Databricks Data Intelligence Platform Reviews & Product Details

The collaborative notebooks are quite useful. My team is able to write both Python and SQL within the same notebook, which really helps speed up our development cycle. The integration with MLflow is, in my opinion, the standout feature. Review collected by and hosted on G2.com.

There was nothing I disliked; everything worked as expected. Review collected by and hosted on G2.com.

The best I like about snaplogic is while doing data transformation of datasets in notebooks it has many languages to use like sql,pyspark and R. This enable me to use any language and effectively complete my requirements.data bricks has many features like Ai,unity catalog workflow managements which makes my ETL implementation ,and data integration easy and quick Review collected by and hosted on G2.com.

Under heavy usage the UI responsive for some implementations like job tracking ,Notebook versioning is a bit slugguish on web Review collected by and hosted on G2.com.

The Unity Catalog bundles useful data types together like Tables (delta), Models, Views, Functions and Volumes (for unstructured data). Additionally, the Workflows tab streamlines efficiency to run jobs and pipelines updating and engaging with big data. Review collected by and hosted on G2.com.

The worst functionality comes with the compute modes, and the restricted abilities. For example, Dedicated single-user access mode is essential to utilize ML runtimes and some Spark context access; however, Standard shared access mode is key for the latest UC functionality such as engaging with Shallow Clones properly. Review collected by and hosted on G2.com.

Databricks’ platform is a powerful collaborative tool which allows teams to work together seamlessly on data projects. The integrated environment for data processing and analytics, along with its user-friendly interface, makes it easy to visualize insights and share findings in real time. Review collected by and hosted on G2.com.

The occasional complexity in managing multiple clusters and environments, which can lead to confusion regarding resource allocation. Additionally, the learning curve for new users can be steep, making it a challenge for organizational adoption. Review collected by and hosted on G2.com.

I attended to explore how Databricks unifies data engineering, analytics, and AI. The platform’s integration with BI tools and support for Delta Lake worked well. Performance and collaboration features stood out. However, the learning curve and some UI complexities could be improved for newer users transitioning from traditional platforms. Review collected by and hosted on G2.com.

I attended the Databricks Data Intelligence Platform session but found it rather unremarkable. The features and performance were neither outstanding nor disappointing. It felt generic, with vague improvements and minimal innovation. Overall, I am uncertain about its distinct advantages, rendering my review neither particularly informative nor actionable for prospective users. Review collected by and hosted on G2.com.

What I appreciate most about Databricks is its unified approach to data engineering and data science. The platform eliminates the traditional silos between our data engineers and data scientists by providing a collaborative workspace where both teams can work on the same datasets using their preferred tools - whether that's Spark, Python, R, or SQL. The Delta Lake technology has been particularly valuable for ensuring data quality and reliability in our pipelines. The auto-scaling clusters mean we don't have to worry about infrastructure management, and the notebook interface makes it easy to document and share our work. MLflow integration for experiment tracking and model deployment has streamlined our machine learning lifecycle significantly Review collected by and hosted on G2.com.

The main challenges we've encountered are around the learning curve and cost management. For team members coming from traditional SQL backgrounds, the transition to Spark-based analytics requires significant upskilling. The pricing model can be complex to predict, especially with auto-scaling clusters, and costs can escalate quickly if not monitored carefully. The UI, while functional, can feel overwhelming for new users with so many features and options. We've also experienced occasional performance inconsistencies during peak usage times, and some of the more advanced features require deep technical knowledge to implement effectively. Documentation, while comprehensive, can be dense and assumes a high level of technical expertise. Review collected by and hosted on G2.com.

Databricks data intelligence platform is very unique and interesting product, it is very efficient and makes running programs so much faster and easier. Over the cost-efficient price and the uses of this platform bring so appealing is what made me choose this product. Review collected by and hosted on G2.com.

Someone I disliked about Databricks Data Intelligence Platform is it is a big slow at times when multiple users are using it which could be worked on. Review collected by and hosted on G2.com.

Honestly, it’s how it turns the chaos of raw data into something beautifully orchestrated—like jazz, but with less saxophone and more Spark.

I love that it’s not just a tool—it’s an entire ecosystem. It brings data engineering, analytics, and AI into one unified environment. That means I don’t have to duct tape a dozen tools together just to move a pipeline forward. Fewer silos, more flow. And the fact that it’s built on open standards like Delta Lake and Apache Spark? Chef’s kiss.

The collaborative features are also huge. Being able to work seamlessly across teams in notebooks, share insights, and iterate in real time? It’s like Google Docs for data nerds, but smarter and with fewer formatting headaches.

Oh, and Unity Catalog? Game-changer. It gives me visibility and governance without making me feel like I need a second job in security.

So yeah—what I like most is that it makes doing complex things feel, well… less complex. It’s not just about managing data—it’s about getting real intelligence out of it, fast. And that’s exactly the kind of velocity I like. Review collected by and hosted on G2.com.

First off, pricing transparency can be… let’s call it “mysterious.” You think you’re just running a simple job, and then BAM—surprise costs like it’s your birthday, but the gifts are all invoices. I’d love a bit more clarity and control there, especially when you’re trying to scale responsibly. Review collected by and hosted on G2.com.

This lake house architecture brings the best of both data lakes and warehouses , so we don't have to deal with the unnecessary complexity. Delta lakes ensures reliability while the notebook based interface makes collaboration seamless. The platform ability to handle batch, streaming, and machine learning workloads in one place is a huge advantage. Review collected by and hosted on G2.com.

The pricing can get expensive, especially if workloads aren't optimized properly, also while the notebooks are great they could use better version control for collaborative work . the initial learning curve can be a bit steep for those new to spark but once you get the hang of it. It is powerful tool Review collected by and hosted on G2.com.

What I like the most is the ease with which I can work with data from different sources in one place. Before, I had to jump between various tools, but here everything is integrated: from ingestion to advanced analysis. Review collected by and hosted on G2.com.

What I like least is that, at the beginning, the learning curve can be a bit steep. Review collected by and hosted on G2.com.