying fragmented marketing and Customer 360 data. It describes native connectors for SaaS applications, databases, and files, configured through a point-and-click UI or API, with governed tables in Unity Catalog and integrations such as HubSpot and Salesforce. The article highlights reduced maintenance compared with custom pipelines and support for downstream reporting, analytics, retention, and churn analysis. ### Source excerpt This is the first post in a new series exploring how Lakeflow Connect brings fully... ## Unifying governance across engines and catalogs in the Open Lakehouse DevFeed: [Unifying governance across engines and catalogs in the Open Lakehouse]() Original publisher: [Read original article]() Author: Daniel Weeks; Ryan Blue; Andrei Tserakhau Published: 2026-09-10T15:05:10Z Content type: article Language: en Sources: [Databricks]() Topics: [Apache Iceberg](), [data](), [databricks](), [Apache Spark](), [DuckDB]() Tags: [apache](), [apis](), [catalog](), [data](), [databricks](), [duckdb](), [governance](), [open](), [product](), [spark]() ### AI overview The article examines two additions to the Apache Iceberg REST Catalog specification: read restrictions and catalog labels. It explains how they support governance across catalogs and engines, including centralized and delegated enforcement, and discusses trust requirements for engines such as Spark, DuckDB, and Trino. ### Source excerpt In our previous posts, we showed how open table formats, open APIs and unified governance... ## Improving Lakebase Postgres compute cache DevFeed: [Improving Lakebase Postgres compute cache]() Original publisher: [Read original article]() Author: David Wein; Sunil Kamath; Haoyu Huang Published: 2026-09-10T13:47:03Z Content type: article Language: en Sources: [Databricks]() Topics: [Caching](), [databricks](), [Databases](), [autoscaling](), [Amazon S3](), [Filesystems](), [cloud-infrastructure]() Tags: [autoscaling](), [cache](), [caching](), [data](), [data-engineering](), [databricks](), [engineering](), [filesystem](), [latency](), [low-latency](), [memory](), [s3](), [storage]() ### AI overview This Databricks article describes improvements to compute-side caching for Lakebase Postgres, a disaggregated storage system backed by object storage such as Amazon S3. It explains PostgreSQL shared buffers, the operating system page cache, and the planned use of dynamically autoscaling shared buffers consuming up to 75% of compute memory. It also introduces a local file cache as an incremental solution for fixed compute instances. ### Source excerpt The disaggregated storage model of Lakebase Postgres provides a feature rich, flexible... ## Five AI Questions We're Hearing from Financial Services Leaders DevFeed: [Five AI Questions We're Hearing from Financial Services Leaders]() Original publisher: [Read original article]() Author: Junta Nakai; Erin Butler; Roshni Joshi; Antoine Amend; Jennifer Miller; Andrea DeSosa; Rajaram Suresh; Kim Hatton; Naeem Rehman; Spencer Cook Published: 2026-09-09T18:09:29Z Content type: article Language: en Sources: [Databricks]() Topics: [Artificial Intelligence](), [databricks](), [audit trail](), [tokenization]() Tags: [2026](), [agentic-ai](), [ai](), [audit-trail](), [banking](), [compliance](), [data](), [databricks](), [financial](), [financial-services](), [governance](), [reconciliation](), [regulatory]() ### AI overview The article presents five questions that financial-services leaders are asking about trustworthy AI. It highlights governance, compliance, auditability, governed data, liquidity, reconciliation, tokenized settlement, financial-crime controls, and human oversight, with Databricks describing examples for banking operations. ### Source excerpt Last year at Sibos Frankfurt, the question was whether AI works. This year: can your... ## A practical approach to end-to-end Solvency II reporting in Databricks DevFeed: [A practical approach to end-to-end Solvency II reporting in Databricks]() Original publisher: [Read original article]() Author: Laurence Ryszka; Jack Yallop Published: 2026-09-09T16:20:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [DataOps](), [Orchestration](), [data](), [systems]() Tags: [business](), [data](), [databricks](), [eu](), [financial](), [financial-services](), [governance](), [industries](), [monitor](), [orchestration](), [platform](), [regulatory](), [solutions](), [uk](), [workflow]() ### AI overview The article presents Databricks as a governed data, orchestration, and reporting layer for end-to-end Solvency II reporting. It covers ingestion, data quality, actuarial reserving, capital calculation, QRT production, ORSA drafting, governance, approvals, and disclosure while allowing insurers to retain established actuarial and capital modeling systems. ### Source excerpt Solvency II reporting is not only a regulatory submission. It is a business process... ## Beyond embedding: How to secure AI/BI Dashboards for every viewer DevFeed: [Beyond embedding: How to secure AI/BI Dashboards for every viewer]() Original publisher: [Read original article]() Author: Sonakshi Pandey Published: 2026-09-09T14:04:44Z Content type: tutorial Language: en Sources: [Databricks]() Topics: [databricks](), [Authorization](), [Grafana](), [SDKs](), [Back end](), [data]() Tags: [authorization](), [backend](), [dashboards](), [databricks](), [embedded](), [guide](), [idp](), [mapping](), [platform](), [product](), [sdk](), [sql]() ### AI overview This guide presents a Databricks design pattern for securing embedded AI/BI Dashboards for different viewers. It uses scoped embed tokens, __aibi_external_value, Unity Catalog row filters and column masks, and identity-provider-synchronized groups so one dashboard can show each viewer only the authorized regions and fields. The same entitlement table governs embedded dashboard access and direct Databricks SQL queries. ### Source excerpt The challengeEmbedding a Databricks AI/BI Dashboard in a customer-facing application is relatively straightforward... ## Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency DevFeed: [Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency]() Original publisher: [Read original article]() Author: Cindy Wang; Cheng Li; Jialu Liu; Sean Kulinski; Arnav Singhvi; Wen Sun; Michael Bendersky Published: 2026-09-09T13:30:00Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Latency](), [Low Latency](), [data]() Tags: [agents](), [announcements](), [benchmarks](), [cost](), [data](), [databricks](), [enterprise](), [latency](), [low-latency](), [performance](), [platform](), [retrieval](), [search](), [speed](), [third-party]() ### AI overview Databricks introduces Adaptive Instructed-Retriever, a retrieval model that combines fast parallel search with sequential multi-step search for harder enterprise queries. It adaptively spends extra computation only when useful, achieving comparable quality to leading third-party models at twice lower latency while improving over single-step retrieval on reported benchmarks. ### Source excerpt Effective enterprise data agents require search that is both accurate and fast. Earlier... ## Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow DevFeed: [Evaluation-First AI Agents: How Zepto Scales Customer Support on Databricks and MLflow]() Original publisher: [Read original article]() Author: Gireesh Sreedhar KP; Deepak Dhankani; Eash Sharma Published: 2026-09-09T03:00:00Z Content type: article Language: en Sources: [Databricks]() Topics: [AI Agent](), [databricks](), [AI Infrastructure](), [real-time]() Tags: [agents](), [ai](), [ai-engineering](), [architecture](), [best-practices](), [blog](), [company](), [customer](), [customers](), [data-science-and-ml](), [data-strategy](), [databricks](), [efficiency](), [engineering](), [evaluation](), [india](), [industries](), [operations](), [platform](), [product](), [production](), [real-time](), [retail-consumer-goods](), [scale](), [workflows]() ### AI overview This Databricks and MLflow case study describes how Zepto uses an evaluation-first, multi-agent AI system to operate customer support at more than 100,000 tickets per day. It focuses on the system architecture, evaluation framework, quality gate, and development and production loops used to improve reliability as volume, product categories, languages, and failure modes expand. ### Source excerpt Zepto's Push for Reliable, Real-Time Customer SupportZepto is one of India's fastest-growing... ## Delivering Real-Time Personalization with Databricks and Redis DevFeed: [Delivering Real-Time Personalization with Databricks and Redis]() Original publisher: [Read original article]() Author: Philip Laussermair, Anant Pingle Published: 2026-09-08T00:00:00Z Content type: article Language: en Sources: [Redis Blog]() Topics: [data]() Tags: [analytics](), [apache-flink](), [batch](), [data](), [databricks](), [e-commerce](), [performance](), [personalization](), [real-time](), [redis](), [spark](), [streaming](), [tech]() ### AI overview The article explains how Databricks Real-Time Mode and Redis support low-latency personalization by continuously processing event streams and serving fresh results quickly. ### Source excerpt Why real-time matters A customer is browsing an e-commerce site. They search for running shoes, open a product, read reviews, and add an item to the cart. Every one of those actions is a signal about what they want right now. If the homepage they lan... ## We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform DevFeed: [We Cut Cloud Waste Before Touching Cluster Sizes: Lessons from Running a Data Platform]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-09-07T06:33:31Z Content type: article Language: en Sources: [Housing.com]() Topics: [BigQuery](), [databricks](), [Optimization](), [AWS Database Migration Service](), [data-platforms](), [Platform Engineering](), [migration](), [Cloud]() Tags: [bigquery](), [cloud-computing](), [cost](), [cost-optimization](), [data-engineering](), [data-platforms](), [databricks](), [delta-lake](), [finops](), [infrastructure](), [migration](), [optimization](), [platform-engineering]() ### AI overview This article explains how a data platform team reduced cloud costs by removing obsolete BigQuery data, adjusting Delta Lake retention, right-sizing DMS infrastructure, identifying unmonitored Databricks jobs, and standardizing pipeline onboarding and cost alerts. It reports that DMS costs were cut by over 50% and that retention was reduced from 90 days to 7 days for appropriate workloads after operational validation. ### Source excerpt How orphaned BigQuery storage, Delta retention, DMS right-sizing, and Databricks System Tables became our biggest cloud cost wins. The biggest cloud cost optimization we made wasn't shrinking clusters.It was deleting data we'd forgotten we were paying for.Like most teams, our first instinct was to tune infrastructure first. Instead, we discovered a treasure trove of hidden costs: orphaned BigQuery datasets, 90-day Delta retention, 24-hour jobs no one monitored, and DMS infrastructure that no longer matched business needs.We stopped treating cloud bills as a finance problem and started treating them as a platform engineering problem.30-second takeaway Why deleting forgotten data saved more than shrinking clusters. How we cut DMS costs by over 50%. How Databricks System Tables exposed hidden 24-hour jobs. How config.metadata standardized pipeline onboarding. How weekly Slack alerts turned cost optimization into a habit. Section 1: Storage Was Our Biggest Leak -- We Were Paying to Store Data Nobody Used This is the most overlooked cost on many data platforms. Storage duplication across platforms We had already migrated several workloads from BigQuery to Databricks. Large datasets were still sitting in BigQuery long after they had stopped serving production workloads - quietly generating storage costs month after month. Nothing failed. No alerts fired. Every month, we paid for storage that no longer served production workloads.A migration isn't complete until the old storage is decommissioned.The hidden cost of long retention The next surprise came from Delta Lake retention settings. Our workspace was configured to retain deleted table data and transaction history for 90 days to support time travel. Time travel is incredibly useful. But did every table need three months of historical recovery? Not really. We reduced retention to 7 days for appropriate workloads after validating operational needs. What changed immediately: Less storage tied up in deleted data. Faster clea ## A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres DevFeed: [A universal interface: How QuintoAndar made ClickHouse plug-and-play with managed Postgres]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-27T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [data](), [Cloud](), [Kafka](), [databricks](), [API]() Tags: [api](), [clickhouse](), [cloud](), [data](), [databricks](), [house](), [latency](), [low-latency](), [tech-lead]() ### AI overview QuintoAndar rebuilt its customer data platform on ClickHouse Cloud, consolidating roughly 900 million monthly events from 14 million users and serving about 300 million API requests. The team replaced separate low-latency and batch-processing pipelines by writing events directly to ClickHouse. ClickHouse Managed Postgres provided compatibility with Hightouch and other Postgres-compatible tools. ### Source excerpt QuintoAndar unified ~900 million monthly events in ClickHouse Cloud and used ClickHouse Managed Postgres to make that data accessible to Hightouch and any Postgres-compatible tool. ## Open-weight models are fast on Neon AI Gateway. Here's why DevFeed: [Open-weight models are fast on Neon AI Gateway. Here's why]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2026-08-20T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [databricks](), [Inference Performance](), [model-deployment](), [Low-Latency Inference](), [Caching](), [Mixture of Experts (MoE)](), [gpt-oss](), [Large Language Model]() Tags: [caching](), [databricks](), [gpt-oss](), [inference](), [mixture-of-experts-moe](), [optimization](), [performance](), [product](), [speed]() ### AI overview The article explains why open-weight models can deliver low latency on Neon AI Gateway when paired with Databricks Foundation Model APIs and an optimized serving stack. It highlights continuous batching, KV-cache paging, custom kernels, quantization, multi-GPU layouts, prompt caching, and Mixture-of-Experts architectures. ### Source excerpt Open-weight models are built for performance, but how they're served determines whether end users actually feel that speed. Neon AI Gateway calls Databricks Foundation Model APIs for Databricks-hosted open-weight models, a stack that has years of inference engineering behind it. ## The Complete Databricks Learning Roadmap for 2026 DevFeed: [The Complete Databricks Learning Roadmap for 2026]() Original publisher: [Read original article]() Author: Zach Wilson Published: 2026-08-14T20:11:45Z Content type: tutorial Language: en Sources: [DataExpert.io Newsletter]() Topics: [databricks](), [Learning]() Tags: [2026](), [databricks](), [learn](), [learning](), [master](), [skip]() ### AI overview A 2026 learning roadmap for Databricks covering what to learn, what to skip, and the recommended order for mastering the platform. ### Source excerpt What to learn, what to skip, and the right order to master Databricks. ## Quasi-Agentic Pipelines with Databricks and Apache Airflow DevFeed: [Quasi-Agentic Pipelines with Databricks and Apache Airflow]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-08-10T21:23:57Z Content type: tutorial Language: en Sources: [Data Engineering Central]() Topics: [databricks](), [airflow](), [Data pipelines](), [Large Language Model](), [data-engineering]() Tags: [airflow](), [apache-airflow](), [data-engineering](), [databricks](), [llms](), [pipelines]() ### AI overview A practical developer discussion of incorporating LLMs and agents into existing data workflows using Databricks and Apache Airflow. It also examines determinism in data pipelines and the gap between business requirements and engineering implementation. ### Source excerpt the strange space in between ## Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently) DevFeed: [Why Most Single Source of Truth Initiatives Fail (And What Successful Teams Do Differently)]() Original publisher: [Read original article]() Author: Deepika Saini Published: 2026-07-20T10:07:52Z Content type: article Language: en Sources: [Housing.com]() Topics: [data](), [product analytics]() Tags: [analytics](), [dashboards](), [data](), [data-architecture](), [data-engineering](), [data-strategy](), [databricks](), [governance](), [leadership](), [product-analytics](), [sql](), [technical]() ### AI overview This article argues that Single Source of Truth initiatives often fail because teams use different definitions for shared business metrics. It presents governance, business ownership of KPI definitions, and alignment between teams as more important than centralizing tables, pipelines, or dashboards. ### Source excerpt "We have multiple dashboards showing different numbers. Which one is correct?" If you've worked in data long enough, you've probably heard this question more times than you'd like. Sales reports one revenue figure. Finance reports another. Product Analytics has a third. Executives spend more time debating whose dashboard is correct than discussing what action to take. The natural response is often: "Let's build a Single Source of Truth." Sounds simple. Build a few centralized tables. Move everyone onto the same dashboards. Problem solved. Except...it rarely is. After leading an enterprise-wide Single Source of Truth (SSOT) initiative, I learned an important lesson: The hardest part wasn't building pipelines or writing SQL. It was aligning people. Technology was the easy part. Changing how the organization thought about data was the real challenge. The Biggest Myth About Single Source of Truth Many organizations believe an SSOT is simply a technical project. The thinking usually goes like this: Collect Data ↓ Transform Data ↓ Build Gold Tables ↓ Everyone Uses Them Unfortunately, reality looks more like this: Different Teams ↓ Different Definitions ↓ Different Dashboards ↓ Different Decisions ↓ Lost Trust The problem isn't that data lives in different places. The problem is that different teams define the same business metrics differently. Figure 1: Moving from fragmented metric definitions to a trusted Single Source of Truth is as much about standardization and governance as it is about technology. A table cannot solve that. Only governance can. Technology Doesn't Create Trust Imagine a metric as simple as Revenue. Ask five departments what "Revenue" means, and you might receive five different answers. Finance may recognize revenue after invoicing. Sales may count closed deals. Marketing may include projected pipeline. Product Analytics may track subscription purchases. Customer Success may exclude refunds. None of them are necessarily wrong. They're answering differen [Next page]()