# business-intelligence Published articles for business-intelligence. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Unlocking your data: the value is in collaboration DevFeed: [Unlocking your data: the value is in collaboration]() Original publisher: [Read original article]() Author: Sam Perridge Published: 2026-08-12T14:59:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [data](), [datasets](), [Artificial Intelligence](), [decision-making](), [systems]() Tags: [ai](), [business-intelligence](), [claude](), [collaboration](), [data](), [data-democratisation](), [data-engineering](), [data-maturity](), [data-platform](), [datasets](), [decision-making](), [governance](), [guardrails](), [reporting](), [self-service]() ### AI overview This opinion article argues that organisations unlock more value from data when datasets are connected and insights are accessible across teams. It describes a progression from paper records and siloed systems to connected and democratised data, including self-service analytics and AI, while emphasising governance and practical adoption. ### Source excerpt Organisations often focus on collecting data and connecting systems, but the greatest value comes from helping datasets work together and making insights accessible to the people who need them. In this post, I explore the journey from siloed data to democratised access, showing how self-service analytics and AI can unlock hidden value, while strong governance provides the guardrails for confident decision-making. ## From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer DevFeed: [From COBOL to Copilot: 30 Years of Data, BI, and AI with David Langer]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-07-01T13:43:11Z Content type: article Language: en Sources: [Data Engineering Central]() Topics: [Artificial Intelligence](), [cobol](), [Data Science](), [mainframes](), [Software Engineering](), [AI Strategy](), [self-service](), [dashboards](), [jupyter notebooks]() Tags: [adoption](), [ai](), [business-intelligence](), [cobol](), [data-science](), [enterprise](), [jupyter-notebooks](), [mainframes](), [programming](), [self-service](), [semantic]() ### AI overview A podcast conversation with Dave Langer about nearly three decades spanning COBOL, enterprise architecture, business intelligence, analytics, data science, machine learning, and AI. It discusses persistent data-industry problems, self-service analytics, dimensional modeling, AI adoption, semantic layers, governance, and career advice for data professionals. ### Source excerpt What happens when someone who started programming on a Commodore 64 watches AI reshape the entire data industry? ## Run Product Analytics on Your Neon Data Using Fabi.ai DevFeed: [Run Product Analytics on Your Neon Data Using Fabi.ai]() Original publisher: [Read original article]() Author: Marc Dupuis Published: 2025-11-14T18:24:08Z Content type: tutorial Language: en Sources: [Blog -- Neon Docs]() Topics: [Data analysis](), [Databases](), [AI Chat]() Tags: [ai](), [business-intelligence](), [community](), [dashboards](), [data](), [database](), [how-to](), [product-analytics](), [sql](), [workflows]() ### AI overview A tutorial showing how to connect a Neon Postgres database to Fabi.ai, use its AI Analyst Agent to query product data in plain English, and turn the results into dashboards and automated workflows. ### Source excerpt You're already using Neon, so chances are you've got valuable application data sitting in your Postgres database that reflects how people interact with your product. This data can tell you a lot about your customers and help guide product decisions, whether you're an engineer, a... ## Durable RAG with Temporal and Chainlit DevFeed: [Durable RAG with Temporal and Chainlit]() Original publisher: [Read original article]() Author: Sergey Ustimenko Published: 2024-09-14T09:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [Orchestration](), [Conversational AI](), [reliability](), [resiliency](), [Framework](), [Scalability](), [Open Source]() Tags: [agents](), [ai](), [applications](), [business](), [business-intelligence](), [how-to](), [rag](), [resiliency](), [workflows]() ### AI overview This tutorial explains how Retrieval-Augmented Generation combines large language models with external knowledge sources, and how Temporal and Chainlit can help build durable, reliable, scalable conversational AI workflows. ### Source excerpt Learn about how Temporal's durable execution framework can help solve this problem by bringing durability and resiliency into multi-step RAG workflows. ## SQL vs NoSQL Explained DevFeed: [SQL vs NoSQL Explained]() Original publisher: [Read original article]() Author: Nelson Djalo Published: 2024-07-02T16:00:57Z Content type: comparison Language: en Sources: [Amigoscode Newsletter]() Topics: [NoSQL](), [SQL](), [Databases](), [clickhouse]() Tags: [acid](), [business-intelligence](), [consistency](), [data-analysis](), [database](), [database-scalability](), [durability](), [microsoft](), [mysql](), [nosql](), [olap](), [oracle](), [postgresql](), [rdbms](), [relational-database-management-systems-rdbms](), [relational-databases](), [schema](), [sql]() ### AI overview This comparison explains the differences between SQL and NoSQL databases, including their characteristics, scalability models, and common use cases. It covers SQL database features such as structured schemas and ACID compliance, along with relational and OLAP database categories. ### Source excerpt Choosing the Right Database for Your Needs ## Configuring a Multi-Instance Looker Deployment DevFeed: [Configuring a Multi-Instance Looker Deployment]() Original publisher: [Read original article]() Author: Dennis Mutia Published: 2023-11-13T15:00:28Z Content type: tutorial Language: en Sources: [Engineering at Premise - Medium]() Topics: [dashboards](), [GitHub](), [Backstage](), [releases](), [Development](), [Deployment]() Tags: [backstage](), [business-intelligence](), [cloud-computing](), [dashboards](), [developer](), [developer-portal](), [development](), [gcp](), [github](), [looker](), [operational](), [production](), [releases]() ### AI overview This article explains how Premise configured three Looker instances to separate development from production for LookML and dashboards. It describes standardized data connections, view-only production access, GitHub-based synchronization and releases, Backstage-driven promotion, and Slack notifications. ### Source excerpt By Dennis Mutia, Software Engineer Image by ismagilov on Unsplash This article provides a high level overview of how we have configured multiple Looker deployments to separate development and production environments for both LookML and dashboards. Having a Looker deployment on a single instance has many challenges which include: a dashboard can get edited when someone else is presenting it to a client or a stakeholder someone testing a large dashboard can slow down Looker which will affect other dashboards Looker users cannot test the effects of model changes to their dashboards without having to request for developer access which has cost implications Multiple Looker instances We have set up 3 Looker instances: a development instance where LookML updates are made and dashboards created and reviewed before being released to production, and two production instances. One of the production instances is for internal looks and dashboards and the other for creating dashboards that can be shared with external clients. All three Looker instances have a standardized data connection to read from the same data warehouse. This is to ensure that dashboards and looks work the same access all environments. To ensure all development actually occurs on the development instance, both production instances enforce view only access for all users. This set up allows us to test both data models and dashboards without affecting production content. It also leads to standardized data models across all production instances. Linking development to production To take advantage of the benefits of multiple Looker instances, while limiting the operational maintance, we have linked all three looker instances using GitHub, and use GitHub Releases to update the production instances whenever changes have been tested and approved in development. To release dashboards from development to production we use Backstage, which is also our developer portal. We have created a template which dashboard builders ## How to organize company reporting without a dedicated BI team DevFeed: [How to organize company reporting without a dedicated BI team]() Original publisher: [Read original article]() Author: Sofia\_Semenova (Кошелёк) Published: 2023-09-21T08:08:08Z Content type: tutorial Language: ru Sources: ["Кошелёк" RU]() Topics: [data](), [Microsoft]() Tags: [analytics](), [business-intelligence](), [canvas](), [dashboard](), [data](), [data-analysis](), [microsoft](), [process](), [tag-03968a887ba5](), [tag-5d6527b0eb46](), [tag-9d8cf70dc46c](), [tag-b92bf5906bbd]() ### AI overview This tutorial describes how the Wallet analytics team organized company reporting without a dedicated BI team. It covers problems with Power BI workspaces, outdated dashboards, inconsistent metric definitions, database load, and unmet business needs, along with the initial steps toward a dashboard development process and a shared metrics library. ### Source excerpt Привет, Хабр! На связи аналитики Кошелька. Наша команда состоит из 13 дата-аналитиков, 5 DE-инженеров, 2 ML-инженеров и ровно 0 BI-аналитиков. Что мы любим делать? Определять метрики и рисовать дашборды. Что нужно заказчику? Метрики и дашборды (а еще достижение целей и выручка, но не будем сейчас об этом). В этой статье мы собрали инструкцию, как можно навести порядок в отчётности без отдельных BI-аналитиков, и с какими проблемами вы можете столкнуться в процессе. Читать далее ## A Journey Towards a Custom Data Warehouse Solution Part 2: We Need Storage DevFeed: [A Journey Towards a Custom Data Warehouse Solution Part 2: We Need Storage]() Original publisher: [Read original article]() Author: Robert Bordo (robert@upday.com) Published: 2017-08-22T04:39:55Z Content type: article Language: en Sources: [Upday]() Topics: [data](), [data lake](), [Amazon S3](), [Amazon Redshift](), [Amazon Web Services](), [Scalability]() Tags: [aws](), [business-intelligence](), [data](), [data-lake](), [s3](), [scalability](), [storage](), [warehouse]() ### AI overview This article examines storage choices for a custom data warehouse. It describes application log data, the limitations of time-series databases for additional master and historical data, business intelligence access needs, and the use of Amazon S3 as a data lake while considering other storage options including AWS Redshift. ### Source excerpt In the beginning we created a cluster. And the cluster was without form, and void; and nulls were upon the face of the storage. As we learned in part 1 of our series, a data warehouse consists of several components. The key component is the storage. All the others group around it. But how can one draw a decision on which storage solution to adopt? What is out there anyway? Preface In a perfect world there would be only one kind of storage that fits all the needs of current DWH development and analysis. But since we are not living in that kind of place, we have several options. And the number of options increase the deeper one dives into the topic. There seem to be solutions for every use case you can think of. That might be a good starting point. What is our most common use case? What are we going to store? And how would we like to access our data in the end? Our major source is a massive amount of log data coming from our app. Everything the user does (e.g swiping through articles, selecting categories, leaving the app) is tracked, enriched with metadata (e.g. the user's location, app version, article identifier) and stored by a third-party service in big, semi-structured log files. Having only this source, a time series database like Graphite or InfluxDB could do the job. But also having slow changing master data, like user profiles, article metadata and maybe even to keep a history of data, this solution would not satisfy our current and future needs. Another thing that comes to my mind is how the data will be accessed by our final consumer (namely: Business Intelligence). Usually they use tools like Jasper Reports or Tableau for generating reports. For analyses we have to pre-aggregate the data to make queries more performant and translate raw information into a digestible format. What else is on the market? Storage good at bad at Example S3/Flat Files scalability, easy to use, data lake querying S3 Time Series DB handling time series data non time series data G