Databricks Reviews & Product Details

As a data engineer, Databricks has become my go-to platform for end-to-end data work. The ease of use is outstanding notebooks, Delta Live Tables, and Genie all have intuitive interfaces that reduce rampup time significantly. Implementation was smooth thanks to excellent documentation and responsive customer support that actually resolves issues fast. I use it daily, and the sheer number of features from Unity Catalog to AI/BI Genie keeps growing. Integration with cloud storage, BI tools, and ML frameworks is seamless, making it a true unified platform. Review collected by and hosted on G2.com.

One challenge is the lack of cost transparency at a granular job level it's difficult to pinpoint exactly which pipeline or notebook is driving up DBU consumption without investing in custom monitoring. Auto scaling clusters, while powerful, can silently balloon costs overnight if not carefully configured with proper limits. Additionally, the SQL warehouse tiers can be confusing to choose from upfront, making budget planning tricky for teams. A built in cost allocation dashboard per job or user would be a huge improvement for day to day cost governance. Review collected by and hosted on G2.com.

Databricks stands out for how well it handles end to end data workflows without needing multiple tools. I can ingest raw data, transform it, and publish curated datasets from the same environment. Features like job scheduling, autoscaling clusters, and Delta tables make pipelines more stable and easier to maintain over time. I also like how version control integration keeps development organized, especially when multiple engineers are working on the same pipelines. Review collected by and hosted on G2.com.

One challenge is keeping compute usage under control, especially when pipelines scale or run more frequently. Without proper monitoring, costs can increase faster than expected. Also, debugging failed jobs can sometimes take time, particularly when dealing with complex dependencies or Spark level issues. The platform is powerful, but it expects a certain level of technical understanding to fully optimize it. Review collected by and hosted on G2.com.

What I like most about Databricks is how it simplifies the entire data workflow. Instead of switching between multiple tools for data processing, analysis, and machine learning, everything is available in one place. The notebook environment makes collaboration really smooth it feels natural to work with teammates, share code, and explain logic without extra effort.

Another thing I appreciate is the performance. Working with large datasets can usually be painful, but Databricks handles it efficiently in the background. You don’t have to worry much about managing clusters or optimizing everything manually it just works most of the time, which lets you focus more on solving the actual problem rather than dealing with infrastructure.

What also stands out is the way it handles data governance and organization. With features like centralized access control and better visibility into data usage, it becomes much easier to manage data responsibly, especially in larger projects. Overall, it gives a good balance between power and ease of use, which is why I enjoy working with it. Review collected by and hosted on G2.com.

One thing I don’t particularly like about Databricks is that it can get expensive pretty quickly, especially if clusters are not managed properly. If you forget to terminate clusters or run heavy workloads without optimization, costs can spike without much visibility at first. For teams that are still learning or experimenting, this can become a concern.

Another downside is that debugging can sometimes feel a bit tricky, particularly when working with distributed jobs. Errors are not always straightforward, and tracing issues across multiple nodes can take extra time compared to working in a simpler local environment. It requires a certain level of experience to quickly understand and fix issues.

Also, while the platform is powerful, it has a bit of a learning curve for beginners. Concepts like cluster configuration, job scheduling, and data governance are not always very intuitive at the start. It takes some hands-on time before you feel fully comfortable navigating and using everything efficiently Review collected by and hosted on G2.com.

Databricks is especially helpful because it brings data engineering, analytics, and machine learning together in a single unified platform, which reduces the need to manage multiple separate tools. Built on Apache Spark, it can process massive datasets quickly and scale smoothly as workloads grow, making it a strong fit for big data use cases. It also supports collaborative notebooks where teams can work together in languages like Python and SQL, which makes it easier for data scientists and engineers to collaborate effectively.

With its lakehouse architecture powered by Delta Lake, Databricks combines the flexibility of data lakes with the reliability of data warehouses, helping ensure better data consistency and performance. In addition, it integrates with tools like MLflow to streamline the machine learning lifecycle end to end, from experimentation through deployment. Overall, Databricks simplifies complex data workflows, improves performance, and helps organizations build scalable data and AI solutions more efficiently. Review collected by and hosted on G2.com.

Databricks does have some limitations, although many of them feel more like trade-offs than outright negatives. A frequently cited drawback is cost: while the platform is flexible and scalable, expenses can rise quickly if clusters aren’t managed carefully. At the same time, that cost often reflects its ability to handle very large workloads efficiently when it’s properly optimized.

Another consideration is the learning curve, especially for beginners who aren’t familiar with Apache Spark or distributed systems. That complexity can be challenging at first, but it also comes with the benefit of powerful capabilities once you get comfortable with it. Some users also find that debugging and performance tuning are less straightforward than with simpler tools; however, Databricks offers detailed monitoring and optimization features that can make these tasks easier over time.

Finally, because it’s a managed platform, there can be a sense of reduced control compared with fully self-managed systems. In return, it removes much of the operational burden that comes with infrastructure management. Overall, while these areas may be seen as the “least helpful” aspects, they’re often balanced by the platform’s scalability, integration, and productivity gains. Review collected by and hosted on G2.com.

What I love most about Databricks is how fast and connected everything is.

Compared to other platforms, it handles heavy big data pipelines without breaking a sweat. But the best part is how easy it is to use that data once it's processed.

Whether I need to build a quick analytics dashboard or train custom machine learning models specific to our data, it all connects seamlessly. It just takes the headache out of moving data around and lets you do everything in one place. Review collected by and hosted on G2.com.

If I had to choose what I dislike, it mainly comes down to the cost and how complex it can be.

First, it can get expensive very quickly. If you’re not careful about managing your computing clusters and shutting them down when you’re done, the bills can creep up on you.

Second, it can sometimes feel like overkill for simpler tasks. Since it’s built for massive data, having to dig through complicated error logs when something breaks can be a real headache compared to using lighter tools. Review collected by and hosted on G2.com.

The unified platform experience is genuinely hard to beat — having MLflow for experiment tracking, Unity Catalog for governance, vector search, and serverless endpoints all in one place removes so much infrastructure friction. Feature engineering pipelines and model deployment feel cohesive rather than stitched together. The SQL warehouse + notebook hybrid workflow also makes it easy to hand off between data engineering and ML work without context switching tools. Review collected by and hosted on G2.com.

Serverless endpoints have some sharp edges — Spark context initialization behaves differently than in interactive clusters, which can cause silent failures if you're not careful about where you initialize things. Cold start latency on serverless is also noticeable for low-traffic production endpoints. Documentation around some of the newer features (like vector search index configs) tends to lag behind the actual product behavior, so you end up doing a lot of trial and error. Review collected by and hosted on G2.com.

We used to use ADF to get data from SQL Server and then work on it in Databricks before putting it into Salesforce. The whole process took a time more than an hour because ADF added extra work.

Now everything happens inside Databricks. We transform the raw data in Databricks and put in into Salesforce all in one place. This has made the whole process much faster, it now takes 10 minutes. That is an improvement from what we had with ADF.

Delta Lake has also been really useful. It helps us keep track of changes and go back if something goes wrong. We can see what happened before . Fix mistakes easily.

Delta Lake also makes sure the data is good before it goes into the pipeline. It stops data from getting in and causing problems later on in Salesforce. This makes the whole process more reliable and easier to take care of. Review collected by and hosted on G2.com.

Databricks is really good at what it does.. Sometimes it takes a while to get the cluster up and running.. The user interface is slow at sometimes. This can be annoying when we are in a hurry to get things done for Salesforce. The Salesforce connectors in Databricks can be a bit tricky to work with. They often need to be set up right and do not work as we expect. This means we have to put in work when we are trying to figure out problems or keep an eye on the pipelines, in Databricks for Salesforce. Review collected by and hosted on G2.com.

Among the various platforms I’ve worked with, Databricks stands out as a genuinely cohesive environment. It feels less like a bundle of disconnected features and more like a unified workspace—one that can evolve alongside the teams using it. The interface is intuitive enough to lower the barrier to entry, while still delivering the depth and power needed for heavy-duty engineering.

One of its biggest strengths is how it consolidates the data lifecycle. By bringing engineering, data science, and SQL analytics under one roof, it helps dissolve the silos that often lead to “data drift” and miscommunication between departments. In practice, it also simplifies the underlying infrastructure, replacing a dozen specialized (and sometimes conflicting) tools with a single, clearer source of truth.

Beyond simply “keeping things clean,” the platform also shines when it comes to collaborative transparency. With notebooks and experiments shared in real time, the gap between an initial data idea and a production-ready model can be dramatically shortened. On top of that, its commitment to open standards like Delta Lake means you’re not boxed into a proprietary black box—you’re building on a foundation that aligns with the broader data community’s direction. Overall, it strikes a rare balance: a polished, user-friendly wrapper around some of the most powerful distributed computing engines available today. Review collected by and hosted on G2.com.

The “Big Task” Breakdown

When Genie processes a large volume of data, it often ends up sending a huge amount of JSON back to the browser so it can render those tables and visualizations.

Memory overload: Browsers (and especially Chrome) can be real memory hogs. If a Genie response includes a very large result set or a massive execution plan, RAM usage can spike quickly, which can lead to that familiar “Not Responding” hang.

The “DOM” lag: Every row in a table and every line of code becomes an element the browser has to keep track of. As you scroll or type, the browser has to repaint thousands of these elements. When the task is too large, the browser’s main thread can get tied up rendering, and your typing starts to feel like it’s trailing behind by a few seconds. Review collected by and hosted on G2.com.

Genie Code

1) Genie Code automated our ETL processes, reducing manual effort and increasing efficiency. With Agentic’s SDL, we implemented CI/CD pipelines for faster, seamless updates and deployments.

2) Genie Code streamlined complex STTM mappings, improving accuracy and speed. Agentic’s real-time updates ensured mapping adjustments were made dynamically to align with changing transaction data.

3) We defined automated unit tests using SKILL.md, ensuring data transformations are validated before deployment. This reduced errors and ensured data quality, boosting confidence in our analytics.

4) Using Skills.md, we added custom extensions to Genie Code, such as integrating third-party data for enriched reports. This agility allowed us to quickly adapt to business needs and deliver new capabilities.

5) Agentic’s SDL enabled real-time data processing, providing immediate analytics for decision-making. Our marketing and sales teams now act on fresh data instantly, improving response times and overall efficiency. Review collected by and hosted on G2.com.

Hope it can be improved in next update -

Debugging issues in complex workflows can be time-consuming due to limited visibility into intermediate data transformations.

Genie Code lacks advanced error recovery mechanisms, making it difficult to manage failures in large-scale data pipelines.

As data volume increases, Genie Code’s performance can degrade, requiring significant manual adjustments to ensure smooth operation at scale. Review collected by and hosted on G2.com.

The unified platform experience is what keeps me on Databricks. Having notebooks, pipelines, SQL warehouses, ML, and governance all in one place under Unity Catalog means I’m not constantly stitching together five different tools just to get work done.

Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries. Review collected by and hosted on G2.com.

Cost management can be tricky—DBUs add up quickly if you’re not careful with cluster sizing and warehouse auto-scaling. The pricing model also isn’t always transparent, especially when you’re mixing serverless and classic compute.

Unity Catalog is powerful, but the initial setup and the migration from legacy HMS can be painful, particularly for large orgs with years of existing Hive metastore objects. The documentation is generally good, yet it sometimes lags behind new feature releases. On top of that, the workspace UI can feel sluggish at times, especially when you’re working with a large number of assets. Review collected by and hosted on G2.com.