We work on three platforms. Databricks is hosted on Azure for us, so we work with ADFS, Azure Data Factory, and also the AWS Cloud. We work for some customers.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
637 reviews
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External reviews are not included in the AWS star rating for the product.
A trusted partner for scalable and smart data workflows.
What do you like best about the product?
A productive maintenance solution for manufacturing equipment was implemented by my team using Databricks. We identified patterns that indicated potential failures by analysing IoT sensors data. What I like best about Databricks is the integration with IoT hubs, which allowed us to collect data from our devices. It also offers pre-build ML libraries which help us during the model development phase and saved us valuable time.
What do you dislike about the product?
Some optimal configurations needs to be added when integrating IoT data streams with Databricks, which was a bit complex. That's why we faced some challenges during this phase of project.
What problems is the product solving and how is that benefiting you?
Our team used Databricks to optimise inventory management by analysing sales pattern, supply, Chinita and seasonal trends. This automated pipeline/workflow, reduced errors and insured timely analysis. The platform also improved team collaboration, as multiple departments could view and edit the data directly within the Databricks environment.
Shared notebooks and scheduling enhance cost efficiency
What is our primary use case?
What is most valuable?
The notebooks and the ability to share them with collaborators are valuable, as multiple developers can use a single cluster. This reduces costs. The scheduling part is managed by Databricks itself, for example, when it is idle, it will automatically turn off. All these features are handled by Databricks, reducing costs. We do not need to schedule separately.
For example, on AWS EC2, we have to create a Lambda function or use System Manager templates to schedule EC2 and EMRs. Here, it is taken care of, saving significant resources.
Additionally, notebooks can be shared within the development team which saves effort. Developers can share their notebooks. Git and Azure DevOps integration on the Databricks side is also very helpful.
What needs improvement?
The API deployment and model deployment are not easy on the Databricks side. We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools. Moreover, the API deployment should be simplified for ease of deployment and consumption.
For how long have I used the solution?
I have been using Databricks for approximately two and a half to three years.
What do I think about the scalability of the solution?
We have not faced any shortages so far. The clusters are available on demand, thus we have not encountered any scalability issues.
How are customer service and support?
We mostly had limited data support required from Databricks. Whenever we did need support, within two or three days the problem was solved. I would rate them ten out of ten.
How would you rate customer service and support?
Positive
What about the implementation team?
We bought it as a service, which is why we never implemented it ourselves. We do not have any implementation team.
Which other solutions did I evaluate?
For companies focused solely on data transformation, transferring data between databases, and not tackling machine learning or deep learning problems, I recommend ADF. It would be sufficient and cost-saving compared to a full-fledged solution like Databricks. However, for data analytics and solving ETL problems, one should consider Databricks.
What other advice do I have?
I would rate it nine out of ten.
God Plataform
What do you like best about the product?
Ease of use, Number of Features and Customer Support
What do you dislike about the product?
current moment I have no negative points
What problems is the product solving and how is that benefiting you?
ETL resolution and database query at the first moment, doing AI tests
Multifunctional Platform for Data Analytics
What do you like best about the product?
Databricks is a complete platform wherethere users can enjoy a big pool of tools regarding data universe.
What do you dislike about the product?
I have no bad point about the platform...
What problems is the product solving and how is that benefiting you?
Find data and informations in a easy way.
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.
A Comprehensive Review of the Databricks Data Intelligence Platform
What do you like best about the product?
It excels with its unified platform for data engineering, science, and machine learning, fostering collaboration and scalability.
What do you dislike about the product?
It can be complex for new users, requiring a steep learning curve to fully utilize its capabilities.
What problems is the product solving and how is that benefiting you?
Databricks streamlines data workflows by unifying data engineering, science, and ML, enhancing team collaboration and speeding up data processing. This reduces complexity and boosts scalability for big data and AI projects.
A versatile data intelligence platform.
What do you like best about the product?
I liked the MLflow integration with Databricks, as it was a crucial part of churn prediction model for our subscription based service that our team developed. The model analysed customer behaviour data to identify potential risks and suggest strategies against that. Also, the job scheduling feature of DataBricks has automated our data preprocessing tasks, which saved us significant amount of time and efforts.
What do you dislike about the product?
We had trouble while setting up real time data ingestion pipelines. But the issue was resolved within a day because of the quick and detailed guidance by DataBricks customer support team.
What problems is the product solving and how is that benefiting you?
Our customer support team needed a dashboard to monitor tickets resolutions time and customer satisfaction score. Using DataBricks, we build a pipeline that pull data from multiple CRM tools. This has improved our productivity as the data collection and report generation is now automated
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.
Best platform for data engineering and data science
What do you like best about the product?
We used Databricks for its features such asreal time data processing and dat exploration tools for visualizing data.AutoML and Mlflow is one of the best AI integration in this platform.This software is cost efficient
What do you dislike about the product?
Limited tutorials for new users , not beginner freindly interface
What problems is the product solving and how is that benefiting you?
We used this platform analyzing and processing big data and process data from various formats, this tool is really great
Databricks - best integration tool
What do you like best about the product?
Databricks data intelligence platform make integration of data engineering, data science, and machine learning into a single environment simplify workflow. Users can easily share data and models in same platform.
Databricks optimize for cloud environment, this flexibility allows organisation to choose their preferred cloud provider.
Databricks has a large and active user community and ecosystem include a wealth of share knowledge resources and third party integration.
Databricks optimize for cloud environment, this flexibility allows organisation to choose their preferred cloud provider.
Databricks has a large and active user community and ecosystem include a wealth of share knowledge resources and third party integration.
What do you dislike about the product?
I have been using this software from while but didn't find any dislike in it.
What problems is the product solving and how is that benefiting you?
Databricks support integration with wide range of data source, they allow users id easily ingest, process,and analysis data from disparate system.
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