# Databricks

The Data and AI Company

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Database for AI Agents: 5 Evaluation Criteria

DevFeed: [Database for AI Agents: 5 Evaluation Criteria](<https://devfeed.tech/articles/database-for-ai-agents-5-evaluation-criteria-42692.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/database-for-ai-agents>)

Author: Databricks Staff

Published: 2026-09-17T17:27:40Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [scaling](<https://devfeed.tech/topics/scaling.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [hybrid-search](<https://devfeed.tech/topics/hybrid-search.md>), [acid](<https://devfeed.tech/topics/acid.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [acid](<https://devfeed.tech/tags/acid.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [data-plus-ai-foundations](<https://devfeed.tech/tags/data-plus-ai-foundations.md>), [database](<https://devfeed.tech/tags/database.md>), [databases](<https://devfeed.tech/tags/databases.md>), [developers](<https://devfeed.tech/tags/developers.md>), [hybrid-search](<https://devfeed.tech/tags/hybrid-search.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>)

### AI overview

The article presents five criteria for evaluating databases that support AI agents in production: branch isolation, serverless scaling, hybrid search, ACID guarantees, and unified platform access. It explains why agent workloads require persistent state, memory, tool results, repeated reads and writes, concurrent activity, and access to current operational data.

### Source excerpt

The five criteria for evaluating a database for AI agents are branch isolation, serverless...

## Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents

DevFeed: [Web Search Limitations and Duplicated Tooling Complicate Market-Signal Agents](<https://devfeed.tech/articles/the-web-search-your-agent-inherited-isn-t-good-enough-41387.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/web-search-your-agent-inherited-isnt-good-enough>)

Author: Charlie Klein; Bryan Smith

Published: 2026-09-17T17:00:00Z

Content type: opinion

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Web](<https://devfeed.tech/topics/web.md>), [Software](<https://devfeed.tech/topics/software.md>), [Claude Code](<https://devfeed.tech/topics/claude-code.md>), [codex](<https://devfeed.tech/topics/codex.md>), [API](<https://devfeed.tech/topics/api.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [databricks](<https://devfeed.tech/topics/databricks.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [api](<https://devfeed.tech/tags/api.md>), [claude-code](<https://devfeed.tech/tags/claude-code.md>), [codex](<https://devfeed.tech/tags/codex.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [databricks-ai](<https://devfeed.tech/tags/databricks-ai.md>), [llms](<https://devfeed.tech/tags/llms.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [platform](<https://devfeed.tech/tags/platform.md>), [product](<https://devfeed.tech/tags/product.md>), [search](<https://devfeed.tech/tags/search.md>), [web](<https://devfeed.tech/tags/web.md>)

### 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...

## What is AIOps?

DevFeed: [What is AIOps?](<https://devfeed.tech/articles/what-is-aiops-41388.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/what-is-aiops>)

Author: Databricks Staff

Published: 2026-09-17T16:49:43Z

Content type: tutorial

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [AIOps](<https://devfeed.tech/topics/aiops.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [Grafana](<https://devfeed.tech/topics/grafana.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aiops](<https://devfeed.tech/tags/aiops.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data-plus-ai-foundations](<https://devfeed.tech/tags/data-plus-ai-foundations.md>), [devops](<https://devfeed.tech/tags/devops.md>), [logs](<https://devfeed.tech/tags/logs.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [network](<https://devfeed.tech/tags/network.md>), [observability](<https://devfeed.tech/tags/observability.md>)

### AI overview

This guide explains AIOps, which applies AI and machine learning to IT operations to detect anomalies, correlate events, identify root causes, and trigger responses. It describes how AIOps analyzes logs, traces, events, and network topology, while complementing observability, DevOps, and human judgment.

### Source excerpt

Artificial Intelligence for IT Operations (AIOps) applies AI and machine learning to IT operations to detect anomalies...

## Modernizing the Trade Lifecycle With Governed Data and AI

DevFeed: [Modernizing the Trade Lifecycle With Governed Data and AI](<https://devfeed.tech/articles/modernizing-the-trade-lifecycle-with-governed-data-and-ai-42693.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/modernizing-trade-lifecycle-governed-data-and-ai>)

Author: Kim Hatton; Andrea DeSosa

Published: 2026-09-17T14:00:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [decision-making](<https://devfeed.tech/topics/decision-making.md>), [execution](<https://devfeed.tech/topics/execution.md>), [Risk](<https://devfeed.tech/topics/risk.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [decision-making](<https://devfeed.tech/tags/decision-making.md>), [execution](<https://devfeed.tech/tags/execution.md>), [financial-services](<https://devfeed.tech/tags/financial-services.md>), [fragmentation](<https://devfeed.tech/tags/fragmentation.md>), [industries](<https://devfeed.tech/tags/industries.md>), [modernization](<https://devfeed.tech/tags/modernization.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [risk](<https://devfeed.tech/tags/risk.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### 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](<https://devfeed.tech/articles/regex-for-rows-simplifying-pattern-detection-in-sql-with-match-recognize-31401.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/regex-rows-simplifying-pattern-detection-sql-matchrecognize>)

Author: Kent Marten; Sergei Fedorov

Published: 2026-09-16T17:14:05Z

Content type: tutorial

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [SQL](<https://devfeed.tech/topics/sql.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Cybersecurity](<https://devfeed.tech/topics/cybersecurity.md>), [Security & Privacy](<https://devfeed.tech/topics/security-privacy.md>)

Tags: [count](<https://devfeed.tech/tags/count.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [failed](<https://devfeed.tech/tags/failed.md>), [false-positives](<https://devfeed.tech/tags/false-positives.md>), [functions](<https://devfeed.tech/tags/functions.md>), [login](<https://devfeed.tech/tags/login.md>), [partition](<https://devfeed.tech/tags/partition.md>), [product](<https://devfeed.tech/tags/product.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [regex](<https://devfeed.tech/tags/regex.md>), [sql](<https://devfeed.tech/tags/sql.md>), [window-functions](<https://devfeed.tech/tags/window-functions.md>)

### 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](<https://devfeed.tech/articles/how-energy-teams-turn-theft-detection-into-governed-action-with-genie-and-ai-business-processes-26720.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/how-energy-teams-turn-theft-detection-governed-action-genie-and-ai-business-processes>)

Author: Daniel Zoccali; Jack Yallop

Published: 2026-09-15T16:50:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [databricks](<https://devfeed.tech/tags/databricks.md>), [energy](<https://devfeed.tech/tags/energy.md>), [fraud](<https://devfeed.tech/tags/fraud.md>), [industries](<https://devfeed.tech/tags/industries.md>), [ml](<https://devfeed.tech/tags/ml.md>), [model](<https://devfeed.tech/tags/model.md>), [reporting](<https://devfeed.tech/tags/reporting.md>), [safety](<https://devfeed.tech/tags/safety.md>), [speed](<https://devfeed.tech/tags/speed.md>)

### 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](<https://devfeed.tech/articles/how-databricks-marketers-use-data-3x-more-with-genie-an-ai-analytics-assistant-26719.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/databricks-marketers-use-data-3x-genie-ai-analytics-assistant>)

Author: Elizabeth Dobbs; Thomas Russell; Katy Yuan; Sydney Sundell

Published: 2026-09-15T00:36:33Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data](<https://devfeed.tech/topics/data.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [governance](<https://devfeed.tech/tags/governance.md>), [marketing](<https://devfeed.tech/tags/marketing.md>), [metrics](<https://devfeed.tech/tags/metrics.md>)

### 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](<https://devfeed.tech/articles/managed-postgres-what-lakebase-actually-takes-off-your-plate-26721.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/managed-postgres>)

Author: Databricks Staff

Published: 2026-09-14T23:36:59Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [Database](<https://devfeed.tech/topics/database.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>)

Tags: [data-plus-ai-foundations](<https://devfeed.tech/tags/data-plus-ai-foundations.md>), [database](<https://devfeed.tech/tags/database.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [developer-tooling](<https://devfeed.tech/tags/developer-tooling.md>), [migration](<https://devfeed.tech/tags/migration.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [recovery](<https://devfeed.tech/tags/recovery.md>), [security](<https://devfeed.tech/tags/security.md>), [serverless](<https://devfeed.tech/tags/serverless.md>)

### 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](<https://devfeed.tech/articles/announcing-on-demand-state-repartitioning-for-apache-sparktm-structured-streaming-on-databricks-26235.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/announcing-demand-state-repartitioning-apache-sparktm-structured-streaming-databricks>)

Author: Thangam Vaiyapuri; Jay Palaniappan; B. Micheal Okutubo; Zifei Feng

Published: 2026-09-14T21:04:30Z

Content type: release

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [amazon-s3](<https://devfeed.tech/tags/amazon-s3.md>), [api](<https://devfeed.tech/tags/api.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fraud-detection](<https://devfeed.tech/tags/fraud-detection.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [net-11-preview-7](<https://devfeed.tech/tags/net-11-preview-7.md>), [production](<https://devfeed.tech/tags/production.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### 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...

## 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?](<https://devfeed.tech/articles/health-plans-your-bi-tells-you-mlr-moved-can-your-ai-tell-you-why-11540.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/health-plans-your-bi-tells-you-mlr-moved-can-your-ai-tell-you-why>)

Author: Aaron Zavora; Jonathan Thompson

Published: 2026-09-11T18:26:59Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Conversational AI](<https://devfeed.tech/topics/conversational-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [healthcare-life-sciences](<https://devfeed.tech/tags/healthcare-life-sciences.md>), [industries](<https://devfeed.tech/tags/industries.md>), [metrics](<https://devfeed.tech/tags/metrics.md>)

### 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](<https://devfeed.tech/articles/unify-your-marketing-data-with-lakeflow-connect-11544.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/unify-your-marketing-data-lakeflow-connect>)

Author: Sonia Bendre; Giselle Goicochea

Published: 2026-09-11T18:00:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Software as a service](<https://devfeed.tech/topics/saas.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [API](<https://devfeed.tech/topics/api.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [hubspot](<https://devfeed.tech/topics/hubspot.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [api](<https://devfeed.tech/tags/api.md>), [automation](<https://devfeed.tech/tags/automation.md>), [churn](<https://devfeed.tech/tags/churn.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [connectors](<https://devfeed.tech/tags/connectors.md>), [crm](<https://devfeed.tech/tags/crm.md>), [customer](<https://devfeed.tech/tags/customer.md>), [customers](<https://devfeed.tech/tags/customers.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [data](<https://devfeed.tech/tags/data.md>), [databases](<https://devfeed.tech/tags/databases.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [marketing](<https://devfeed.tech/tags/marketing.md>), [product](<https://devfeed.tech/tags/product.md>), [retention](<https://devfeed.tech/tags/retention.md>), [saas](<https://devfeed.tech/tags/saas.md>), [salesforce](<https://devfeed.tech/tags/salesforce.md>), [series](<https://devfeed.tech/tags/series.md>)

### 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](<https://devfeed.tech/articles/unifying-governance-across-engines-and-catalogs-in-the-open-lakehouse-11545.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/unifying-governance-across-engines-and-catalogs-open-lakehouse>)

Author: Daniel Weeks; Ryan Blue; Andrei Tserakhau

Published: 2026-09-10T15:05:10Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Apache Iceberg](<https://devfeed.tech/topics/apache-iceberg.md>), [data](<https://devfeed.tech/topics/data.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Apache Spark](<https://devfeed.tech/topics/spark.md>), [DuckDB](<https://devfeed.tech/topics/duckdb.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [apis](<https://devfeed.tech/tags/apis.md>), [catalog](<https://devfeed.tech/tags/catalog.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [duckdb](<https://devfeed.tech/tags/duckdb.md>), [governance](<https://devfeed.tech/tags/governance.md>), [open](<https://devfeed.tech/tags/open.md>), [product](<https://devfeed.tech/tags/product.md>), [spark](<https://devfeed.tech/tags/spark.md>)

### 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](<https://devfeed.tech/articles/improving-lakebase-postgres-compute-cache-11541.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/improving-lakebase-postgres-compute-cache>)

Author: David Wein; Sunil Kamath; Haoyu Huang

Published: 2026-09-10T13:47:03Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [Filesystems](<https://devfeed.tech/topics/filesystems.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>)

Tags: [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [cache](<https://devfeed.tech/tags/cache.md>), [caching](<https://devfeed.tech/tags/caching.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [filesystem](<https://devfeed.tech/tags/filesystem.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [memory](<https://devfeed.tech/tags/memory.md>), [s3](<https://devfeed.tech/tags/s3.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### 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](<https://devfeed.tech/articles/five-ai-questions-we-re-hearing-from-financial-services-leaders-11539.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/five-ai-questions-were-hearing-financial-services-leaders>)

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](<https://devfeed.tech/sources/databricks.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [audit trail](<https://devfeed.tech/topics/audit-trail.md>), [tokenization](<https://devfeed.tech/topics/tokenization.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [audit-trail](<https://devfeed.tech/tags/audit-trail.md>), [banking](<https://devfeed.tech/tags/banking.md>), [compliance](<https://devfeed.tech/tags/compliance.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [financial](<https://devfeed.tech/tags/financial.md>), [financial-services](<https://devfeed.tech/tags/financial-services.md>), [governance](<https://devfeed.tech/tags/governance.md>), [reconciliation](<https://devfeed.tech/tags/reconciliation.md>), [regulatory](<https://devfeed.tech/tags/regulatory.md>)

### 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](<https://devfeed.tech/articles/a-practical-approach-to-end-to-end-solvency-ii-reporting-in-databricks-11543.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/practical-approach-end-end-solvency-ii-reporting-databricks>)

Author: Laurence Ryszka; Jack Yallop

Published: 2026-09-09T16:20:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [DataOps](<https://devfeed.tech/topics/dataops.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [data](<https://devfeed.tech/topics/data.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [business](<https://devfeed.tech/tags/business.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [eu](<https://devfeed.tech/tags/eu.md>), [financial](<https://devfeed.tech/tags/financial.md>), [financial-services](<https://devfeed.tech/tags/financial-services.md>), [governance](<https://devfeed.tech/tags/governance.md>), [industries](<https://devfeed.tech/tags/industries.md>), [monitor](<https://devfeed.tech/tags/monitor.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [platform](<https://devfeed.tech/tags/platform.md>), [regulatory](<https://devfeed.tech/tags/regulatory.md>), [solutions](<https://devfeed.tech/tags/solutions.md>), [uk](<https://devfeed.tech/tags/uk.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### 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](<https://devfeed.tech/articles/beyond-embedding-how-to-secure-ai-bi-dashboards-for-every-viewer-11537.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/beyond-embedding-how-secure-aibi-dashboards-every-viewer>)

Author: Sonakshi Pandey

Published: 2026-09-09T14:04:44Z

Content type: tutorial

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [Grafana](<https://devfeed.tech/topics/grafana.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [authorization](<https://devfeed.tech/tags/authorization.md>), [backend](<https://devfeed.tech/tags/backend.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [guide](<https://devfeed.tech/tags/guide.md>), [idp](<https://devfeed.tech/tags/idp.md>), [mapping](<https://devfeed.tech/tags/mapping.md>), [platform](<https://devfeed.tech/tags/platform.md>), [product](<https://devfeed.tech/tags/product.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [sql](<https://devfeed.tech/tags/sql.md>)

### 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...

## Introducing Consort: Test-driven development on a branching database

DevFeed: [Introducing Consort: Test-driven development on a branching database](<https://devfeed.tech/articles/introducing-consort-test-driven-development-on-a-branching-database-11542.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/introducing-consort-test-driven-development-branching-database>)

Author: Kevin Hartman

Published: 2026-09-09T13:41:25Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [Test-driven development](<https://devfeed.tech/topics/tdd.md>), [Database](<https://devfeed.tech/topics/database.md>), [Integration testing](<https://devfeed.tech/topics/integration-testing.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Refactoring](<https://devfeed.tech/topics/refactoring.md>), [Continuous Delivery (CD)](<https://devfeed.tech/topics/continuous-delivery.md>), [Pull Request](<https://devfeed.tech/topics/pull-request.md>), [Reverse Engineering](<https://devfeed.tech/topics/reverse-engineering.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [continuous-delivery](<https://devfeed.tech/tags/continuous-delivery.md>), [database](<https://devfeed.tech/tags/database.md>), [development](<https://devfeed.tech/tags/development.md>), [integration](<https://devfeed.tech/tags/integration.md>), [partners](<https://devfeed.tech/tags/partners.md>), [platform](<https://devfeed.tech/tags/platform.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [pull-request](<https://devfeed.tech/tags/pull-request.md>), [refactoring](<https://devfeed.tech/tags/refactoring.md>), [reverse-engineering](<https://devfeed.tech/tags/reverse-engineering.md>), [software](<https://devfeed.tech/tags/software.md>), [software-development](<https://devfeed.tech/tags/software-development.md>), [testing](<https://devfeed.tech/tags/testing.md>)

### AI overview

The article introduces Consort, a workflow for applying test-driven development to a branching database. It argues that copy-on-write branches of a real database can replace mocks, bring integration testing into the inner development loop, and let engineers run destructive tests in isolation. Versioned schema migrations can move with the dependent code, enabling what the article calls Data CD and helping catch production database problems during pull requests and merges.

### Source excerpt

For 25 years I built software on the practices I grew up with: Kent Beck's TDD, Martin...

## Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency

DevFeed: [Adaptive Instructed-Retriever: Frontier-Quality Search at 2x Lower Latency](<https://devfeed.tech/articles/adaptive-instructed-retriever-frontier-quality-search-at-2x-lower-latency-11536.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/adaptive-instructed-retriever-frontier-quality-search-2x-lower-latency>)

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](<https://devfeed.tech/sources/databricks.md>)

Topics: [databricks](<https://devfeed.tech/topics/databricks.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Low Latency](<https://devfeed.tech/topics/low-latency.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [announcements](<https://devfeed.tech/tags/announcements.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [performance](<https://devfeed.tech/tags/performance.md>), [platform](<https://devfeed.tech/tags/platform.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [search](<https://devfeed.tech/tags/search.md>), [speed](<https://devfeed.tech/tags/speed.md>), [third-party](<https://devfeed.tech/tags/third-party.md>)

### 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](<https://devfeed.tech/articles/evaluation-first-ai-agents-how-zepto-scales-customer-support-on-databricks-and-mlflow-11538.md>)

Original publisher: [Read original article](<https://www.databricks.com/blog/evaluation-first-ai-agents-how-zepto-scales-customer-support-databricks-and-mlflow>)

Author: Gireesh Sreedhar KP; Deepak Dhankani; Eash Sharma

Published: 2026-09-09T03:00:00Z

Content type: article

Language: en

Sources: [Databricks](<https://devfeed.tech/sources/databricks.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [databricks](<https://devfeed.tech/topics/databricks.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [blog](<https://devfeed.tech/tags/blog.md>), [company](<https://devfeed.tech/tags/company.md>), [customer](<https://devfeed.tech/tags/customer.md>), [customers](<https://devfeed.tech/tags/customers.md>), [data-science-and-ml](<https://devfeed.tech/tags/data-science-and-ml.md>), [data-strategy](<https://devfeed.tech/tags/data-strategy.md>), [databricks](<https://devfeed.tech/tags/databricks.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [india](<https://devfeed.tech/tags/india.md>), [industries](<https://devfeed.tech/tags/industries.md>), [operations](<https://devfeed.tech/tags/operations.md>), [platform](<https://devfeed.tech/tags/platform.md>), [product](<https://devfeed.tech/tags/product.md>), [production](<https://devfeed.tech/tags/production.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [retail-consumer-goods](<https://devfeed.tech/tags/retail-consumer-goods.md>), [scale](<https://devfeed.tech/tags/scale.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### 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...