Tableau vs ThoughtSpot

How to Build a Tableau Dashboard and Story

Step-by-step tutorial showing how to create a published Tableau dashboard and a three-point narrative story from a real dataset. The guide uses the Telco Customer Churn dataset (7,043 customers) and a public GitHub repo for data-shaping code. It stresses shaping data upstream (one row per entity, 1/0 outcome column, readable names, ordered buckets), creating a single calculated field for rates (Churn Rate = AVG([Churned])), building four focused worksheets (one point each), assembling them into a dashboard, and sequencing three story points (problem, mechanism, action). It explains Tableau Public publishing requirements (workbooks must use extracts) and gives practical UI steps and common error fixes. The guide also covers visual rules (one-accent color, bar chart accuracy) and advises documenting limitations when publishing.

• Worked example uses the Telco Customer Churn dataset on Kaggle with 7,043 customers (one row per customer).
• Author provides a public GitHub repository (telco-churn-analysis) containing the Python script that shapes the data for the example.
• Recommended calculated field for the rate: Churn Rate = AVG([Churned]) (1/0 column average).

Put Your Data to Work with ThoughtSpot in ChatGPT Work

New blog post: 'Put Your Data to Work with ThoughtSpot in ChatGPT Work' (Sep 10, 2026). Also new: '2026 Best Books on AI and Data' (Sep 10, 2026), 'Introducing SpotterCode in Developer Playground' (Sep 9, 2026), 'Trust, Tested: What Consumers Really Think About AI in Retail' (Sep 2, 2026), and 'Employee Recognition: Meet This Quarter's Icon Award Winners' (Sep 1, 2026).

ThoughtSpot Named a 2026 Gartner® Magic Quadrant™ Leader for Analytics and BI Platforms

ThoughtSpot has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Analytics and BI Platforms, with a dedicated report available for download.

Postgres pattern to query row state as of a time

The article presents a Postgres schema pattern for answering point-in-time questions like “what did this row look like at T?” by keeping a history table alongside the main table (SCD Type 4) and using a row-level trigger to mirror INSERT/UPDATE/DELETE operations into that history table. The author contrasts common alternatives — event sourcing, in-place versioning (SCD Type 2), and periodic snapshotting — and provides concrete SQL: history table creation (LIKE ... INCLUDING DEFAULTS), a trigger function that stamps valid_from/valid_to and writes tombstone rows on DELETE, a point-in-time SELECT that picks the version valid at :as_of, and a backfill pattern for pre-install rows. The piece also warns about operational concerns such as schema drift when adding columns and recommends automating schema migration for history tables.

• The article describes using an SCD Type 4 pattern in Postgres: a records_history table that mirrors records plus valid_from, valid_to and operation columns.
• A row-level trigger function mirrors every INSERT, UPDATE and DELETE on records into records_history, stamping valid_from/valid_to and writing tombstones for deletes.
• The point-in-time query uses WHERE valid_from <= :as_of AND (valid_to IS NULL OR valid_to > :as_of) with DISTINCT ON (record_id) and ORDER BY record_id, valid_from DESC to return one row per record as of :as_of.