Real-Time Data Pipelines: A Business Leader's Guide to Streaming Analytics
Real-time data pipelines are transforming how businesses react to change. Learn how streaming analytics works and why it matters in 2026.
Fintel Analytics blog
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
15 of 206 posts
Real-time data pipelines are transforming how businesses react to change. Learn how streaming analytics works and why it matters in 2026.
Real-time data pipelines are reshaping how businesses make decisions. Learn how streaming analytics works, where it delivers value, and how to get started.
Most startups don't realise their data pipeline is broken until a board meeting goes sideways. Here's how to build reliable data infrastructure from day one — before it costs you.
Manual compliance is costing fintechs more than they realise — in headcount, fines, and failed audits. Here is how to build the data infrastructure that fixes it.
Silent data failures are the most expensive kind — your pipeline runs green while bad data corrupts every dashboard downstream. Here's how to build the analytics layer that catches them first.
Most startups don't find out their data is broken until a stakeholder catches it in a board deck. A proper dbt testing strategy changes that — here's how to build one.
Training-serving skew is the silent killer of production ML. Learn how to build feature pipelines that actually hold up — and stop your models degrading the moment they ship.
An internal data marketplace stops teams rebuilding the same pipelines and fighting over different numbers. Here is how to build one that actually works at startup scale.
dbt full-refresh models in BigQuery silently balloon your cloud bill as data grows. Here's how to find the offenders, understand the real cost, and migrate safely to incremental.
Your APIs process every payment, every user action, every integration event — but most fintech teams never analyse that data. Here is how to fix that.
Batch or CDC for Postgres to BigQuery? A practitioner's framework covering hard deletes, hidden merge costs and the pipeline failures that surface in production.
Fan-out joins silently inflate dbt metrics in BigQuery — revenue looks higher, churn looks lower, and nobody notices until a stakeholder asks why the numbers don't add up.
Most fintechs have a ledger. Very few have analytics built on top of it. This guide shows exactly what breaks, and how to fix it.
Half of finance teams still take six or more days to close the books. Financial close analytics fixes the broken data pipelines causing that delay — here is how to build it.
Misusing STRUCT and ARRAY types in dbt BigQuery models silently inflates your bill and slows your pipelines. Here is how to find and fix it.