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
628 reviews
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External reviews are not included in the AWS star rating for the product.
Data Asset version control.
What do you like best about the product?
Big data analysis typically need the version control for all data assets for simplicity , this tool have it. This is one common platform for all data assets. We can export data assets in multiple formats.
What do you dislike about the product?
Version interface should be more simpler for beginners and pricing is also an issue.
What problems is the product solving and how is that benefiting you?
Very efficient tool for sharing data with external partners and managing them.
Real time Data Power Tool.
What do you like best about the product?
Team collaboration feature is the liked feature in this tool which helps to collaborate with collogues, security and compliance are robust in Databricks. This support open Lakehouse architecture.for unified storage.
What do you dislike about the product?
The manual tunneling option are limited in this , not suitable for beginners.
What problems is the product solving and how is that benefiting you?
for dicovering assets using smart search we using this tool , it is very efficient.
Unified Analystics and AI platform
What do you like best about the product?
The processing ability to process data at scale is accurate , and can be integrated with many software ,Databricks is highly reliable and very easy to use, Easy integrated with BI tool also
What do you dislike about the product?
High cost for beginners , need low tier plan for beginners.
What problems is the product solving and how is that benefiting you?
we use this data analytics and data governance and Ai model development ,Databricks Data intelligence is extraordinary.
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.
Amazing Platform for Big Data Analysis and Data Management
What do you like best about the product?
One of the best Data management platform, I really like their ease to use integration and predefine templates that help a lot on data analytics without and efforts.
What do you dislike about the product?
I really like their services and their affordability and customer support always provide hassle free services.
What problems is the product solving and how is that benefiting you?
Mainly we are using Databricks only for Data analytics and data warehousing and both our work completing on time without any hassle and their user friendly infrastructure gives hassle free services that make our work more ease to use and best for our data management.
Highly scalable and developer friendly data solution.
What do you like best about the product?
I used the Databricks Data Intelligence Platform to build a real-time vehicle processing pipeline for a client in the logistics sector. The project involved collecting sensor data from hundreds of delivery trucks and processing it to detect anomalies and trigger alerts in real time. What I like best was how easily Databricks integrated with Azure Event Hubs because it allows me to stream data in and start processing instantly. I also used Delta Lake for storing clean data, which became the foundation of our analytic dashboard. Additionally, the Databricks rest API allowed us to trigger jobs from our monitoring systems, which enhances the automation to a great extend.
What do you dislike about the product?
Although the product is powerful, the learning curve for structured streaming was quite steep for new team members. I also encountered some integration limitations while sending alerts directly to external APIa from within notebooks. But we found workaround which involves using Azure Functions outside Databricks, which added some extra complexity.
What problems is the product solving and how is that benefiting you?
It has a major impact on our team’s productivity. Previously, our data engineers, cloud, architecture and analytic team worked on different tools which leads to delay and communication gaps. Now, with Databricks as the central platform, we collaborate in real time and debug issues together in shared notebooks.
Faster processing and scalability
What do you like best about the product?
It's a flexible solution that works well, it's designed to distribute data. It can easily scale to handle large amounts of data and offers numerous high-level features. I like the replication features.
What do you dislike about the product?
It could be better if there was a more optimized user interface, it can introduce relatively high latency for operations with small files.
What problems is the product solving and how is that benefiting you?
It helps process larger files in a shorter period of time and supports various resources manager and engines. This product is renowned for its reliability and meets the need to process large amounts of data.
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.
Cloud platform enables advanced collaboration but new SAP data feature could enhance its capabilities
What is our primary use case?
I am currently working as an IT architect. We have an AWS analytics platform, a cloud-based platform. We use Databricks for our AI/ML requirements and also the Databricks platform. For the past year, we have been using Databricks for our data scientist community to build their apps.
What is most valuable?
Databricks has a Unified Catalog that assists with secured access and governance. Additionally, serverless computing is crucial for our computing needs. Its collaboration features, such as data sharing capabilities, are also outstanding. Overall, the platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
What needs improvement?
I heard that a new feature is being developed for SAP that can bring SAP data directly into the platform for generating reports. This feature, if made publicly available, may act as a game-changer, considering many global organizations use SAP data for their ERP requirements.
For how long have I used the solution?
We have been using Databricks for one year.
What was my experience with deployment of the solution?
We did not experience deployment issues as we have an internal database AWS cloud admin team. We worked directly with Databricks personnel to configure our requirements.
What do I think about the stability of the solution?
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far. However, since we are still in the process of building much on the platform, we are still observing its stability.
What do I think about the scalability of the solution?
Databricks is an easily scalable platform. It leverages the cloud advantage directly, and scalability is one of the great features of the cloud platform.
How are customer service and support?
We have a customer portal where we can raise issues. As of now, we are raising issues and they are providing solutions without any problems.
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
Before Databricks, we were using the R Studio platform for our advanced analytics requirements. We switched to Databricks because it is an advanced platform compared to R Studio.
How was the initial setup?
The initial setup of Databricks could be complex. We set it up with AWS as the backend. It's crucial to have both cloud-specific knowledge and how to configure its features in the platform.
What about the implementation team?
We have our internal database AWS cloud admin team. We did not use external integrators or consultants.
What was our ROI?
We haven't yet seen an ROI from this solution, as we are still using and observing the platform.
What's my experience with pricing, setup cost, and licensing?
There are some issues with pricing specific to our accounts, and Databricks is investigating. It is not a cheap solution.
Which other solutions did I evaluate?
We previously used R Studio before switching to Databricks.
What other advice do I have?
I would recommend Databricks as an advanced analytics platform. It offers features to quickly pull data from various sources required for report generation. Overall, I rate this solution as seven or eight out of ten.
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?
Amazon Web Services (AWS)
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