About Databricks
I recently had the chance to dive into Databricks, the unified data and AI platform that’s all the rage among data scientists and business analysts. At its core, Databricks combines a data lakehouse architecture with business intelligence features, giving users a powerful toolkit for storing, processing, and analysing heaps of data. The platform also enables machine learning model development, making it a one-stop shop for all your data needs. But let’s not kid ourselves; while it sounds fantastic on paper, its execution is a mixed bag.
One of the standout features of Databricks is its ability to conduct natural language querying. This means that even those who aren't data whizzes can ask questions in plain English and get meaningful insights back. This is a huge plus for teams that may not have a dedicated data analyst at their disposal. Moreover, the AI-generated dashboards are pretty impressive, allowing users to visualise data trends effectively. However, the dashboard customisation options left me wanting more; I found them a bit limiting, especially if you’re used to the flexibility offered by competitors like Tableau.
Pricing is another area where Databricks tries to shine. While the platform offers a free tier, it’s essential to note that this free option is somewhat restrictive. If you're a small business or a solo entrepreneur, you might find the features in the free tier quite basic—good for a taste, but not enough to drive real value without upgrading. The paid plans can be quite steep, particularly for those just starting their data journey. So, it’s crucial to weigh the cost against the value it brings to your specific use case.
In my experience, Databricks is particularly suited for larger organisations that have the budget to invest in a full-fledged data solution and the expertise to make the most of it. However, smaller teams or those just dipping their toes into data analysis might feel overwhelmed by its complexity. If you're looking for a platform that offers a bit of everything but doesn't quite nail any single aspect, Databricks might be worth your consideration, but be prepared to navigate its quirks and limitations.
Our Review
Verified 11 May 2026Reviewed by Delv Editorial, Delv Team
When I first started testing Databricks, I was excited. After all, it’s touted as the unified data and AI platform that can handle everything from data storage to machine learning model development. I mean, who wouldn’t want that? However, as I dug deeper, it quickly became clear that, while it has a lot of potential, it also has its fair share of quirks that might leave some users scratching their heads.
One of the features that genuinely impressed me was the natural language querying capability. I can’t tell you how refreshing it was to type in a simple question and get back insightful data without having to wrestle with complicated SQL queries. This is a real win for teams that might not have a dedicated data analyst on hand. The AI-generated dashboards were another highlight, as they allow for quick visualisation of trends. But here’s the kicker: the customisation options are pretty basic. If you’re someone who thrives on tweaking every little detail of your data visuals, you might find yourself frustrated.
On the flip side, I must mention the pricing. While there’s a free tier, it’s quite limited, and I found myself quickly hitting the walls of what it could do. If you want to harness the full power of Databricks, you’ll likely need to opt for a paid plan, which can be a bitter pill to swallow for smaller businesses or startups. It feels like the platform is designed with larger organisations in mind, which is a shame because it means those smaller teams might miss out on some fantastic features.
In comparison to competitors like Tableau or Power BI, Databricks offers a more extensive data processing capability, but it lacks the intuitive user experience that makes those alternatives so appealing. If you're a data team in a large corporation needing to analyse complex datasets, Databricks could be your best friend. However, if you’re just starting or working solo, you might want to consider other options that won’t leave you feeling overwhelmed and under-resourced.
Overall, Databricks has some fantastic features but also comes with a steep learning curve and some limitations that can’t be ignored. If you’re willing to invest the time and resources, it can be a powerful tool. But for those looking for something more straightforward, you might find the alternatives more appealing.
Getting started with Databricks
In this guide, you'll learn how to set up your Databricks account and perform your first data analysis task. By the end, you'll be able to navigate the platform and start working with your data efficiently.
Step 1: Sign up and set up
Step 2: Your first notebook
```python
print("Hello, Databricks!")
```
Step 3: Get better results
```python
df = spark.read.csv("/path/to/your/file.csv", header=True)
df.show()
```
Pro tip
Use the command palette by pressing `Cmd + Shift + P` (Mac) or `Ctrl + Shift + P` (Windows) to quickly access functions, create new notebooks, and navigate your workspace without using the mouse.
Common mistake to avoid
A frequent error is not setting the correct cluster when running your code. Make sure you have a cluster running before executing any commands. You can check this by clicking on the “Clusters” tab in the left sidebar and starting a cluster if necessary.
The Verdict
Databricks offers a comprehensive data and AI solution that shines for larger organisations with complex data needs. However, its steep learning curve and limited free tier make it less ideal for smaller teams or individuals. If you're on the hunt for a powerful data platform and have the resources to support it, give Databricks a try; otherwise, consider simpler alternatives.
Best For
- Data teams in large organisations who need robust collaboration and analysis tools
- Business analysts looking for intuitive data querying without deep technical skills
- Companies implementing machine learning models that require a comprehensive data solution
- Fast-paced businesses needing real-time data visualisations for immediate decision-making
- IT departments requiring scalable solutions for handling extensive datasets
At a Glance
Databricks is a unified data and AI platform that offers a blend of data lakehouse architecture and business intelligence capabilities. It’s particularly useful for larger organisations looking to analyse vast amounts of data and develop machine learning models, but its pricing and complexity might deter smaller teams.
Strengths
- +Natural language querying simplifies data analysis, making it accessible for non-technical users who need to extract insights without deep data expertise.
- +The collaborative environment allows multiple users to work on projects in real time, enhancing teamwork and project efficiency across different departments.
- +AI-generated dashboards provide a visual representation of data trends, enabling faster decision-making based on real-time insights.
- +The platform is built for scalability, meaning it can handle vast amounts of data, making it suitable for enterprises with extensive datasets.
- +Integration capabilities with various data sources facilitate a more comprehensive data strategy, allowing businesses to pull in data from multiple points effortlessly.
Limitations
- -The free tier is quite limited and might not offer enough functionality for users who want to explore its full potential, making upgrading almost necessary for serious use.
- -Customisation options for dashboards are somewhat lacking, which can frustrate users who want to tailor their data visualisations more precisely to their needs.
- -The learning curve is steep, especially for those new to data analysis; users may need significant training to utilise the platform effectively.
- -Performance can be inconsistent, particularly when handling large datasets, leading to slow processing times that could hinder productivity.
- -Customer support can be hit or miss, and users may find themselves searching through documentation rather than getting timely assistance when issues arise.
Use Cases
- -Data teams in large corporations that need to analyse complex datasets and require robust collaboration features to work across departments.
- -Business analysts looking to generate insights quickly using natural language querying rather than traditional querying languages.
- -Companies wanting to implement machine learning models but lacking the technical infrastructure to do so effectively.
- -Organisations that require real-time data visualisations to inform immediate decision-making in fast-paced environments.
- -IT departments needing a scalable solution that can integrate various data sources and support extensive data processing requirements.
