Databricks Data Intelligence Platform

What do you like best about the product?

Setting up and integration into data sources was super easy… allowed for quick ROI

What do you dislike about the product?

So far my interactions with the Databricks platform have been positive

What problems is the product solving and how is that benefiting you?

Connecting various data sources into a single platform to then consume

What do you like best about the product?

I love this platform for its capability to handle big pool of data efficiently. I love the idea of Data Lakehouse of this platform. The Collaborative work supported by this platform greatly enhances productivity and team work. In our context of using the Databricks SQL query we can easily identify the best match of entrepreneurs needed to avail a specific scheme. This saves both time and increases efficiency. The capacity of this platform to analyze the past performance and trends enable us to map out a highly targeted approach that is really efficient. I love the capability of this platform as it can identify probable stress asset which might be an issue for future investment. This makes us rethink our strategy and approach towards the best and efficient way forward. I feel that the platform have a user friendly interface. I love the integration ability of this platform as it integrates with most of the major platform. This makes this platform more robust and powerful. The onboarding of this platform is also easy as we could easily login with our Google ID. Other than this I love the ability of this platform to create pipeline. The capability of this platform to create agents also ease up tasks. It also enhances capability to handle work load more effectively and efficiently.

What do you dislike about the product?

I love most part of this platform but I have to admit that user will need a some training for the user to be more efficient. I feel that having a more technical experience will adapt well with this platform. I also wish that the pricing of this platform was also more moderate. Frankly saying the accuracy of output of this platform depends on how clean the Data is . So there is always a chance of spilling in the unclean data. This will directly impact the result.

What problems is the product solving and how is that benefiting you?

Frankly this platform translates into assistance in accurate decision making. The decision made by using this platform directly impacts productivity and averts risk.

What do you like best about the product?

I appreciate how Databricks helps our different teams to collaborate together. A shared notebook project allows our marketing analysts, supply chain engineer, and data scientist to work together in real-time. The version history within Databricks prevents any confusion about the latest developments. I like that it helped us build predictive models to forecast demand more accurately, and we created visual dashboards that we shared with our leadership team, giving them clear regional insights. Within a few months, we saw measurable lifts in sales and improved profit margins. Databricks streamlined processes that used to take weeks of manual spreadsheets and emails, now happening in days with fresh and reliable data. The cross-departmental data sourcing it provides breaks down silos in our organization, enabling smarter and faster decision-making based on a complete picture rather than fragmented departmental views. The setup was straightforward right on the cloud, avoiding messy offline setups.

What do you dislike about the product?

There were delays in obtaining permissions for enterprise security and approval workflows while sharing data.

What problems is the product solving and how is that benefiting you?

Databricks enables real-time collaboration across teams, fosters holistic insights by breaking down departmental silos, and speeds up processes that used to take weeks. It helps in building predictive models, sharing clear visual insights, and making smarter decisions with reliable data.

What do you like best about the product?

I really love that Databricks has a UI that is essentially very simple to understand, and the categorizations of data make it easy to find and manage repositories. It's also very easy to set up jobs right on the fly without writing extensive scripts, which is a really good functionality. The native visualizations on Databricks allow me to uncover a lot of insights and make business-driven decisions. Additionally, the role-based access is very seamless, and the functionality provided by Databricks makes it very valuable. The native notebooks feature is also very, very valuable. Overall, with the amount of functionality it has, using Databricks is a buy.

What do you dislike about the product?

Maybe the multi-select cursor functionalities, which I initially had in open source Redash, might be very useful for productivity. It's a minor kind of functionality. Other than that, Databricks is really useful, and not much changes which I would recommend.

What problems is the product solving and how is that benefiting you?

I use Databricks for usage analytics, understanding data storage, uncovering insights, visualizing data, and making business-driven decisions.

What do you like best about the product?

Databricks simplifies the process of running data transformation operations on massive datasets. Although it can be a bit of a paradigm shift from classic asynchronous processing architectures, it is extremely easy to get started with. Simply put, the thing I like best about it is it's ability to do work at scale.

What do you dislike about the product?

The inability to run a copy of Databricks locally to test changes before deploying them to production is a significant hindrance. Creating per-developer staging environments might be a close solution l, but might be a lot of work to manage.

What problems is the product solving and how is that benefiting you?

Databricks is making it possible to process tremendous amounts of data efficiently, while simultaneously not requiring a large amount of engineering effort to be applied towards designing the system itself (engineers can focus on solving data problems rather than scaling problems)

What do you like best about the product?

In fact, the most valuable thing about Databricks is that you do not require worrying about looking after the Spark infrastructure. Previously, it took us so much time to configure clusters manually and here, in a few clicks, you can spin up a cluster.

The collaborative notebooks are also very much helpful. My teammates and I are able to collaborate in the same notebook and write Python or Scala or SQL in the same location and share the output in a short time. The connection to AWS and Git is also very fluid, and thus pushing code to production is not demanding a lot of effort at the moment.

What do you dislike about the product?

The most significant issue of mine is the cluster start time. There are also cases that I would simply need to make a minor change in the code and the cluster can take about 5-7 minutes to spin up a cold start. It actually disrupts the development.

What problems is the product solving and how is that benefiting you?

We are deploying Databricks to create our ETL pipelines and process large volumes of customer data every day.

Our local machines would crash prior to using Databricks due to memory problems with large datasets. At this point, we simply push it all to the Databricks cloud. It has completely addressed our scaling problems. Also, job scheduling is extremely simple here, even simple daily pipelines do not require orchestrators such as Airflow. It saves us much time in maintenance.

What do you like best about the product?

The best thing is that we don't have to do any infrastructure to manage now. My team was spending too much time on setting up Apache Spark cluster, managing yarn, and memory crashes on-premise before. With Databricks, we could – within 2-3 clicks – spin up a cluster; collaborative notebooks are very nice! Data engineers and data scientists share the same notebook, so they can collaborate on the same notebook, at the same time. We can have Python, Scala and SQL together in one place without changing any environments. Another super solid feature is delta lake; those provide us with transactions over raw data, this saved us from a lot of data corruption issues since in the past.

What do you dislike about the product?

Frankly, its cost is quite high. They charge for DBUs (Databricks Units) and then the cloud provider charge (we're using AWS). Unless you are monitoring, the bill will shoot like a rocket. At times, my developers forget to shutdown the clusters and, if auto-termination is not configured correctly, it's running all night and we get an interest big wake up call in the billing portal the next morning.

What problems is the product solving and how is that benefiting you?

We had our data in a lot of different places prior to Databricks. The marketing data was somewhere else, transactional database was somewhere else. We may have had a lot of problems with silo thinking. We are porting Databricks to implement our Lakehouse architecture.We are deploying Databricks to create our Lakehouse Architecture. Now, all raw data will be transferred to S3 and will be cleaned, processed and BI Reporting will be done on Databricks. Went a long way to help resolve our speed issue. Our daily ETLs used to run from 6 - 8 hours. Today, the same pipelines are completing within 45 minutes or less, thanks to Spark optimization in Databricks. The reporting of my business team is providing on time in the morning; thus, the decision making is very fast.

What do you like best about the product?

Managing end-to-end machine learning pipelines, specifically training and deploying multi-agent models and recommendation engines.What I appreciate most about Databricks is how it completely eliminates the coordination overhead—the fragmentation tax—between our data engineering and data science teams. Before Databricks, we were losing hours every day moving data between unmanaged data lakes, proprietary data warehouses, and our isolated machine learning compute clusters. Having MLflow natively managed inside the Databricks workspace is a massive advantage for my day-to-day workflow. I no longer have to worry about setting up tracking servers or maintaining infrastructure just to log my training metrics, because Databricks handles the automatic updates and maintenance seamlessly. Every experiment is automatically tracked, and the model registry seamlessly handles version control, making the handoff from experimentation to production deployment incredibly smooth. Additionally, the recent updates to MLflow for evaluating GenAI agents, specifically the ability to use trace-derived baselines to generate runnable evaluation scripts, have saved me countless hours of manual assembly.

What do you dislike about the product?

The transition to Unity Catalog has been a significant hurdle for our team. Upgrading our legacy workspace to support Unity Catalog's centralized access control and lineage tracking involved a steep learning curve, especially when dealing with privilege inheritance and ensuring the correct schema privileges were granted across the board. Furthermore, while the platform beautifully abstracts away a lot of DevOps work, it can obscure underlying infrastructure costs. It is far too easy for an engineer to spin up an oversized compute cluster for a simple exploratory data analysis task, leading to sudden and severe spikes in our monthly cloud bill. You have to be extremely disciplined with setting strict auto-termination policies and cluster management rules to keep costs in check. The user interface can also feel a bit tedious at times, requiring you to click through multiple layers in the Catalog Explorer just to view the model details page and trace table-to-model lineage.

What problems is the product solving and how is that benefiting you?

The primary problem Databricks solved for us was the massive bottleneck in deploying machine learning models to production. We used to struggle with the classic issue where a model worked perfectly in a local notebook but failed in production due to environment mismatches and a lack of proper version control. By standardizing on Databricks and the managed MLflow environment, we established a strict, documented approval chain that satisfies both our engineering standards and our strict compliance requirements. A real-life example of this was when we recently deployed a multi-agent system for customer churn prevention. We were able to run the inference, monitor the agent's safety and relevance metrics using MLflow's built-in judges, and continuously track the outputs all in one unified platform. This consolidated architecture cut our deployment timelines drastically and significantly reduced the time we spent debugging production errors.

What do you like best about the product?

I appreciate how Databricks brings together data engineering, analytics, and machine learning processes in a single, governed workspace. The data reliability features like automatic versioning, transaction support, and quality controls are great for maintaining consistency and audit readiness without extra manual effort. For AI-related work, I find the experiment tracking, model deployment, and governance capabilities helpful for scaling efforts securely while meeting compliance standards. I also like the cost monitoring and cluster data management tools, which provide better visibility and help control expenses as usage grows across departments. The detailed breakdowns by job, cluster, user, and workload type, along with budget and alerts, are particularly useful.

What do you dislike about the product?

There is a learning curve when first adopting Databricks, especially for teams transitioning from traditional setups. The initial setup was a little difficult for these teams.

What problems is the product solving and how is that benefiting you?

Databricks unifies data engineering, analytics, and machine learning in one workspace, boosting data reliability. It helps scale AI efforts securely, while cost monitoring tools provide visibility and control as usage expands.

What do you like best about the product?

As a Data Scientist working for a mid-size company, my main use case for Databricks is as the central engine for all of our data processing and predictive modeling pipeline. I use it every day to pull raw dirty data from our cloud storage, explore it with complicated SQL queries and then create and train machine learning models with PySpark and Python. Basically it gives our data engineering and data science teams a common place to play on the same huge data sets at the same time without having to endlessly exchange files or credentials.From a day-to-day workflow perspective, I love the fluidity of the collaborative notebook environment. The ability to work with different languages in the same workplace is a great advantage. I can perform an optimized SQL query to pull in a hefty data set in one cell, then process it in the next using PySpark, and visualize it with Python libraries straight after. This fully removes the need to constantly bounce between different tools or IDEs. Another big victory for my daily work is the out-of-the-box connection with MLflow. It makes it very easy to roll back to a previous version, automatically tracks hyperparameter tuning, compares several model runs, and manages the full lifespan of a model. I really enjoy how Databricks takes away the effort of managing Spark clusters, you can spin up a distributed cluster with a few clicks, and focus on writing algorithms vs playing DevOps.

What do you dislike about the product?

And despite all its potential, working with Databricks does come with certain daily difficulties. What is most important for a mid-sized company like us is the aggressive pricing model for compute costs. The monthly payment can get out of control very rapidly, if you’re not compulsively watching your cluster configurations and auto-termination settings especially if a high-memory cluster is unintentionally left operating over the weekend. Another major pain point is the built-in Git integration. Databricks Repos has been helpful however managing complicated merge conflicts or branch management still feels unexpectedly clumsy compared to a regular local IDE like VS Code. Lastly, the learning curve is rather severe for new employees. The user interface might be complicated and debugging distributed computing failures can be a major bottleneck for young data scientists getting up to speed.

What problems is the product solving and how is that benefiting you?

The largest basic problem that Databricks tackled for our business was breaking down the separate silos between our data engineers and data science team. We saw this effect in the real world recently when we were working on a project to build a fraud detection algorithm. In the prior approach, I would have to submit a ticket to data engineering, wait days for them to extract and clean the data, and then try to train the model locally. I would get out of date data by the time I got it, and my machine would crash all the time owing to memory constraints. I could immediately connect to our Delta Lake, utilize PySpark to process the huge data size without any memory issues and train the model on a scalable cluster, all in the same ecosystem using Databricks. This one-stop-shop decreased our model deployment duration from about a month to a couple of days, dramatically enhancing how fast we offer actionable business value.