Right now, AI is everywhere. Just about every boardroom conversation, every roadmap, every vendor pitch starts and ends with, “So, tell me about your approach to AI…”
Not to diminish the hype – but, you can’t leverage AI if your data isn’t ready for it.
Many large organizations have data silos, stale data pipelines, and a mix of structured and unstructured data that’s hard to pull together and gain insights that help drive business decisions.
This phenomenon is exactly why many large enterprises are looking to leverage Databricks. Databricks is not just another analytics tool; it has been designed to break down those silos, handle unstructured data, and get it into shape for deeper analytics, machine learning, and generative AI.
What is Databricks?
At its core, Databricks brings together the best of two worlds: the flexibility of a data lake (where you can store all kinds of raw data) and the reliability of a data warehouse (where data is structured and ready for fast queries). That’s why it’s called a Lakehouse Platform.
Here’s what makes Databricks stand out:
- Delta Lake: This is akin to a storage layer that keeps your data clean and reliable. It enforces rules (so “bad” data doesn’t sneak in), supports ACID transactions (this basically guarantees that multiple jobs writing to the same table won’t corrupt the data, and that the data itself can return to a previous recoverable state), and even offers “time travel” (you can look at your data as it WAS, at a certain point in the past). In practice, this means you can trust your data even when hundreds of processes are writing to it at once.
- Apache Spark Under the Hood: Apache Spark is a distributed computing framework. In layman’s terms this means that instead of one computer doing all the work, Apache Spark spreads the load across many machines. That makes heavy data jobs (like, cleaning messy datasets or training a machine learning model) finish in a fraction of the timeframe, (we’re talking minutes or hours) instead of days.
- Unity Catalog: Think of this as the control center for data governance. It manages who has access to what data, tracks where that data comes from (called lineage), and ensures audits are consistent. So, regardless of whether your teams are writing queries in SQL, using Python notebooks, or building machine learning data pipelines, the rules ALWAYS stay the same.
- MLflow: This is a built-in tool for machine learning. MLFlow lets teams track “experiments” regarding their data, register scalable data models that are also reliable enough to be repeated over and over once we’ve established they’re successful at analyzing what we need them to analyze, and move them into production without patching together third-party tools. In other words, data scientists don’t have to reinvent the wheel each time they test or deploy a model.
As you can see, instead of stitching together three or four different platforms, Databricks gives teams one place to do it all – everything from data ingestion, transformation, analytics, and then to machine learning. This cuts costs, reduces complexity, and speeds up delivery.
What’s new in Databricks? And, what direction is Databricks moving?
Over the past couple of years, Databricks has rolled out innovations that go far beyond incremental feature updates. Each one is aimed at solving a real pain point for businesses and technology teams alike:
- Unity Catalog → As mentioned earlier, this means that even though your systems speak different languages, Unity Catalog “translates” them into something that is the exact same and therefore measurable. It manages permissions, tracks lineage, and supports audits across SQL, notebooks, Python, and ML pipelines.
Why it matters: Compliance gets easier, audits are less painful, and tech teams stop wasting cycles stitching together multiple governance tools. -
Apache Iceberg Support → In the past, every platform (Databricks, Snowflake, Hive, Presto, etc.) had its own way of storing tables. Essentially, that meant vendor lock-in: if you wanted to switch engines or run multiple in parallel, you were stuck with painful migrations.
Databricks now works with Iceberg, an open table format that plays nicely with other engines. Apache Iceberg is an open standard that makes those tables “portable.”
Why it matters: You’re not locked in. Your data strategy can evolve without costly rewrites or migrations. - Databricks Genie → A natural-language assistant, Genie lets business teams query data in plain English, without writing SQL.
Why it matters: Non-technical users (often leadership teams in marketing/finance/sales who track ROI and budgets) get answers faster, while data teams aren’t stuck fielding endless one-off report requests. - Lakebase + AI Agents → Lakebase is a new type of database Databricks built. It’s Postgres-compatible, which means it speaks the same “language” as one of the most popular databases in the world (Postgres). This matters because it makes developers’ lives easier – in that they already know how to work with it.
On top of Lakebase, are AI agents which are artificial intelligence assistants you can actually build in the Databricks ecosystem, and they can use your company’s real, governed data. So, instead of being a generic chatbot, they’re connected to the data that actually matters to your business.
Why it matters: This takes Databricks well beyond analytics. Imagine customer service bots that pull from governed data, or supply chain assistants that surface risks in real time.
Taken together, these aren’t just “nice to have” upgrades they show that Databricks is evolving intentionally into an enterprise hub for AI-native development. And data governance solutions, data openness, and usability will all come together under one roof.
Why should my business care about Databricks? What’s in it for me?
Let’s translate the technology jargon into real business impact:
- Data governance solutions that scale – Instead of every team creating their own rules for who can see what, Databricks sets one standard across the board. This data lineage makes auditors happy because they get a full history of who touched the data and when. For example, a hospital can track exactly which doctor accessed patient records, on what type of device, on which browser, which time it happened, and even if they went in and out of the records multiple times – without slowing anyone down with extra hoops.
- Fewer instances of silos – Most companies have structured data (like transactions in a database) and unstructured data (like images, call transcripts, or sensor logs). Normally, these live in different systems. Databricks brings them together in one place. That matters when you’re building AI models – like a fraud detection model that needs both transaction data and call center transcripts. Or market research for a brand that needs both quantitative and qualitative/anecdotal data.
- Faster iteration – In many companies, it can take months for a new machine learning model to go from idea to production. With built-in tools like MLFlow and Delta Live Tables, data scientists can test and deploy in weeks, and automatically retrain when new data comes in. Think about a retailer updating its demand forecast weekly instead of quarterly – it’s a HUGE competitive edge.
- Lower cost of ownership – Instead of buying and stitching together separate systems (a warehouse for reporting, a data lake for storage, and extra platforms for AI/ML), Databricks does it all in one. That means fewer vendor contracts, less integration overhead, and a smaller risk surface.
- Broader adoption – We mentioned this earlier but would be remiss not to reiterate it: With tools like Genie, non-technical users can just type a question in plain English (“Show me last month’s sales by region!”) and get answers. At the same time, engineers and analysts can collaborate in the same workspace. The result: more people using data every day, not just the data team.
So, when should you reach out for Databricks? Here’s a quick lay of the land…
Now you know what Databricks can do, you probably want to know scenarios in which it can come in handy.
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When your data is big and messy – Databricks can process enormous datasets (think billions of transactions) that would crush a traditional system. Perfect for industries like finance or retail where data volume explodes fast.
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When you’re building machine learning models – Databricks has built-in tools (like MLflow) to track experiments, manage versions of models, and move them into production without the usual manual hassle.
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When you need real-time insights – Databricks doesn’t just run overnight reports- it can stream data as it happens. Eg: fraud alerts while the transaction is still happening, IoT monitoring for equipment in the field, or live dashboards for digital apps.
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When different teams need to work together – Engineers, analysts, and data scientists usually work in different tools. Databricks gives them a shared workspace, so they’re all working off the same data instead of passing spreadsheets around.
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When you want to avoid lock-in – With support for open formats like Apache Iceberg, you’re not tied to one vendor. Your data stays portable, so if strategy shifts, you can take it with you.
All of this aside – how do you know if YOU yourself are ready to invest in Databricks?
It works when you have some foundation in place. Here’s what we recommend:
- Teams
– A small data engineering team (people who can build and optimize pipelines).
– Access to data analysts and scientists if you want to go beyond reporting into AI/ML.
– At least one person thinking about governance/compliance to set policies upfront. - Budget mindset
– Expect to start in the tens of thousands per year for modest use cases, scaling up depending on workload size.
– Compare that to the combined cost of separate tools (ETL + warehouse + ML platforms). For many enterprises, Databricks ends up being cheaper and simpler overall, but you need to plan for usage spikes. - Data maturity
– Some centralized storage should already in place (e.g., Azure Data Lake or S3).
A few clear use cases lined up: “We need real-time fraud detection” or “We want faster forecasting.” Going in without defined goals = wasted expenditures. - Cloud readiness
– Databricks is cloud-native. If you’re still mostly on-prem with no cloud policies, you’ll want to sort that out first.
In conclusion…
Databricks positions itself very well as the data and AI backbone for enterprises that want to move beyond dashboards, and into real-time decisions, AI, and a culture where data is accessible to everyone.
That said, success with Databricks isn’t automatic. It takes the right mix of people, data governance solutions, and planning to get the most value and to keep costs predictable. Organizations that come in with clear goals – whether that’s faster forecasting, better fraud detection, or simply unifying data silos – see the fastest payback.
For leaders, the question to really drill down on (and hopefully that we’ve answered) isn’t just “What does Databricks do?”, but: “Are we ready to use it, and use it WELL?”
If you’re exploring that question, let’s have a conversation. Together, we can map out where Databricks fits in your roadmap, what it would take to get there, and how to do it. Reach out to us by filling in THIS form.