The best thing about Databricks is the managed Spark clusters. Earlier, setting up Apache Spark manually on AWS or Azure was a big headache. Now, with Databricks, I can spin up a cluster with just a few clicks. The auto-scaling feature works very well, when processing heavy data workloads, it automatically adds nodes and reduces them when done, which saves some cloud costs.

Also, the collaborative notebooks are amazing. My team members and I can work on the same Python or SQL code at the same time, just like Google Docs. The integration with Delta Lake is also a big plus because it gives ACID transactions directly on cloud storage, so data corruption issues are very rare now. Review collected by and hosted on G2.com.

The biggest issue is the pricing. Databricks DBUs Databricks Units are quite expensive, and if you are not careful with cluster configurations or leave a cluster running by mistake, the cloud bill will jump very high quickly. The cost management tools inside the platform could be much better. Review collected by and hosted on G2.com.

What I like the most about Databricks is the whole ecosystem. It's not easy to have everything you need in a single platform that already has access to the data by its nature. You don't have to handle complex integrations for new projects like data engineering, machine learning, creating dashboards, or developing applications. Review collected by and hosted on G2.com.

I think Databricks can improve in the complexity. It gets difficult or tricky because there are plenty of things and features, and at some point, it becomes complicated to catch all of them. The user experience can improve, especially for stakeholders that are not 100% technical. It's not easy to set up; you need to set up a lot of things, and when you just start, it's really complicated to get things done. Review collected by and hosted on G2.com.

As a researcher of AI, it seems like infrastructure is the number one problem, especially setting up clusters, building drivers, and scaling distributed training. Databricks takes care of all that by itself. I can easily and quickly deploy a cluster of nodes for GPUs with PyTorch and DeepSpeed preconfigured in a few clicks. This built-in MLflow is a lifesaver to keep track of experiments. All the hyperparameters or architecture changes with respect to an embedding model are automatically being tracked every time. ESSENTIAL: I no longer have to struggle to get clean and versioned datasets from data engineers for training purposes when working with Delta Lake. Getting around those feature stores is also very easy with the Unity Catalog. Review collected by and hosted on G2.com.

First, it's really expensive, brother. On an extremely large A100 GPU cluster, if you, or someone on your team, forget to configure the auto-terminate, you are going to have a very bleak day with finance tomorrow. Expenses can add up quickly. Additionally, although they are too lightweight to be an ideal platform for distributed deep learning, the debugging workflow may be tedious. The intersection of the computing nodes makes it difficult to find the exact PyTorch-Out-Of-Memory or CUDA-Out-Of-Memory error occurring in the Spark logs. I also feel like the native MLflow UI in Databricks isn't as advanced and specialized as some of the tools like Weights & Biases. Review collected by and hosted on G2.com.

I work as a Data Analyst and every day, I use Databricks to complete my data tasks. The best thing I like is the processing speed. We were loading large tables and it was taking too long before we could load big tables using normal databases. My rich SQL queries are very fast in Databricks due to the use of Apache Spark backend.

In addition, the Notebook feature is quite useful to me. I can create SQL code in a cell and in the next cell, I can write Python or Pandas code to do some particular data cleaning. It is also easy to connect Databricks to our Power BI dashboards. Review collected by and hosted on G2.com.

There are some things which I am facing issues with. First is the cluster starting time. In the morning, it takes 5-10 minutes to boot but once I log in. When the management requests urgent report, then I must make myself sit and wait till the cluster turns green. Review collected by and hosted on G2.com.

We have a very big dealer and distributor network throughout India in our sanitaryware business. Big sales data are being generated everyday. Speed is my favorite thing about Databricks. Whenever we use simple excel or old software before, it is always hanging. And now our company data team is churning through the millions of rows in a short time. As a Senior Sales Specialist, I am making sure that I get my territory dashboard and forecasting reports at least daily in the morning. It is bringing all disperse data together in a good manner. Review collected by and hosted on G2.com.

Worst thing is that it is highly technical software. As a sales person, I cannot apply it in locating data directly. I need to request data engineering or IT team to code or make query every time I desire some new custom report. User-non technical interface is becoming very complicated. And my management is continually saying this costs a great deal. Review collected by and hosted on G2.com.

What I like most is how Databricks brings data engineering, analytics, and machine learning together in one environment. Our teams no longer need to jump between multiple tools to build pipelines, analyze data, and train models, which keeps work more consistent and streamlined. The notebook experience is genuinely collaborative and helps us move from exploration to development much faster. Integration with Spark and Delta Lake also makes it easier for us to process large datasets efficiently and stay organized as projects grow. Review collected by and hosted on G2.com.

The platform can feel overwhelming for new users due to the sheer number of features available. Some of the more advanced configurations also require a solid understanding of cloud infrastructure and cluster management, which can add to the learning curve. Cost monitoring needs close attention as well, especially for teams that run large workloads on a frequent basis. Review collected by and hosted on G2.com.

I like Databricks for many reasons. Its version control system is a standout feature, especially with its ability to roll back time, which has really saved us when we needed to recover data or understand changes. I also appreciate the user interface and the collaborative experience it provides. The ability to comment, version, and link results makes troubleshooting much easier for us. Additionally, I found the initial setup process to be surprisingly easy, with everything working very well right from the start. Review collected by and hosted on G2.com.

When something goes wrong, the messages can be cryptic, it would be helpful to add a built-in user-friendly guide for common errors. Review collected by and hosted on G2.com.

I like Databricks for its centralized UI that allows for seamless development, collaboration, and deployments. I appreciate the Unity Catalog Governance, which helps with data sharing in a controlled yet federated manner. The platform's capability for seamless data import, quick retrofit pipelines, and automation makes my tasks more efficient. Additionally, bringing disparate data sources together and automating with notebooks to rewrite ETL processes was easy, and it quickly sped up the onboarding of legacy ML models. Review collected by and hosted on G2.com.

DABs are limited to notebooks. I want it for DLTs, ML models, Genie. GitHub integration is confusing with deployment handoffs and collaboration. Review collected by and hosted on G2.com.

As a Senior Data Scientist at a mid-sized tech company, I've used Databricks for the last couple of years, and it has really transformed our data teams. The main use case we want to process large amounts of user interaction data to create predictive models, namely customer churn, recommendation engines, and customer lifecycle value (LCV) estimation. Prior to Databricks, our workflows were very disjointed. The data engineers utilized one set of complicated tools for ETL tasks and the data scientific research group utilized completely various neighborhood environments for modelling. Databricks gave everyone a common platform and workspace, a cloud-based experience, which put everyone together under one roof.I like the ability to work with collaborative notebooks together with great computing at the same time the most. It's a significant productivity win to be able to code in Python, SQL and Scala — and, vitally, do so in the same environment as the data engineers who are creating the core pipelines. Additionally, I find the out-of-the-box integration with MLflow to be a game-changer in my workday routine. It eliminates the pain of managing version registries, tuning parameters, and more complicated model experiments. I have to also point out the ease with which they have improved cluster management. As a data scientist, I need to use a heavy machine learning model, one that is not always in use for the duration. I can start a distributed, powerful Spark cluster in a few clicks, train my model on it, and then quickly configure it to automatically kill itself after the job completes - I do not want to waste resources. Review collected by and hosted on G2.com.

The platform doesn't have exceptional user-friendliness, though, and there are some drawbacks that you'll need to navigate with care. The greatest disadvantage is the loss of control of spending if you're not careful. With so much of the complicated back-end infrastructure abstracted away, it's quite easy for a newer team member to provision an unnecessarily large compute cluster or forget to switch on auto-termination, with a very unpleasant surprise on the monthly billing statement. One must be careful about creating rigid rules for use of the workplace and tracking how it is used. Moreover, it can be unpredictable to learn the learning curve of a distributed computing paradigm that is different from the one analysts or data scientists already have experience with, such as Apache Spark. They have come a long way in introducing features that are similar to the standard Python library but for complex distributed errors, a lot of knowledge about the inner workings is still needed for debugging. The user interface can also sometimes be a bit slow and cumbersome when working with deep levels of workspace folders in which there are hundreds of notebooks from the legacy version. Review collected by and hosted on G2.com.

Databricks provides a unified platform for data engineering, analytics, and AI, reducing migration complexity.

* It supports seamless integration with cloud storage, databases, and legacy data platforms.

* Built-in scalability allows organizations to migrate from on-premises or traditional data warehouses without major infrastructure changes.

* With automated optimization and open formats like Delta Lake, data migration becomes faster, more reliable, and future-proof. Review collected by and hosted on G2.com.

The interface could be better and

The platform can have a steep learning curve for new users, especially when managing clusters, jobs, and workspace administration.

* Cost management could be more transparent, as compute and storage expenses can grow quickly without proper monitoring.

* Debugging and troubleshooting distributed workloads can sometimes be challenging compared to traditional environments.

* Some enterprise features and integrations require additional configuration, which can increase setup and operational complexity. Review collected by and hosted on G2.com.