Databricks Data Intelligence Platform

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?

The best thing I want to highlight about Databricks is the overall efficiency and data handling capabilities which has been exceptional in our case, we have used databricks for almost 3 years and had not found anything blocking our data and engineering teams.

Talking about its features as an head of quality my primary focus was to ensure the Reliability and the accuracy and performance for our machine learning models since we are using massive data to feed them and databrics has done the job perfectly for our engineering and testing teams as well.

It has allowed us to enable quality control directly into the pipeline which has automated the set rules bad records automatically before the reach our models it has saved 63.5% time in total.

What do you dislike about the product?

I don't dislike anything about databrics it has done the job perfectly has have to trigger Ci Cd pipelines through github actions using the data bricks rest API has helped to implement shift left continuous testing forever AI workloads and the AI integrations and clean dashboards has been game changer in this.

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

The major problem we had been facing was fragmented test environment and ML models were tested in silos and the test data management was logistical nightmare for us and we lact visibility into the quality of the streaming data feeding via our algorithm hair the databrics has provided us unified Lake house architecture which has solved our test data management issues and gave us escalable version control storage and as a testing leader it has allowed me to enforce strict data governance to automated quality gates across all our Ai pipelines.

It has help to reduce 73.5% production defects as well as the time to market has been reduced by 53.5% due to ML model and ETL testing. It has been a gamechanger with a great pricing and licence support.

What do you like best about the product?

I particularly like being able to cross-reference product behavior data with acquisition data to calculate insights—for example, seeing that users who arrived via LinkedIn have twice the retention rate. Another aspect I appreciate is the ability to automate reports through workflows.

What do you dislike about the product?

Collaboration with the data team has been good overall, but I’d like more tools to clearly label whether an item is related to a marketing analysis, a business model, or an executive report.

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

It resolves the disconnect between campaign data and product data, which used to be a real problem. Databricks has allowed me to integrate everything into a single model, and it has been, quite simply, excellent.

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 like the end to end complexity handling by databricks since it has mature that our gap between data engineering pipelines and machine learning workflows are bridged. The photon and auto scaling spark drastically simplified our large scale computing and also the required custom clusters via kubernetes. Collaborative notebook environment with supports multi language python sequel and scala, and allowed our data engineers to pair program and debug pipeline logic real time which has contributed to reduce total time investigating by 78.2% which is a great thing.

What do you dislike about the product?

There are no major dislikes I love the platform and the integrations it provides us with cloud and our data bases secondly they have great pricing range which is going perfectly with our company vision. Talking about improvements I feel that the cic integration using databrikcs bundles required steep learning which could be difficult that's all which I have as an improvement.

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

Databricks helps us to eliminate the unstructured data before using databricks we were using RAW elementary and unstructured training data sets lived in isolated S3 buckets with analytical reported by databricks we were able to unify these layers into single Lake house giving both BI and AI teams a single source of truth which was a great thing for us. Second it has given us AI readiness one hour production servers with deep rooted LLM learning models so these are things which which we have used databricks for.

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.

What do you like best about the product?

I use Databricks daily, and it’s integrated into everything—from data ingestion with Auto Loader from S3 and ADLS to transformations with Spark and Delta Live Tables. Now our whole team works on the same notebooks, jobs, and tables with version control. I really like the API and Terraform provider for managing clusters, jobs, and permissions with version-controlled code. Delta Live Tables also lets me create declarative pipelines with built-in expectations and data quality checks instead of programming validations manually. The initial setup was easy.

What do you dislike about the product?

I need better alerts and expense tracking broken down by job, team, and user.

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

Databricks replaced our separate environments, versions, and scripts; now my whole team works on the same notebooks, jobs, and tables, all with version control.

What do you like best about the product?

I bring campaign data from social platforms, client websites and online stores into one workspace, so I don’t have to rebuild the weekly attribution view across separate tools, and my figures match the account managers’ figures. Since we share a workspace, our web team usually catches broken tags before they distort a report. I value having analytics and machine learning together: I can write SQL, test a Python model and share the result with colleagues in the same notebook. Setup was simple, and our team was sharing notebooks soon after the first login. I also value the cluster settings, which let me size compute for a heavy year of ad impressions and keep the weekly attribution work manageable.

What do you dislike about the product?

Speed is the weak point for me. When I process a full year of ad impressions across several client accounts, queries slow down noticeably. I filter data early and right-size my clusters, which softens the problem, but I’d also like Databricks to suggest cluster sizes and flag slow queries automatically so I can fix slowdowns without trial and error.

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

I use Databricks to bring campaign data from social platforms, client websites and online stores into one workspace, so I no longer rebuild the weekly attribution view across separate tools, and my figures match the account managers’ figures.

What do you like best about the product?

I like that it lets me work with a large amount of information, prepare data, and build workflows. On top of that, it’s a flexible platform that adapts well to what I need.

What do you dislike about the product?

It would be excellent if some processes were made simpler, especially for users who don’t have much experience.

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

I use it for a variety of needs, primarily for data-related tasks such as designing and demonstrating data solutions, analytics, and artificial intelligence to address different business requirements.