A typical use case for the solution is to build the data lakehouse for the client because they have a variety of source systems, and they want to unify that data into the lakehouse platform, where they want to use the data for analytical purposes and insights.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
638 reviews
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Databricks Simplifies Data Processing and AI Integration
What do you like best about the product?
Lakehouse architecture combines the best of data lakes and warehouses, offering ease of use, seamless integration and eliminating the need for complex data pipelines.
What do you dislike about the product?
Databricks is powerful but can be difficult for beginners, especially those unfamiliar with big data tools or SQL.
What problems is the product solving and how is that benefiting you?
Big data processing has become much more efficient. We now handle large volumes of data in real time, which allows us to generate AI-driven insights faster and make smarter business decisions.
Unifying data for analytical insights with smooth AI and machine learning integration
What is our primary use case?
What is most valuable?
The most valuable features of Databricks are especially the Delta Lake and the Unity Catalog; those are the main features. The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse. Currently, they're coming up with workflow jobs, along with other supporting elements to create an end-to-end solution.
What needs improvement?
In my opinion, areas of Databricks that have room for improvement involve the dashboards. Until recently, everyone used third-party systems such as Power BI to connect to Databricks for dashboards and reports, but they're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
For how long have I used the solution?
I have approximately four years of experience working with Databricks.
What do I think about the stability of the solution?
I would rate the stability of Databricks as highly stable, around nine out of ten.
What do I think about the scalability of the solution?
I would rate the scalability of this solution as very high, about nine out of ten.
How are customer service and support?
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features. For us, it's so far so good with no problems, and I would rate the support quality as eight out of ten.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup of the Databricks solution is reasonably fair enough. It doesn't give any trouble to implement the solution, and I think it's fairly easy to set up and work on Databricks.
What was our ROI?
I can't say if there's seen an ROI from the solution because I do not have exposure in that area, although I think the people who decided to implement Databricks might have done all this analysis and POCs.
What other advice do I have?
My relationship with the vendor is that I'm not a partner of Databricks; I work for a client where we use the Databricks software for implementing the solutions.
My clients are usually enterprise-level organizations, but the area where they're implementing is medium level here, although it might go into enterprise level in the future.
Regarding the price of Databricks, I don't involve myself in those decisions.
I think Databricks is very good at facilitating AI and machine learning projects; they implement AI and machine learning models very well, and clients can run their models on Databricks. I believe they are in a better place compared to competitors such as Snowflake, and they are tying up with important companies such as SAP and Palantir.
Based on my experience, I would recommend Databricks to other people. Overall, I would rate this solution as one of the best, about eight out of ten, although I might not know some of the pitfalls; it's based on use case to use case, but for us, it's working well.
Databricks make enterprise scale ML project easier to manage.
What do you like best about the product?
We have build a fraud detection system for a European finntech product using Databricks Data Intelligence Platform. The project required ingesting large volume of transaction data, cleaning it and training multiple machine, learning models using historical fraud patterns. Feature like tight integration with ML flow, alone helped us avoid the usual mess of managing models across Juypter notebooks and cloud storage. It’s collaborative environment allowed our ML engineers and data scientists to work together in Databricks notebooks in the same interface. Additionally, the ability to schedule retraining jobs made it easier to put a model into production with minimum effort.
What do you dislike about the product?
While MLflow is great, the UI for comparing runs can feel a bit outdated and lacks advanced filtering options. Managing features stores also felt slightly inefficient without more granular access control for different user roles.
What problems is the product solving and how is that benefiting you?
Our ML pipeline is far more stable and efficient after we implemented Databricks. We have a standardised our development workflows and now our engineers, analyst and business teams can access the same datasets and results in a single environment. This has dramatically improved our team collaboration.
Great advanced analytical tool that utilises Spark to fullest
What do you like best about the product?
Its ability to combine big data processing with machine learning makes it possible to do advanced analytics and data engineering efficiently in one space. Its scalable design and collaborative workspace also make it simple for teams to work together and process large datasets without slowing down the system
What do you dislike about the product?
One downside of the Databricks Data Intelligence Platform is the steep learning curve for new users, especially when navigating complex features like Delta Lake and managing large-scale workloads
What problems is the product solving and how is that benefiting you?
It provides a unified environment for analytics, machine learning, and data engineering, addressing issues like managing massive datasets, scaling machine learning models and enables team collaboration. While collaborative notebooks improve teamwork, increasing productivity and speeding the implementation of data-driven solutions, its interaction with Apache Spark and Delta Lake guarantees effective data processing, consistency, and version control.
Why Databricks Data Intelligence Platform?
What do you like best about the product?
Databricks simplifies big data processing with AI-powered analytics, seamless integration, and collaborative workspaces, making data-driven decisions faster and more efficient. Implementation is smooth, and customer support is helpful.
What do you dislike about the product?
Databricks is great, but the cost can escalate quickly, especially with high workloads and auto-scaling.
What problems is the product solving and how is that benefiting you?
Databricks makes working with data easier by combining analytics, AI, and storage in one place. It helps teams work faster, automate tasks, and get insights quickly—saving time and effort.
Easy to use, easy to access support system, unified lakehouse architecture and timely new features.
What do you like best about the product?
Flexibility of using languages like Python, Pyspark and SQL. New file arriving feature. variety of options to connect with almost all kind of source. Very simple implementation of unity catalog which was hard to manage initially. Volumn that works seamless with python/pandas code.
What do you dislike about the product?
Databricks diagnose error suggestion. Failed to provide queries for like mail attachment extraction, AES 256 encrypt code etc. Very good for support in python, pyspark and SQL code but not in rare usecase like mentioned above.
What problems is the product solving and how is that benefiting you?
Complex logic and Expensive processing, Unified batch and stream processing, data governance by unity catalog, use of separate BI tool etc.
Databricks Analytics Amazing Tool
What do you like best about the product?
It provide codes based on taking context from previous cells, which helps in developing code better.
Serverless querying is faster and cheaper, which helps build and query effectively.
Serverless querying is faster and cheaper, which helps build and query effectively.
What do you dislike about the product?
Nothing disappointing about this product.
What problems is the product solving and how is that benefiting you?
Helps ingest data from various sources and structure data properly. Unity catalogue also helps in data governance as similar data under a single catalogue.
It helps create jobs and utilities based on custom requirement.
It helps create jobs and utilities based on custom requirement.
Unified Data Analytics Platform
What do you like best about the product?
There are so many features which I loved and some of them are unity catalog (data governance), Serverless compute (sql warehouse) and delta acid transactions (time travel in case of accidental data delete or update)
What do you dislike about the product?
there is nothing to say but in previously when databricks changed the UI design then little bit problem occured but now I am used to
What problems is the product solving and how is that benefiting you?
Its real streaming helps lot as we receive the data in near to real time and its provides all the supporting features apart from this easy to make different kind of ETL and data integration from various source
Databricks is a great solution for data engineering and analytics
What do you like best about the product?
I love Databricks because it consolidates data engineering, machine learning and analytics into one and the feat of using collaborative notebooks also enables real-time and seamless teamwork in working between a data engineer working on data pipelines and a data scientist running various experiments. And it handles large-scale data quite easily and running complex SQL queries within seconds with no infrastructure related issues. Also it has some built-in governance tools like unity catalog which help me in managing data lineage and controlling access.
What do you dislike about the product?
Databricks can be quite overwhelming for beginners especially if they do not have a good grasp of sql and spark to begin with. And the pace of their updates, although is quite good, it tends to introduce breaking changes which can be a pain to keep up with. Also the pricing can get expensive at scale especially for teams that work with really big sets of data.
What problems is the product solving and how is that benefiting you?
Before Databricks, we had disjointed processes, terribly slow data processing and no easy way to collaborate but now, we can integrate multiple data sources more easily and process big data in an efficient manner. Also this rapid development of machine learning models makes the infrastructure manageable for us and we save hundreds of hours with automation – not just in maintaining infrastructure but in thinking innovation first.
A Game-Changer for Data and AI Teams
What do you like best about the product?
the most helpful feature is unity catalog (data governance) and sql warehouse
What do you dislike about the product?
now a days nothing, it is already too enhanced
What problems is the product solving and how is that benefiting you?
Data Integration from various sources like mongoDB, Postgres, ADLS, SFTP, SQL server, big query and etc as we get the data from mentioned sources and need to store in multiple data layer (raw data, flatten date, aggregated data) according to business, so its help in segregation based on catalog according to line of business use case.
And ETL (databricks workflow) provides lots of option for scheduling jobs in multiple way as per requirement.
And ETL (databricks workflow) provides lots of option for scheduling jobs in multiple way as per requirement.
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