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.
External reviews
627 reviews
from
and
External reviews are not included in the AWS star rating for the product.
Future of Data Intelligence for Businesses
What do you like best about the product?
The built in visual tools help us understand data more easily. Instead of just seeing a bunch of numbers, I can spot trends and patterns clearly, which makes it easier to make smart decisions.
What do you dislike about the product?
Because it’s a cloud based platform, everytime I need a stable internet connection to use it. Sometime I am working offline or in places with spotty internet.
What problems is the product solving and how is that benefiting you?
I am really happy with how Databricks has transformed our data processes. It’s made things faster, simpler and more efficient which allowing to focus on insights rather than struggling with infrastructure.
Good holistic platform starting big data all the way to latest AI
What do you like best about the product?
A platform that host wide varieties that can be used for for both Data Lake and Warehouse purpose
What do you dislike about the product?
Complexity in making things works, especially cluster management
What problems is the product solving and how is that benefiting you?
Started with the replacement of Hadoop & now many of Analytics practice offloaded to Databricks & Data Lake
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
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.
Best unified platform for AI and data analytics
What do you like best about the product?
It stands out as it seamlessly integrates data engineering, analytics and AI/ML workloads. Their Lakehouse architecture is a game changer as it combines the best of data lakes and warehouse, eliminating then need for complex ETL pipelines.
What do you dislike about the product?
Cluster startup is slow and time consuming. Price of products are very high if it is not used to its fully utilizing capabilities.
What problems is the product solving and how is that benefiting you?
We use for big data processing and real time analytics and AI driven insights. It has significantly improved data governance, performance and collaboration across teams. By replacing our legacy ETL workflow we have reduced processing time by half and improved model deployment efficiency.
The Tool for data analysis is Phenomenal
What do you like best about the product?
Offers interactive notebooks where different users can collaborate on data projects in real time.
What do you dislike about the product?
The disadvantage is that depending on the cluster, it takes a long time to pull the database.
What problems is the product solving and how is that benefiting you?
It helps to process large volumes of data, understand and optimize the problem, as through analysis we can have a more assertive decision-making process.
Boosting efficiency with the unified data and AI ecosystem
What do you like best about the product?
Databricks scalability features allowed us to process millions of rows of customer data efficiently. We use utilized Databricks to build a customer segmentation model for an e-commerce platform. Here we identified targeted groups by analyzing purchase history, browsing behavior and other related data. The huge amount of data can be visualized using in-built visualization tool in Databricks, which was incredibly helpful in exploring patterns and presenting insights.
What do you dislike about the product?
I personally reached out to support when debugging data pipeline issues. But the issue got resolved in no time, preventing delays in the project.
What problems is the product solving and how is that benefiting you?
The platform has helped us to automate data aggregation and pre processing, which results in cutting down the time spent on repetitive task by nearly 30%. Also, it's support for real time data updates insured that the result were always based on the most current data.
Ideal for large-scale data processing and collaboration
What do you like best about the product?
I like the way Databricks does data management, all in one place. What it does is it unites the data engineers and data scientists on the same platform to collaborate and solve problems quickly. Scaling became effortless thanks to the integration with tools like AWS, as well as keeping up with the progress in the notes continues to keep us all on the same page in the notes. It’s helped remove communication issues and it’s helped take care of things faster.
What do you dislike about the product?
Databricks has one downside and that is the learning curve, especially for people that want to get started with a more complex configuration. We spent some time troubleshooting the setup, and it’s not the easiest one to begin with. The pricing model is also a little unclear, so it isn’t as easy to predict cost as your usage gets bigger. At times that has led to some unforeseen expenses that we might have cut if we had better cost visibility.
What problems is the product solving and how is that benefiting you?
We’ve seen Databricks drastically improve our efficiency. Now we can manage large datasets, run machine learning models, and work in real time but without worrying about working with different tools. It’s allowed us to simplify our data pipeline and speed up decision making, which has been a big win for the team. At the same time, this has allowed faster product development cycles all the way to shorter time to market for new features.
From raw data to actionable insights in record time
What do you like best about the product?
Databricks has the great ability to handle streaming data and integrate with Kafka. This is an essential feature for our organisation as we used Databricks to enhance our real time fraud detection system in the financial service sector. This has improved security and reduced fraud activities. The real time processing capabilities were also a crucial feature for our use case. Databricks also support multiple languages development, which is a key benefit for our organisation as we have both Python and Scala developers.
What do you dislike about the product?
During a critical phase of the project, we faced few challenges while optimising our Spark jobs. The user interface for cluster management could be improved, as we occasionally face delays when scaling clusters to handle large workloads.
What problems is the product solving and how is that benefiting you?
Our logistic team used Databricks to optimise delivery routes by analysing traffic patterns, fuel consumption and delivery time. By optimising all these things we almost reduced delivery time by 20% and saved significant cost.
Simplify big data challenges for better decision-making
What do you like best about the product?
Recommendation engine for an e-commerce platform was developed by our team with the help of DataBricks. The project involved analysing customer behaviour to suggest products on the website. For this project we are required to process bulk data without any performance issues. That could only be possible with DataBricks as the platform is scalable. We also integrated DataBricks with AWS S3 to access data on cloud.
What do you dislike about the product?
Initially, we faced some challenges as the platform has a learning curve, but when we encountered any challenges, we connect with their customer support team and they provided a detailed guidance on every issues that we had.
What problems is the product solving and how is that benefiting you?
We have multiple sources of data and Databricksh has greatly improved our efficiency by combining all the sources of data into single platform. This has eliminated the need to switch between different tools and saving us hours of work each time.
showing 21 - 30