# 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. ## 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](), [Architecture & Design](), [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 ## Building a Data Pipeline to Track Strava's Bad Events DevFeed: [Building a Data Pipeline to Track Strava's Bad Events]() Original publisher: [Read original article]() Author: Bisman Sodhi Published: 2024-01-08T20:19:46Z Content type: opinion Language: en Sources: [Strava Engineering]() Topics: [data](), [airflow](), [Amazon S3](), [Python](), [Amazon Web Services](), [JSON](), [dashboards](), [Computer science]() Tags: [airflow](), [apache-airflow](), [aws](), [data](), [data-engineering](), [data-pipeline](), [data-platforms](), [integrity](), [json](), [python](), [s3](), [software-engineering](), [strava](), [tableau](), [warehouse]() ### AI overview A software engineering intern describes building a daily Apache Airflow pipeline that extracts schema-invalid user behavior events from S3, decompresses them into JSON, and loads them into Snowflake. Staging tables protect production data from partial loads, while materialized SQL views and a Tableau dashboard improve querying and monitoring. ### Source excerpt Hi my name is Bisman and I studied Computer Science at University of California, Santa Barbara. During summer of 2022, I had the most amazing experience working as a Software Engineer Intern on Strava's Data Platform Team. In the first fews weeks, I learned the tools my team uses and then spent the rest of the time working on my project. TRACKING BAD EVENTS For my major summer project, I created a data pipeline that pulls user behavior data out of external storage and persists it in our data warehouse. Strava uses a service called Snowplow to collect this user behavior data, like loading a club page or uploading a profile photo. Sometimes, this data fails to match the schema that we've set, and a piece of data that fails this schema validation is called a bad event. Previously, these bad events were temporarily stored in an Elastic Search. Persisting this data in Snowflake, our data warehouse, makes it accessible to a wider audience. It also makes it easier to incorporate the bad events data with other services used at Strava. To start my project, I created a directed acyclic graph in Apache Airflow, a scheduling framework, using python that extracts bad events data from the S3, AWS's storage service, buckets on a daily cadence. This data was stored as gzip files on S3 which I decompressed and stored the data as JSON blobs. As I was working with billions of rows of data, it was important to maintain data integrity and take measures in case data failed to load from S3. Therefore, I loaded data into a staging table in Snowflake. The staging table ensured that if loading from S3 failed, the production table would remain untouched. This data was then loaded into the production table free of any partial data. After all the data was loaded into the production table, I created six view tables because there were six different types of bad events stored in the production table. I collaborated with our stakeholders -- data analysts -- throughout this process to craft tables bas ## 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: [Optimization](), [dashboards](), [data](), [real-time](), [Tool]() Tags: [dashboards](), [data](), [data-analysis](), [data-visualization](), [guide](), [optimization](), [real-time](), [reporting](), [tableau]() ### AI overview A beginner-oriented guide to improving Tableau dashboard performance. It covers performance recording, filtered or sampled extracts, data-source connections, live versus extract queries, and context filters. The article reports reductions in render or load time, extract time, and improvements in view throughput at Blinkit. ### 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