# tableau Published articles for tableau. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data DevFeed: [Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data]() Original publisher: [Read original article]() Author: Eric Mboizi Published: 2026-09-17T17:46:19Z Content type: news Language: en Sources: [TechRepublic]() Topics: [Artificial Intelligence](), [Google](), [Google Cloud Platform (GCP)](), [Cloud](), [data]() Tags: [agentforce](), [ai](), [ai-agents](), [artificial-intelligence](), [cloud](), [crm](), [data](), [enterprise](), [enterprise-ai](), [enterprises](), [gemini](), [gemini-enterprise](), [google](), [google-cloud](), [hyperforce](), [infrastructure](), [integration](), [news](), [salesforce](), [tableau](), [tech-industry](), [workflows]() ### AI overview Google Cloud and Salesforce are connecting Gemini Enterprise, Agentforce, Hyperforce, and Tableau with CRM data to reduce integration friction in enterprise AI workflows. ### Source excerpt Google Cloud and Salesforce are connecting Gemini Enterprise, Agentforce, Hyperforce, and Tableau to reduce integration friction for enterprise AI workflows. The post Google Cloud, Salesforce Tie Gemini Enterprise to Agentforce and CRM Data appeared first on TechRepublic. ## 7 best free open source business intelligence tools right now DevFeed: [7 best free open source business intelligence tools right now]() Original publisher: [Read original article]() Author: Ian Vanagas Published: 2026-03-24T00:00:00Z Content type: comparison Language: en Sources: [PostHog]() Topics: [bi-tools](), [business-intelligence](), [Grafana](), [semantic-layer]() Tags: [000](), [7-best](), [best](), [business](), [business-intelligence](), [data](), [enterprise](), [free](), [google](), [intelligence](), [looker](), [money](), [open](), [open-source](), [right](), [send](), [source](), [tableau](), [tools](), [wants]() ### AI overview The seven tools serve different needs: PostHog combines product analytics, SQL dashboards, and synced business data in one platform, with a hosted free tier of 1 million events and 1 million synced rows monthly. Metabase emphasizes accessible visual and SQL querying, but offers less data modeling than some traditional BI suites; its hosted plan follows a 14-day trial. Superset supports SQL databases, 40+ visualizations, and a semantic layer, while its listed AI SQL assistant is limited to Preset's paid plans. Grafana focuses on infrastructure observability, with dashboards, alerts, and a broad plugin ecosystem; its free Cloud tier includes usage limits and 14-day retention. Evidence treats dashboards and data products as code using SQL and Markdown, with AI tools in its cloud version. Redash is a lightweight SQL and dashboard tool without its own hosted service, and it has no AI query generation. Lightdash builds on dbt models and metrics, with code-oriented workflows and AI analysts, but depends on dbt and its hosted plan starts at $3,000 monthly. The text frames the choice around team needs, existing data stack, and capacity to self-host. ### Source excerpt Not everyone has money to dish out for an enterprise Tableau contract, and not everyone wants to send their data to Looker (Google) either. Free and... ## Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform DevFeed: [Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform]() Original publisher: [Read original article]() Author: Karen Fehmer Published: 2025-06-02T03:53:58Z Content type: article Language: en Sources: [Kogan.com]() Topics: [data](), [software-architecture](), [BigQuery](), [CI/CD](), [GitHub Actions](), [Git]() Tags: [architecture](), [bigquery](), [ci-cd](), [data](), [dbt](), [github-actions](), [looker](), [self-service](), [tableau]() ### AI overview Kogan.com describes replacing a request-based BI model with a governed self-service data platform built around BigQuery, dbt, Looker, and Acryl (DataHub). The architecture uses layered models, GitHub Actions CI/CD, testing, documentation, and controlled environments to support trusted analysis and reporting. ### Source excerpt At Kogan.com, our data needs have grown alongside the business. As more teams relied on insights to move quickly, it became clear our request-based BI model couldn't scale. We needed a platform that empowered teams to answer their own questions, trust the numbers, and move independently. That journey led us to build a self-service platform grounded in governance, transparency, and scalability--powered by dbt, Looker, and Acryl (DataHub). Rethinking Our BI Model We originally relied on Tableau. It served us well but had limitations: duplicated logic, inconsistent metrics, and limited collaboration with dbt. Tableau workbooks weren't version-controlled, which made maintaining consistency difficult. To bridge modeling and reporting, we often created extra presentation tables in dbt, adding complexity. We needed a platform that integrated tightly with dbt and supported governed exploration. A New Architecture: Modular, Transparent, Scalable We redesigned the platform around a clean, modular flow: Raw Sources -> BigQuery -> dbt -> Looker -> Acryl (DataHub) Our data transformations are built in dbt, where we follow a layered modeling structure. While we use stg_ (staging) and int_ (intermediate) models primarily for data cleaning and standardization, the marts_ models are the ones that power our analysis and reporting. These models contain our fact and dimension tables, fully aligned with business logic and ready for consumption in Looker. We've integrated CI/CD pipelines using GitHub Actions, and every change is tested before deployment. This includes dbt tests, schema validations, and model documentation to ensure confidence at every layer. Why Looker Was the Right Fit for Self-Service Looker offered a structured, governed approach that aligned with our dbt-first architecture. LookML let us centralize business logic, version it with Git, and deploy changes through CI/CD. With support for multiple environments (UAT and Production), we can test safely before releasing to users ## Dagster announces integrations with Looker, Power BI, Tableau, and Sigma DevFeed: [Dagster announces integrations with Looker, Power BI, Tableau, and Sigma]() Original publisher: [Read original article]() Author: Pedram Navid Published: 2024-11-14T00:00:00Z Content type: release Language: en Sources: [Dagster]() Topics: [DataOps](), [bi-tools](), [looker](), [DevOps]() Tags: [analytics-stack](), [business-intelligence](), [dagster](), [engineering](), [integrations](), [looker](), [power-bi](), [tableau]() ### AI overview Dagster announces integrations with Looker, Power BI, Tableau, and Sigma that bring BI assets into its asset graph and lineage view. The integrations support visibility into downstream dashboard dependencies and orchestration of BI data refreshes; the Sigma integration is available in preview. ### Source excerpt Discover new integrations between Dagster and popular BI tools like Looker, Power BI, and Tableau to enhance your analytics stack. ## Fundrise breaks down data silos with Omni DevFeed: [Fundrise breaks down data silos with Omni]() Original publisher: [Read original article]() Published: 2024-10-16T16:00:00Z Content type: article Language: en Sources: [Omni Blog]() Topics: [product analytics](), [dashboards](), [data](), [looker]() Tags: [bi-tools](), [data](), [governed-data](), [looker](), [platform](), [silos](), [spreadsheet](), [tableau](), [team](), [use-cases]() ### AI overview Fundrise replaced separate Tableau and Looker setups with Omni, a BI platform on Snowflake, to combine governed financial reporting with accessible internal analytics. The case study describes how a shared data model and Excel-style formulas helped consolidate isolated dashboards and support collaboration. Fundrise says it onboarded all 50 initial BI power users and reduced dozens of dashboards to five used daily. ### Source excerpt With Omni, Fundrise consolidated their Tableau & Looker use cases into a single BI platform. Omni lets them report on tightly governed data while also moving fast internally, especially using Omni's Excel formulas - a familiar interface for their spreadsheet-savvy team. ## Aviatrix case study DevFeed: [Aviatrix case study]() Original publisher: [Read original article]() Published: 2024-07-24T16:00:00Z Content type: article Language: en Sources: [Omni Blog]() Topics: [Computing](), [looker](), [Web Accessibility (a11y)]() Tags: [analytics](), [aviatrix-consolidation-looker-tableau-excel](), [bi-tools](), [business-intelligence](), [cache](), [caching](), [case-study](), [excel](), [looker](), [migration](), [tableau](), [team](), [use-cases]() ### AI overview Aviatrix replaced separate Looker, Tableau, and Excel workflows with Omni for business intelligence. Its three-person data team migrated 15 frequently used dashboards and reports in three weeks, removing duplicate content and training users through guided sessions. The article reports twice the full adoption of BI features compared with the previous tools, along with easier self-service analysis and team-specific workbooks that do not alter the shared data model. ### Source excerpt After a three-week migration to Omni, the Aviatrix team consolidated their Looker, Tableau, and Excel use cases and doubled BI adoption. Here's how they did it. ## Make your reports faster : Beginner's guide to Tableau Optimization DevFeed: [Make your reports faster : Beginner's guide to Tableau Optimization]() Original publisher: [Read original article]() Author: Akash Vishwakarma Published: 2023-03-07T15:58:04Z Content type: tutorial Language: en Sources: [Grofers]() Topics: [bi-tools](), [performance-optimization](), [etl](), [Cache]() Tags: [beginner](), [blinkit](), [business-intelligence](), [dashboards](), [data](), [data-analysis](), [data-visualization](), [faster](), [fields](), [guide](), [looker](), [memory](), [metrics](), [network](), [optimisation](), [performance](), [power-bi](), [real-time](), [recording](), [reporting](), [reports](), [tableau]() ### AI overview Blinkit reports dashboard load time down over 50% in six months, view traffic throughput up more than threefold in three months, and extract time down 50% in six months. The excerpt recommends using Tableau performance recording to locate bottlenecks; filtering or sampling data during extract generation; checking network and database conditions; and reviewing query filters, including context or action filters where suitable. It advises reducing marks by using hierarchical summary views, splitting dense views, limiting dimensions on the Marks shelf, and favoring charts over tables. Blinkit shifts complex calculations and aggregation to scheduled backend ETLs, leaving dynamic calculations in Tableau, and suggests simpler data types while avoiding string calculations, table calculations, and LOD functions. It favors cached extracts over live connections for large datasets, incremental refresh when existing records do not change, and hiding unused fields to reduce extract or custom SQL work. The excerpt offers recommendations but does not quantify each step's individual effect. ### Source excerpt Make your reports faster: A beginner's guide to Tableau Optimisation In today's world, given the pace at which data operates, we need a tool that can help us to generate reports faster and bring out insights within milliseconds. In order to solve this challenge, several companies have started utilising a few Business Intelligence (BI) tools such as Tableau/Power BI/Superset/Looker/Qlikview, etc. We at Blinkit have also moved away from the traditional way of reporting via spreadsheets to a more scalable and robust tool -- Tableau. Earlier, we had no single source of truth for metrics; it took a significant amount of manual effort to compile data in spreadsheets, and we were frequently limited by our local memory. Theoretically, Tableau can handle data up to ∞ rows and columns. However, it slowly begins to slow down as it renders calculations, fields, and formulas. At Blinkit, tracking supply and consumer metrics in real-time is the need of the hour. The faster we track, the faster we decide and move forward. Given below are some hacks that enabled us to achieve a significant improvement in the performance of our dashboards. The effects have been noted across various aspects of our operations, including but not limited to: Reduction in the average render/load time by over 50% in the last six months. Over a three-fold improvement in throughput in traffic to views in the last three months. A 50% reduction in the extract time over the last six months. Check performance recording This is the first step in identifying the problem. The performance recording gives information about key events as you interact with a workbook. Further details can be found here. Fig. 1: Start Performance Recording in TableauFig. 2: Events Sorted by TimeGenerating extract To speed up extract generation, consider importing only some data from the original data source by using filters or importing a limited sample of data. Connecting to data sources Slow connections could be due to network issues o ## How Tableau field-level lineage helps analysts explore, validate, and troubleshoot data DevFeed: [How Tableau field-level lineage helps analysts explore, validate, and troubleshoot data]() Original publisher: [Read original article]() Author: Angeline Junaedy Published: 2022-05-11T16:10:21Z Content type: article Language: en Sources: [Monte Carlo]() Topics: [data-engineering](), [data analyst](), [Data science and engineering](), [Grafana]() Tags: [analyst](), [analytics](), [announcements](), [data-engineering](), [data-observability](), [data-products](), [tableau](), [tableau-lineage]() ### AI overview Monte Carlo describes how field-level lineage in Tableau workbooks helps analysts trace fields to upstream sources, check data freshness and quality, and identify contacts when issues arise. It also helps data engineering teams assess which dashboards are affected by changes. ### Source excerpt We have extended coverage all the way to Tableau workbooks to better understand field-level relationships across the warehouse and BI layer. Here's how analysts can use it.