Databricks Reviews & Product Details

Databricks is great because it brings everything you need for data and AI into one place.

Instead of switching between different tools for data engineering, data cleaning, analytics, and machine learning, you can do it all in a single environment. That makes life a lot easier. Review collected by and hosted on G2.com.

Databricks is not beginner-friendly. You often need solid data engineering skills to use it effectively.

Reviews point out that while Databricks is extremely capable, it’s “a high‑end workshop” that requires expertise and is not easy for less technical teams.Databricks uses cost units (DBUs), which many people find difficult to estimate and manage.

Even expert reviews highlight that its pricing is famously complicated and can hide unexpected costs. Review collected by and hosted on G2.com.

Databricks collaborative notebooks are really useful and let me work in whatever language I need to meet my requirements effectively. The ability to mix Python, SQL and even Scala within a dashboard makes collaboration and teamwork much smoothet. I also appreciate how easily it integrates with other tools and cloud platforms, so it fits into my existing workflows without very little friction. Review collected by and hosted on G2.com.

I like their customer support and the frequent updates are a big reason this has become my favorite for data management, I also appreciate how well it integrates with external tools like Power BI for reporting its really good. Review collected by and hosted on G2.com.

Everything is centralized is a single dashboard spark jobs, notebooks and data pipelines. Autoscaling and auto termination genuinely help keep costs under control, and we could was a pleasant surprise that both run smoothly without any noticable lag. Sharing notebooks with the team is straightforward and cuts down on alot of back and forth. Review collected by and hosted on G2.com.

Finding older queries is really paunful. Anything beyond a few weeks becomes hard to track down, which makes it difficult to keep my data to day work flowing smoothly and to continue working without constant interruptions. Review collected by and hosted on G2.com.

It brings all the tech stacks together in one platform—data engineering, machine learning, AI, and data management—so everything is in one place. It also includes advanced features that make the platform feel complete and capable. Review collected by and hosted on G2.com.

We need more open-source, direct connectors to both legacy and current-generation platforms to enable better data extraction. These connectors should support real-time extraction as well as real-time data rendering. Review collected by and hosted on G2.com.

The best part of databricks data intelligence is that it's very simple to use and have lot of fetures that helps us develope data pipeline and AI, and it help us us to easy implemet GenAI mostly RAG in production. LakeFlow made inetegration very esy with different sources as low code no code approch. Review collected by and hosted on G2.com.

Currently I think we don't have such dislike things in databricks as it's enabling new feature on daily basis and it's helping developers and analyist most. Review collected by and hosted on G2.com.

What I like most about Databricks is how it brings data engineering, analytics, and machine learning together in one platform. It streamlines the entire data pipeline—from ingestion and transformation through to serving—so I don’t have to rely on multiple separate tools to get end-to-end workflows done.

Its integration with Spark and Delta Lake is another big plus, making it both scalable and dependable when working with large datasets. Review collected by and hosted on G2.com.

One challenge with Databricks is cost management and visibility. Since compute is abstracted through clusters and jobs, it can sometimes be difficult to track and optimize costs without additional monitoring or governance in place. Review collected by and hosted on G2.com.

I like the concept of transforming data into visuals for each table. Genie Code also helps with debugging and validating the data, which makes it easier to trust what I’m working with. Review collected by and hosted on G2.com.

As a proprietary platform built on open-source foundations, it can still introduce vendor lock-in risks, particularly through components such as Unity Catalog and its custom APIs. Review collected by and hosted on G2.com.

Databricks transformed my day-to-day workflow, taking me from constant SQL Server/ADF headaches to scalable, unified analytics. Migrating stored procedures into Spark SQL notebooks was surprisingly smooth, and using Delta Lake MERGE instead of complicated UPDATE logic saved me weeks of rewriting.

The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly.

On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that.

If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank. Review collected by and hosted on G2.com.

The cluster reconnects fairly often, which can be disruptive during active work sessions. Also, when I run complex or heavy queries, I notice clear lag in response times, and that slowdown can hurt productivity. Review collected by and hosted on G2.com.

The collaborative notebooks are hands-down my favorite part of Databricks. I love being able to jump into a notebook with my team, tweak Spark SQL queries live on those massive shipment datasets, and watch everything sync instantly—without any version-control.

It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver.

Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms. Review collected by and hosted on G2.com.

One key drawback is the cost management—charges can accumulate rapidly if clusters are left running, requiring careful monitoring of DBU usage and auto-termination settings.

Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential. Review collected by and hosted on G2.com.

What I like most about Databricks is how it brings data and AI into one place, so you’re not jumping between tools.

It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking.

The integration with Apache Spark helps handle large datasets without extra setup.

Overall, it just reduces the friction between raw data and actually getting useful AI outcomes. Review collected by and hosted on G2.com.

One thing I find challenging with Databricks is cost visibility-it can scale quickly, and predicting spend isn’t always straightforward.

There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs.

And for simpler use cases, it can feel like overkill compared to lighter-weight solutions. Review collected by and hosted on G2.com.