# Industries Published articles for Industries. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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... ## Rail Network Modernization: FRMCS, Sovereignty and the AI Edge DevFeed: [Rail Network Modernization: FRMCS, Sovereignty and the AI Edge]() Original publisher: [Read original article]() Author: Steve Payne Published: 2026-09-15T13:31:46Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Network](), [Critical Infrastructure](), [digital sovereignty](), [Resilience](), [Availability](), [Cybersecurity](), [Artificial Intelligence]() Tags: [2026](), [availability](), [cisco](), [cisco-industrial-iot-iiot](), [critical-infrastructure](), [digital-sovereignty](), [end-of-life](), [eu](), [europe](), [industries](), [infrastructure](), [internet-of-things-iot](), [latency](), [modernization](), [network](), [networks](), [nis2](), [resilience](), [systems](), [transportation](), [unified-edge]() ### AI overview Cisco's pre-InnoTrans 2026 perspective examines the modernization of European rail communications from GSM-R to FRMCS. It argues that the transition requires changes to the underlying IP network, including high availability, deterministic latency, cybersecurity, and large-scale automation, while operators address resilience, digital sovereignty, investment, and regulatory requirements. ### Source excerpt Join Cisco at InnoTrans 2026 to explore the next generation of rail networks. Discover how FRMCS, AI edge computing, and digital sovereignty are transforming critical infrastructure into secure, automated systems. ## Cisco and the DISA STIG: Turning Zero Trust Policy into Repeatable Practice - Part 2: Cisco SNA DevFeed: [Cisco and the DISA STIG: Turning Zero Trust Policy into Repeatable Practice - Part 2: Cisco SNA]() Original publisher: [Read original article]() Author: Norman St. Laurent Published: 2026-09-15T13:13:53Z Content type: article Language: en Sources: [Cisco Blogs]() Topics: [Cybersecurity](), [Cisco](), [telemetry](), [Zero Trust](), [vulnerability](), [Network]() Tags: [benchmark](), [cisco](), [cisco-secure-network-analytics-sna](), [cybersecurity](), [department-of-defense-dod](), [government](), [hardening](), [industries](), [nist](), [public-sector](), [stig](), [visibility]() ### AI overview The article explains how the DISA Security Technical Implementation Guide for Cisco Secure Network Analytics turns Zero Trust policy into testable configuration requirements. The STIG provides a shared hardening baseline for the platform and its management functions, with 31 requirements derived from NIST SP 800-53 and related requirements. ### Source excerpt Discover how the new DISA STIG for Cisco Secure Network Analytics helps defense organizations securely configure and harden their analytics platform, ensuring trusted network visibility for Zero Trust operations. ## 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,... ## 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... ## 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... ## Different Types of Database Management Systems DevFeed: [Different Types of Database Management Systems]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-20T10:30:23Z Content type: article Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [Databases](), [Database](), [data](), [Software](), [Scalability](), [NoSQL](), [MongoDB](), [MySQL](), [Oracle Database](), [sql-server]() Tags: [applications](), [backend](), [backup](), [big-data-and-dbms](), [choosing-the-right-dbms-for-your-needs](), [cloud-based-dbms](), [cloud-native](), [comparison-of-dbms-types](), [conclusion](), [consistency](), [data](), [database](), [definition-and-importance-of-dbms](), [different-types-of-database-management-systems](), [emerging-types-in-database-management-systems](), [evolution](), [evolution-of-database-management-systems](), [examples](), [hierarchical-database-management-systems](), [industries](), [integrity](), [management](), [mysql](), [network-database-management-systems](), [nosql](), [nosql-databases](), [object-oriented-database-management-systems-oodbms](), [recovery](), [relational-database-management-systems-rdbms](), [relational-databases](), [scalability](), [systems](), [types](), [types-of-database-management-systems]() ### AI overview This article explains what database management systems are, why they matter, how they support storage, queries, security, integrity, backup, and recovery, and how major DBMS categories have evolved. It also introduces examples including MySQL, Oracle Database, Microsoft SQL Server, and MongoDB. ### Source excerpt Data is one of the most valuable assets for organizations in this AI-powered digital era. From banking systems and e-commerce [...] The post Different Types of Database Management Systems appeared first on Talent500 blog. ## End-to-end SAP Observability with Elastic, Google Cloud, and Kyndryl: A deep dive DevFeed: [End-to-end SAP Observability with Elastic, Google Cloud, and Kyndryl: A deep dive]() Original publisher: [Read original article]() Author: Valerio Arvizzigno,Francesco Di Stefano Published: 2024-07-08T00:00:00Z Content type: article Language: en Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack]() Topics: [observability](), [Google Cloud Platform (GCP)](), [dashboards](), [log management](), [Monitoring](), [hosting](), [Security](), [networking]() Tags: [anomaly-detection-cloud-migration-sap-monitoring-real-time-analysis-network-visibility-google-c](), [cloud](), [deep-dive](), [elastic](), [enterprise](), [google](), [google-cloud](), [industries](), [infrastructure](), [integrations](), [kibana](), [kyndryl](), [logs](), [metrics](), [monitoring](), [networking](), [observability](), [observability-log-analytics-metrics-business-analytics-cloud](), [operations](), [platforms](), [security]() ### AI overview This article presents a full-stack observability approach for complex SAP environments, developed by Elastic in collaboration with Google Cloud and Kyndryl. It describes using Kibana and Elastic integrations to provide visibility across hybrid infrastructure, applications, business processes, logs, metrics, traces, security, and compliance. ### Source excerpt Companies across almost all industries rely on robust complex SAP systems to power their core operations. At Elastic, with the collaboration of Kyndryl and Google Cloud, we designed a full-stack observability experience for your SAP environment. ## Delivering a Near Real-Time Single View into Operations with a Federated Database DevFeed: [Delivering a Near Real-Time Single View into Operations with a Federated Database]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-04-17T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Tutorial](), [Amazon S3](), [Node.js](), [API]() Tags: [api](), [aws](), [aws-s3](), [data-federation](), [industries](), [mongodb](), [node-js](), [s3](), [supply-chain](), [tutorial]() ### AI overview This tutorial demonstrates how to use MongoDB Data Federation to query, transform, and aggregate data across MongoDB, AWS S3, and HTTP API endpoints. It uses customer, vendor, and transaction data to create custom operational views. ### Source excerpt So the data within your organization spans across multiple databases, database platforms, and even storage types, but you need to bring it together and make sense of the data that's dispersed. This is... The post Delivering a Near Real-Time Single View into Operations with a Federated Database appeared first on MongoDB. ## Pulsar Summit Asia 2022 Recap DevFeed: [Pulsar Summit Asia 2022 Recap]() Original publisher: [Read original article]() Author: Sherlock Xu Published: 2022-12-01T00:00:00Z Content type: news Language: en Sources: [Apache Pulsar Blog]() Topics: [Apache Pulsar](), [Messaging](), [Streaming](), [event driven](), [Data Infrastructure](), [Cloud](), [Open Source]() Tags: [apache](), [e-commerce](), [event](), [event-driven](), [gaming](), [industries](), [messaging](), [open-source](), [recap](), [social-media](), [streaming](), [summit](), [virtual-event]() ### AI overview Pulsar Summit Asia 2022 was a two-day online event focused on Apache Pulsar, messaging and streaming technologies, and event-driven applications. The recap highlights more than 1,400 registrations, over 40,000 global views, 41 speakers, 36 sessions, keynotes, use cases, technical deep dives, and ecosystem discussions. ### Source excerpt Since its inception in 2020, Pulsar Summit Asia has received increasing attention from both Asia and beyond. For Pulsar Summit Asia 2022, more than 1400 people from companies like Amazon, Tencent, IBM, Huawei, Dell, ByteDance, and Splunk registered for this online event to discuss the latest messaging and streaming technologies powering a wide variety of industries like education, food, gaming, e-commerce, and social media.