Databricks Archives - unifeye.ai

Why the Future of Analytics Is Not Dashboards, It’s Conversations

Unifeye Team — Wed, 08 Apr 2026 14:28:13 +0000

The shift we are seeing in analytics is not technical. It is organisational.

For years, organisations have invested heavily in dashboards, semantic layers, and modern data platforms. And yet, the same question keeps coming back from the business:

“Why does it still take days to get a simple answer?”

At first glance, it feels like a tooling problem. It isn’t.

It is a consumption problem.

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Databases Were Built for Stability. AI Demands Adaptability

Unifeye Team — Tue, 17 Feb 2026 12:43:03 +0000

I was in the room at Databricks Summit San Francisco when Lakebase was first announced. At the time, it felt bold, almost provocative, to talk about operational databases as ephemeral, serverless infrastructure running directly on the lake.

Now, Lakebase PostgreSQL is generally available on AWS and beta in Azure, and the significance of that moment is much clearer. This isn’t just a new Databricks product reaching GA, it’s a signal that databases themselves must adapt to the realities of an AI-native world.

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Where Analytics Become Decisions: A Look at Databricks Apps

Unifeye Team — Wed, 04 Feb 2026 12:05:34 +0000

Within Databricks, Dashboards are frequently used to surface data to this audience and provide quick internal visibility.

However, anyone who has relied on Databricks Dashboards as a presentation layer will be familiar with their limitations. Interactivity is constrained, large datasets are often truncated, version control is minimal, cross-dashboard interactions are not supported, and geospatial visualisation options are fairly limited and difficult to customise.

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Data Platforms That Deliver – 10 Product Thinking Ideas To Apply

Unifeye Team — Thu, 22 Jan 2026 17:11:11 +0000

Picture the scene…
You’re three years in, millions spent and a technically impressive data platform has now been sitting proudly in production for a few months.
And yet, wider adoption is anaemic. Business units are still building shadow systems and the data team spends most of their time fielding complaints and one-off manual requests.
Unfortunately, this is not as unique a case as it should be with industry research suggesting anywhere from 60-85% of data platform deliveries fail to deliver on their promise. The interesting wrinkle, though, is that these failures are not primarily technical but are rooted in process and mindset, especially neglecting user needs, misaligned outcomes, lack of ownership and absence of effective feedback loops between technology teams and end users.
This is where product thinking can bring real tangible benefits. At Unifeye we've built our practice around a simple principle: data platforms succeed when they're treated as products, not standalone projects. That means dedicated ownership, user-centric design, continuous feedback and value-based measurement all underpinned by adaptive delivery practices and organisational change support.
The reality is your data platform is a product and even if you don't treat it that way your users will. They're making adoption decisions, comparing it to alternatives and voting with their feet when it doesn't meet their needs.

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Traditional ML meets Databricks’ AI/BI Genie

Unifeye Team — Thu, 11 Dec 2025 15:14:49 +0000

For the Databricks Free Edition Hackathon, I wanted to show that traditional machine learning still has a big role to play today, and how it can work hand in hand with Databricks’ newer AI tooling. As a concrete use case, I built a recipe recommendation engine that suggests relevant recipes to users: classic natural language processing (NLP) and topic modelling structure the data, and AI/BI Genie helps surface that value for end users. Both approaches work together rather than replacing one another.

I have always been interested in using NLP tools to analyse classical Arabic texts, but I had never built an end to end solution in Databricks that brings an NLP pipeline to life. This felt like the perfect opportunity to do exactly that.

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Predicting Power Grid Blackouts from Space Weather

Unifeye Team — Tue, 09 Dec 2025 17:02:21 +0000

Hackathons give you the freedom to approach problems from different angles. Most of the time you're handed a problem and asked to solve it, but with a broad scope I decided to flip that - find interesting data first and then discover what problem it could solve.

I'm drawn to data engineering because I like understanding how systems work, and that curiosity extends to physics in my spare time. Solar flares seemed like fascinating territory to explore. I'm no expert, but I knew they can cause serious problems for electrical grids, especially aging infrastructure. The question formed: what if we could predict these events days in advance and help grid operators prepare?

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Turn SharePoint into a Smart, Searchable Data Source with Lakeflow Connect

Unifeye Team — Wed, 03 Dec 2025 10:00:46 +0000

In today’s data-driven ecosystems, silos are the enemy of agility. Whether it's marketing assets buried in SharePoint folders or operational logs scattered across cloud drives, fragmented data slows down insights and complicates governance. The Lakehouse architecture, pioneered by Databricks, offers a unified solution, combining the reliability of data warehouses with the flexibility of data lakes.

But to unlock its full potential, we need seamless ingestion pipelines that bridge these silos. That’s where Lakeflow Connect comes in.

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Agent Bricks: From AI Experiments to Production-Ready Intelligence

Unifeye Team — Thu, 20 Nov 2025 15:54:01 +0000

With the recent explosion of AI models and the rapid ongoing innovation in that space, enterprises are trying to make use of the technology to gain a competitive edge. But while base AI models offer general capabilities, they fall short when it comes to deeply understanding proprietary data, workflows, and domain-specific nuances. Enterprises need intelligent agents that capitalize on their most valuable resource - their data. They don’t just need Artificial Intelligence; they need Data Intelligence. This could be for many reasons, including building innovative products, getting quick insights for better decision making, and for improving productivity.

However, the task of augmenting base models with proprietary data is not easy and the path to production remains treacherous. A staggering 90% of enterprise Gen AI projects fail to reach production. Why? The reasons may be familiar...

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Where Dashboards Become Decisions on Databricks

Unifeye Team — Mon, 10 Nov 2025 14:46:59 +0000

Operations teams have always been the heartbeat of execution, people who make things happen when strategy meets reality. But in many organisations, their dashboards haven’t evolved.

Leaders still rely on fragmented reporting tools, weekly Excel dumps, and delayed performance insights. The result? Decisions made on stale data and entire teams waiting for “the next refresh.”

It’s time to change that.

This blog explores how organisations can move from manual to autonomous dashboards using Databricks AI/BI Genie, Unity Catalog, and the new Lakehouse-native Dashboards, without depending on third-party tools like Power BI, Tableau, or Palantir.

Because intelligence shouldn’t live in a licence. It should live where your data already does, inside the Lakehouse.

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Empowering Geospatial Practitioners on Databricks

Unifeye Team — Tue, 28 Oct 2025 15:23:15 +0000

Every organisation, whether managing homes, hospitals, or highways, depends on location. Where things happen shapes why they happen and what we should do next.

Yet, many teams still treat geospatial data as a specialist discipline locked inside GIS (Geographic Information Systems) tools, accessible to only a few experts This creates silos and delays, when in reality, location is everyone’s business.

It’s time to bring geospatial intelligence out of the GIS corner and into the Databricks Lakehouse, governed, scalable, and available to every data-driven team.

With Spatial SQL, Lakeflow, and Unity Catalog, organisations can now turn coordinates into context and context into action, moving beyond static maps to living, real-time insights that evolve with the world.

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