Databricks Data Intelligence Platform
Databricks, Inc.External reviews
768 reviews
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Robust Data Processing with High Costs and Learning Curve
What do you like best about the product?
What I like the most about Databricks is the performance. When handling a large amount of datasets, it's time-consuming and requires a lot of effort to clean and use the data, but Databricks allows me to do all of that on a single platform. I appreciate that I can have all the tools for processing, storage, and analysis in one place. The notebook feature is also very good, providing a nice experience for writing code, creating pipelines, and more.
What do you dislike about the product?
Things I want to be improved in Databricks is the cost. Like, it's really expensive to be used when we are small scale businesses. I would like the cost to be decreased. There's also a lot of configuration and setup, which makes it hard for junior level users to just start and work with it. It needs more learning modules to help start with the platform. Initial setup wasn't easy, needing certification on data exposure and it was tough because engineers couldn't reset the database properly.
What problems is the product solving and how is that benefiting you?
I use Databricks for large-scale data and analytics, making it easier to handle and process large datasets. It helps analyze data faster, enables efficient data cleaning and transformation, and provides all tools in a single platform for processing, storage, and analysis.
Fast, Reliable Lakehouse That Unifies Data Processing and SQL
What do you like best about the product?
Data processing and SQL queries are fast and reliable, and the lakehouse platform really helps unify our work.
What do you dislike about the product?
There’s a steep learning curve that demands advanced coding skills and solid Spark expertise, which can make it feel like overkill for teams that only need straightforward SQL reporting.
What problems is the product solving and how is that benefiting you?
We’ve improved our data team’s productivity by removing the need for manual infrastructure management.
Unified Analytics Powerhouse with Minor Hiccups
What do you like best about the product?
I like most about Databricks is that it brings data engineering, analytics, and AI workflow into one shared platform, which makes collaboration much easier. It's valuable for working with a large dataset and notebooks, and it helps set up suitable pipelines without the hassle of managing too many separate tools.
What do you dislike about the product?
Cost visibility and resource users can be hard to track, especially as more teams cluster and jobs start using the platform. I also like to sync up permission management. Clear troubleshooting for a job failure and a smoother experience around the workspace governance and configuration. CDC lake flow is always stuck for a last table and not giving a clear picture till now. Serverless logs are sometimes very difficult to track, making it hard to understand the reason for job failures.
What problems is the product solving and how is that benefiting you?
I find Databricks solves handling large-scale data processing and analytics by unifying data engineering, analytics, and AI workflow into one platform. It simplifies collaboration on notebooks and automation workflows, enabling faster work with big datasets using Spark.
Seamless, Scalable Unified Platform for Data, Analytics, and ML
What do you like best about the product?
Its unified platform for data engineering, analytics, and machine learning makes workflows seamless and scalable.
What do you dislike about the product?
It can be expensive and has a bit of a learning curve for new users.
What problems is the product solving and how is that benefiting you?
It simplifies big data processing and analytics in one platform, helping me save time and scale workflows efficiently.
All-In-One Platform That Revolutionizes Workflows
What do you like best about the product?
I love that Databricks combines data engineering, SQL analytics, and machine learning in a collaborative platform. My team and I can clean data, run SQL, and build models all in one place without losing our tasks.
What do you dislike about the product?
The price can get scary fast, and sometimes the cluster startup delay makes me feel like I am waiting for a coffee machine that's still warming up.
What problems is the product solving and how is that benefiting you?
I use Databricks to run SQL, clean data, and build models all in one place, eliminating the need for multiple tools while handling huge data efficiently.
All-in-One Collaborative Platform for Data, ML, and Analytics
What do you like best about the product?
Databricks brings together data engineering, data science, machine learning, and analytics in a single collaborative environment. You don't need to juggle multiple tools for different tasks.
What do you dislike about the product?
Databricks pricing is based on Databricks Units (DBUs), which can be expensive and difficult to estimate upfront. Costs can quickly spiral out of control, especially with auto-scaling clusters.
What problems is the product solving and how is that benefiting you?
Creating a unified lakehouse platform that combines data lakes, data warehouses, and machine learning in one place.
BI and Data Engineering in One Place, with AI-Assistant
What do you like best about the product?
Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems.
Using AI to improve and make faster the SQL writing and execution
Using AI to improve and make faster the SQL writing and execution
What do you dislike about the product?
Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so.
What problems is the product solving and how is that benefiting you?
Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data.
Unified Platform with Powerful Features, Needs Faster Cluster Startups
What do you like best about the product?
I appreciate how Databricks brought everything onto one unified platform, allowing our teams to collaborate in shared notebooks and ensuring data consistency with Delta Lake's ACID transactions. My favorite feature is Auto Loader, which automatically ingests new data files as they land in cloud storage, saving our team 2-3 hours a week on manual pipeline monitoring. Unity Catalog has been a game changer for us, providing a central place for governance and access control, which before was a mess. The initial setup was straightforward, and we had our first cluster and notebooks connected to S3 within a day, which was impressive given the platform's power. The workspace configuration and cloud integration guides are solid to follow.
What do you dislike about the product?
The cluster startup time is something that still catches us off guard. Cold start can take anywhere from 3-5 minutes, which gets frustrating when you are in the middle of an iterative debugging session and just need to test a quick fix. The cost management also needs some upgrades as currently the billing dashboards are improving but it still takes some digging to pinpoint exactly which job or user is driving up spend.
What problems is the product solving and how is that benefiting you?
I use Databricks to unify our data processing and machine learning, reducing pipeline delivery delays by 40%. It enables team collaboration with consistent data, saving hours with the autoloader, and simplifies governance with Unity Catalog.
Streamlined Data Processing with Unmatched Speed
What do you like best about the product?
I use Databricks for real-time data ingestion and processing as well as batch processing. I find it easy to use with PySpark, and I appreciate that it serves as a single platform for both real-time and batch processing. The in-memory processing drastically reduces processing time, and working with dataframes makes handling structured data straightforward. I like the fast execution and the ability to clean, massage, and manipulate data all on the same platform. It's also easy to deploy, and I enjoy the smooth CI pipeline with just one click. The initial setup was quite easy, and the product support made it a cakewalk.
What do you dislike about the product?
Databricks should come up with agentic framework integrated, making it a single stop for Data and AI.
What problems is the product solving and how is that benefiting you?
Databricks offers an easy-to-use platform for both realtime and batch processing. It integrates easily with PySpark and supports in-memory processing, significantly reducing processing time. Dataframes make handling structured data simpler.
Great UI and a Straightforward, Linear Learning Curve.
What do you like best about the product?
The UI is great compared to other providers. It’s easy to work with, and the learning curve feels linear and straightforward.
What do you dislike about the product?
Consumption-based costs are on the higher side, and it can be difficult for users who aren’t proficient in Python or Spark.
What problems is the product solving and how is that benefiting you?
A centralised data warehouse, with notebooks running on top of it for further analysis and ML use cases.
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