Systems | Development | Analytics | API | Testing

Natural Language to Data Pipeline: How to Build Migrations Without Writing Code

You build a data pipeline from natural language by describing the source, destination, and required transformations in plain English to a platform with a prompt-to-pipeline feature, which then generates a draft pipeline with inferred field mappings, transformations, and a schedule for you to review and adjust. This guide is for operations teams, data analysts, and junior team members who understand the desired outcome of a migration but don't write SQL or Python.

How to Ingest and Reconstruct Multiple Unrelated CSV Exports or a PostgreSQL Dump from an Acquired Legacy System

You ingest and reconstruct multiple unrelated CSV exports or a PostgreSQL dump from an acquired legacy system by first mapping the dump's underlying schema and relationships, then building a staged pipeline that loads raw files or tables as-is, reconstructs relationships through keys, and only then applies business logic to produce clean, usable records.

Preventing Kafka war rooms: with self-service troubleshooting

Most Kafka war rooms don't start at 2am in production. They start weeks earlier, when an engineer couldn’t explore a topic, see partition distribution, a quota or a config and shipped hoping for the best. That's the Platform Engineering waiting room: developers building apps waiting in a queue of tickets, with no self-service visibility into Kafka. That release triggers a P1 later.

How To Unlock AI Data Anywhere (Even On-Prem) for Regulated Industries

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.

The AI Opportunity Gap Is Real. It's Growing. And It Is Not About Access to Tools.

In the first part of this series, I argued that discernment, the ability to recognise when an AI-generated answer is wrong, is becoming one of the most valuable capabilities inside an organisation. The question this piece addresses is simpler and harder: who is actually being given the opportunity to develop it? The AI opportunity gap is real. It is not primarily a gap in access to tools. It is a gap in permission. And I believe that gap starts earlier than most leaders realise, often in school.

Rethinking AI Governance with Lisa Pent #Cloudera #Shorts #AIGovernance #TechRisk

New AI tools are coming out faster than ever, and your employees are working hard to stay at the cutting edge of new technology. Where does governance come into play? Explore how to govern AI without slowing down innovation in the latest episode of The AI Forecast, sponsored by Cloudera.

How Endpoint Clinical Closed the Embedded Analytics Revenue Gap

I'll be honest: one number from the latest embedded analytics research stopped the entire planning conversation for this webinar. 57% of teams with embedded analytics report no measurable business impact, and that means not low impact or underwhelming impact, but no measurable impact at all.