What is Databricks? A simple guide to the data and AI platform

If you've ever tried to pin down what Databricks actually does, you’re in good company. One minute you hear it’s for data scientists, the next it’s a data warehouse, and then suddenly it’s all about building your own AI. It’s genuinely confusing because the platform has morphed from a specific tool for Apache Spark into a huge, do-it-all suite for pretty much anything data-related.

Why can I not understand what Databricks is? Can someone explain it to me like I'm 5?

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My goal here is to cut through the buzzwords and give you a straight answer. We'll cover what Databricks is, what people use it for, and who it's really built for. At the end of the day, it’s a single place to manage all your company’s data, from messy, raw files to sophisticated AI models.

What is Databricks?

The whole idea for Databricks came from the people who originally created Apache Spark, the open-source tool for handling massive amounts of data. Their initial goal was pretty simple: let people use Spark in the cloud without all the headaches of setting up and managing servers.

Over the years, that simple idea has ballooned into what they now call a "Data Intelligence Platform." The core of this platform is something called the "data lakehouse." It sounds like another piece of jargon, but the concept is pretty clever. It tries to give you the best of both a data lake and a data warehouse.

A data lake is like a giant, cheap storage bin where you can throw all your data in its raw, messy format. A data warehouse, on the other hand, is a highly organized system built for fast analysis and reporting. The lakehouse architecture aims to merge the cheap, flexible storage of the lake with the speed and structure of the warehouse.

A really key point here is that Databricks doesn't hoard your data in some special format you can't access. It works directly with your own cloud storage (like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage) using open formats. This means your data is always yours, and you're not locked into using Databricks forever.

Core components of the platform

Databricks isn't just one thing; it's more like a workshop with different stations for different jobs. In fact, when you log in, it often asks you to pick a "persona", basically, "what's your job title?", to show you the tools most relevant to your work.

Databricks for data engineering and ETL

If you're a data engineer, your world is all about building data pipelines. You’re the one doing the "extract, transform, load" (ETL) work: grabbing data from all over the place (databases, apps, you name it), cleaning it up, and getting it ready for others to use. Databricks is a huge playground for this. It can chew through data that's processed in big chunks overnight (batch processing) or handle data that's flowing in constantly, like website clicks (real-time streaming).

Databricks for data warehousing and analytics

After the engineers have worked their magic, the data is clean and ready for analysis. This is where data analysts step in. They can use Databricks SQL to poke around and ask questions of the data, just like they would with a normal data warehouse. It’s designed to feel familiar. They can even plug in their favorite BI tools like Tableau or Power BI to create dashboards and reports. To make sure all of this runs quickly, Databricks has a speedy query engine called Photon working behind the scenes.

Databricks for data science and machine learning

For data scientists, Databricks is where they can dig into the data, try out different algorithms, and build machine learning (ML) models. It has collaborative Notebooks, which are basically shared documents where teams can write and run code together in languages like Python, R, or Scala. It also comes with a handy tool called MLflow, which helps manage the entire lifecycle of a machine learning project, from tracking experiments to putting the final model out into the world. People in the industry call this process "MLOps."

Databricks for generative AI and LLMs

More recently, Databricks has jumped headfirst into the generative AI wave. They’ve added tools that let you build and train your own large language models (LLMs) on your company’s private data. This means you could create a custom chatbot that knows your product line inside and out or an AI that can answer questions based on your internal docs. It’s a seriously powerful feature, but it also shows just how complex the platform has become.

Common Databricks use cases: Who is it for?

With all these features, you might be wondering who actually needs Databricks. It’s definitely not a one-size-fits-all tool. It really clicks for a few specific types of companies and teams.

Companies with large data teams

Databricks is built for companies that have a whole team of data folks, engineers, analysts, and data scientists. It gives them one shared space to work on the same data, which helps avoid the classic problem where everyone has their own separate, out-of-sync copy of information.

Organizations with complex data processing needs

The platform's real superpower is wrestling with "big data." If your company is drowning in terabytes (or even petabytes) of data that would make a normal database cry, Databricks is designed for that kind of scale. It’s great at handling huge amounts of both neat, organized data and messy, unstructured stuff, which is why you see it used a lot in finance, e-commerce, and media.

Teams building custom AI/ML solutions

If your goal is to build your own custom AI or machine learning models from scratch, Databricks is a solid bet. It gives your team total control over the entire process, from prepping the data to launching the final model. This is perfect for companies where their unique AI is what sets them apart from the competition.

The challenges and complexities of using Databricks

Okay, so Databricks is powerful, but it’s definitely not a simple, "just press a button" kind of tool. All that flexibility comes with some real challenges you should know about before diving in.

The steep learning curve

Anyone who has used it will tell you: Databricks is huge. It's packed with features and settings, and it's not something you can learn in a weekend. To really get your money's worth, your team needs to know their way around things like distributed computing, data engineering, and the cloud. It takes a skilled crew and some real training to run it well.

Unpredictable cost management

Databricks has a pay-as-you-go price tag. You pay for "Databricks Units" (DBUs) whenever you're running a task. On one hand, that's flexible. On the other, it can make your monthly bill a bit of a guessing game. Trying to fine-tune your usage to keep costs down can feel like a full-time job in itself, and it's surprisingly easy to get a much bigger bill than you expected if you're not watching closely.

The gap between infrastructure and business applications

Maybe the trickiest part is understanding that Databricks gives you the raw materials, not the finished product. It provides all the power you need to process data and build models, but it doesn't build the final app for you.

For instance, say you want to build an AI to answer customer support questions. Databricks can help you train the model, but you're still on the hook for connecting it to your helpdesk, managing the chat interface, and actually automating the ticket responses. This is often called the "last mile" problem, and it’s a big one.

It's where tools built for a specific job can make a huge difference. While Databricks can process your company knowledge, a tool like eesel AI is built to take that knowledge and turn it into a working AI support agent. It connects directly with tools you already use, like Zendesk, Slack, and Confluence, and gives you a ready-to-go solution in a few minutes. You get the benefit of AI-powered support without needing a team of data engineers to build it from the ground up.

A complete breakdown of Databricks pricing

Trying to predict your Databricks bill can be tough. The pricing is all based on usage, measured in something called a Databricks Unit (DBU). Think of a DBU as a unit of processing power that you pay for by the second whenever your system is working. The price of a DBU changes depending on what you're doing.

Here's a quick look at the starting prices for their main services:

One big thing to keep in mind: these prices are just for Databricks. They don't include what you have to pay your cloud provider (AWS, Azure, or Google Cloud) for the actual servers and storage that Databricks runs on. That’s a separate bill, and it can be a big one.

This pay-as-you-go model is nice for data teams that need to scale up and down, but it can give finance departments a headache. When you're trying to solve a specific problem like automating customer support, predictable pricing is often a lot easier to manage. That's why platforms like eesel AI offer simple monthly plans based on how many AI answers you use, so you know exactly what your bill will be. No surprises.

This video provides a great introduction to the core components of Databricks, including Spark, Delta Lake, and MLflow.

Is Databricks the right tool for your team?

So, should your team use Databricks? Here’s the bottom line: it's a beast of a platform for companies that need to handle huge amounts of data and build custom AI from the ground up. Its biggest advantage is being a flexible, open sandbox where a skilled data team can build just about anything.

But all that power comes at a cost: it's complex, takes a long time to learn, and the pricing can be a handful. It’s a tool for builders, teams who have the time and skills to make the most of it.

If your main goal is to solve a clear-cut business problem, like reducing customer support tickets or setting up an internal helpdesk for your team, you probably don't need to bring in a tool as big and complex as Databricks. A solution designed for that specific job, like eesel AI, can get you there much faster. It hooks into the tools and knowledge bases you already have, letting you launch a helpful AI agent in minutes, no data engineering degree required.