Databricks Data Intelligence Platform
Databricks, Inc.External reviews
797 reviews
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Intuitive UI and AI-Powered Experience That Keeps Getting Better
What do you like best about the product?
The UI is pretty intuitive and they are using ai to make the experience even better
What do you dislike about the product?
For the most part, it’s a great platform, but some of the debugging options could be improved.
What problems is the product solving and how is that benefiting you?
I use it to write queries for extracting data and running experiments, mostly with SQL and Python.
Scalable, All-in-One Environment with Some Learning Curve
What do you like best about the product?
I like Databricks for its scalability and all-in-one environment for data engineering, analytics, and machine learning. It allows me to process large datasets efficiently while keeping workflows organized in one platform. The scalability is very valuable because it lets me handle growing data volumes and complex workloads without performance issues. As projects expand, the platform can scale resources efficiently.
What do you dislike about the product?
Some features can have a learning curve, especially for new users working with advanced configurations or cluster management. The interface could also be more intuitive in certain areas. The setup was relatively smooth for core features, but some advanced settings like cluster optimization, permissions, and integrations required more time and technical knowledge.
What problems is the product solving and how is that benefiting you?
Databricks solves major data management and analytics challenges by efficiently handling large datasets, simplifying ETL processes, and centralizing workflows. Its scalability allows me to manage growing data volumes without performance issues, ensuring resources scale efficiently as projects expand.
Unifies Data Engineering, ML & Analytics with Ease
What do you like best about the product?
I like how Databricks brings data engineering, analytics, and machine learning into a simple unified platform. I appreciate the faster end-to-end data flow, the single source of truth it provides, and the better collaboration it enables. I also found the initial setup to be quite simple.
What do you dislike about the product?
Cost management is a concern for me. Being a scalable and compute-heavy platform, costs can increase quickly.
What problems is the product solving and how is that benefiting you?
I use Databricks for data processing and engineering, handling large data volumes and eliminating data silos. It unifies data engineering, analytics, and machine learning for faster data flow, providing a single source of truth and improving collaboration.
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.
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