For 30 years, every Semantic Layer attempt failed for the same reason: the people who carry the meaning were never the ones building the layer. AI Analytics is finally changing that.
For 30 years, every Semantic Layer attempt failed for the same reason: the people who carry the meaning were never the ones building the layer. AI Analytics is finally changing that.
An edited Q&A from the Data Splash podcast, hosted by Ido Bronstein and brought to you by Upriver. Our guest is Harel Shein, Senior Engineering Manager at Datadog, where he works on data observability products. Harel previously led data engineering and R&D teams at WeWork and Astronomer, and is a Technical Steering Committee member and contributor to OpenLineage, the open standard for data pipeline lineage.
A practical framework for data platform AI-readiness - 5 dimensions, before-and-after examples, quick wins, and a scorecard you can run with your team.
An edited Q&A from the Data Splash podcast, hosted by Ido Bronstein and brought to you by Upriver. Our guest is Prakash Reddy, Head of Data Engineering & AI Enablement at Atlassian. Prakash started his career in big data when "big" meant Oracle, was one of the early data practitioners at LinkedIn leading data products for Ads, and is an active investor and advisor for startups.
A practical framework for data platform AI-readiness - 5 dimensions, before-and-after examples, quick wins, and a scorecard you can run with your team.
Lessons learned from analyzing Claude Code's leaked sources
Knowledge engineering disappeared into data pipelines and infrastructure. Now AI is bringing it back. Here's why 95% of AI investments fail without it - and what the new knowledge engineer looks like.
Why AI agents fail on data platforms - and what makes data fundamentally different from application code.
My response to a16z’s article
How Upriver & Nimble together allow building real-time pricing pipelines natively in Snowflake with UDTFs.
In this post, we’ll break down why monitoring data in motion—as it’s pulled, interpreted, and acted on—is essential for building reliable agentic systems
Shifting Data Left: Data Quality Starts at the Source Giving data producers full ownership of their data unlocks faster feedback, fewer silos, and better quality — but only if they have the right tools
Bring your messiest ticket. Our agent solves it before your coffee gets cold.
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