Databricks Data Intelligence Platform

What do you like best about the product?

I use Databricks to bring scattered client data from older systems into one workspace and prepare clean tables for our consultants. Scheduled jobs keep the tables current, so we can work from reliable figures, and I can trace numbers back to source records to support the advice we give clients. Its data connectors bring in exports with little friction, and setup was quicker than I expected—I had a working workspace and my first pipeline running within a few hours. Genie gives me a first draft of a table or transformation, so I can spend my time checking the logic instead of writing routine code. I also value Delta Lake time travel for comparing an earlier table version with the previous load, and Unity Catalog’s lineage view for seeing which source tables feed downstream tables and assessing the impact before I change a transformation.

What do you dislike about the product?

My main reservation is the Python editor. Smarter autocomplete, clearer inline error highlighting, and stronger debugging tools would make custom scripting less laborious. Too often, I only find a small mistake after running a notebook. I’d also like clearer cost visibility, since it’s hard to tell in advance how much a large job will consume.

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

I use Databricks to bring scattered client data from older systems into one workspace and keep clean tables current with scheduled jobs, so our consultants can analyze reliable figures. I can quickly trace questioned numbers to source records, keeping our advice to clients well supported.

What do you like best about the product?

Lots of helpful vendors willing to assist with questions or issues

What do you dislike about the product?

Not much just that Genie does not have a powerpoint creation feature

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

Conversational analytics for dashboards

What do you like best about the product?

Everything in one place. SQL and Python in the same notebook, right on top of the medallion tables, no moving data around. Delta history is lifesaver when numbers don't match

What do you dislike about the product?

While it has significantly become better, still clusters can take a while to be activated. Also the dashboard has significantly improved though not there yet tbh.

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

Before the data was spread across different places and checking report numbers took forever. Now the whole pipeline lives in one place, so I can trace a number from raw events to the final reports and validate it much faster.

What do you like best about the product?

What I like is that, from day one—on that very same day—I can move straight into a production workflow without having to set up the infrastructure from scratch or request permissions from another team. I really like that.

What do you dislike about the product?

For now, I’d simply like the costs to be more predictable and transparent, both at the project level and across the team.

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

Now, with Databricks, everything works smoothly because it brings processing, storage, orchestration, governance, and collaboration together in one place. I couldn’t ask for more, and that’s why we’re so happy with it.

What do you like best about the product?

I appreciate how the platform adapts to my scaling needs, even when my workload fluctuates—ranging from small-scale tasks one day to processing historical behavioral data the next. Throughout these shifts, the system remains reliable. I also particularly value that it serves as a shared space where my Growth Marketing team can collaborate with the engineering, data science, and sales teams, all within Databricks.

What do you dislike about the product?

I expect the control panel to become more intuitive for business users, and for a more natural bridge to be created between campaign-related queries and analysis notebooks.

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

It resolves metric-related issues that arise when marketing, sales, and product teams measure different things using different definitions, which can make decision-making political rather than analytical. It also addresses, in particular, the need for speed in experimentation: testing a growth hypothesis requires up-to-date, reliable data.

What do you like best about the product?

Good tool for engineers even with low coding knowledge

What do you dislike about the product?

Need to spend good amount of time to figure out optimal settings to optimize usage cost

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

Helping with connect with internal datalake and run optimization problems

What do you like best about the product?

As an Azure data engineer, I like Databricks because it gives me the flexibility to build data pipelines that can handle both structured and semi-structured data. I can use PySpark and Spark SQL to clean raw files, apply business rules, and prepare curated datasets for analytics. Working with Delta Lake is especially useful when managing incremental loads and maintaining reliable data across different processing stages. My favorite features are Delta Lake, PySpark notebooks, Spark SQL, workflow scheduling, and the medallion architecture. Delta Lake supports reliable table operations, and the workspace provides the best environment for developing and testing data pipelines. Notebooks make it easy to combine code, SQL, and output results in one place.

What do you dislike about the product?

Some configurations require a good understanding of compute resources, access permissions, and workspace settings. Troubleshooting slow jobs can also take time when data is skewed or transformations are not optimized. Monitoring compute usage is important because poorly managed workloads can increase cloud costs.

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

It helps me to process data from multiple sources and transform it into a consistent format for downstream reporting. In an Azure environment, I can work with files stored in Azure Data Lake Storage, apply transformations using PySpark, and organize the results into bronze, silver, and gold layers. This makes it easier to implement data quality checks and maintain a repeatable ETL process.

What do you like best about the product?

its for all the data needs. can be scaled, room for endless new features, data governance

What do you dislike about the product?

nothing much . its pretty good and i love it

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

it can solve any data problem. sky is the limit

What do you like best about the product?

What I like best about Databricks is its ability to bring data engineering, data analytics, and machine learning together in one platform. It makes it easier to process large datasets, build data pipelines, and collaborate using notebooks and SQL. I also like its integration with cloud platforms and its support for scalable data processing, which helps simplify complex data workflows.

What do you dislike about the product?

One thing I dislike about Databricks is that it can be challenging for beginners to understand because of its wide range of features and configuration options. Cluster setup and compute costs can also be difficult to manage if resources are not monitored properly. A simpler onboarding experience, clearer cost tracking, and more beginner-friendly guidance would make the platform easier to use.

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

Databricks solves the challenge of processing and managing large volumes of data across different sources. It brings data engineering, analytics, and machine learning into one platform, making it easier to build data pipelines, transform data, and prepare it for analysis. This benefits me by helping me practice real-world data engineering workflows, improve my understanding of cloud-based data processing, and work more efficiently with large datasets.

What do you like best about the product?

I use Databricks for Unity Catalog and the infrastructure to handle all our data streams and stitch them together. I like its security, user-friendliness, and all-in-one platform.

What do you dislike about the product?

I find the LLM DBU costs can add up quickly and are noticeably more expensive than personal subscriptions, though I understand it's a different business model. I also wish Databricks managed context a little more intuitively.

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

I use Unity Catalog and Databricks’ infrastructure to handle all our data streams and stitch them together for managing university data.