I have recently gotten into Databricks and trained on one model. I started using Databricks because of its hardware support and all the other things that it provides, and it is easier to get into. Earlier, when I had to test some part of my code or test if it was working or not, it was not just a fair, not a full production run, but just a fair testing; I had to get a machine, raise a request, get into the whole process. With Databricks, I can just simply create one myself. I could get the resources, whatever they are required, test it out all there, and then go ahead with that, and that is why I have been using it primarily.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
640 reviews
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External reviews are not included in the AWS star rating for the product.
High speed Data Management tool
What do you like best about the product?
Databrics givs a very high speed and effective experienceto manage the huge data without any complexcity. They have number of features and ease to use interface that make them very reliable and user friendly.
What do you dislike about the product?
We use databrics frequently in our company for data mangement.Their ease to use interface gives hassle free solution and great customer suport. We can very easily implement their templates and custom dataset to integrate our project and work on them.
What problems is the product solving and how is that benefiting you?
It is the best ETL tool and their pipelines make the data very friendly and ease to manage. They have multiple features like data warehouse, big data analysis and MLOps platforms etc. Their echo friendly system make them more useful and user friendly data management solution for us.
Provides resources to users quickly without much hassle
What is our primary use case?
What is most valuable?
The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle.
What needs improvement?
I think setting up the whole account for one person and giving access are areas that can be difficult to manage and should be made a little easier.
For how long have I used the solution?
I have experience with Databricks.
What do I think about the stability of the solution?
I think there's a duration after which our training without any activity would expire, which I think is a fair point, and that is the only place where I think this will stop. I haven't come across a lot of problems with Databricks.
What do I think about the scalability of the solution?
The tool is not used as frequently as PyTorch. I don't know why I am comparing Databricks to PyTorch, but I think around five people use it.
How are customer service and support?
I have not contacted the solution's technical support team.
Which solution did I use previously and why did I switch?
Before Databricks, I used to use a cloud support platform.
How was the initial setup?
The solution is deployed on the cloud.
Which other solutions did I evaluate?
I chose Databricks over other products, considering the hardware support it offers.
What other advice do I have?
A little bit of time will be needed to get comfortable with Databricks.
I rate the tool an eight out of ten.
Leveraged the Databricks Data Intelligence Platform for streamline data processing and analytics.
What do you like best about the product?
What I like best about Databricks Data Intelligence Platform:
The seamless integration of big data and machine learning workflows, enabling efficient data processing, collaborative development, and scalable analytics in a unified environment
There are numerous upsides but I will go with my top THREE:
1. Scalability: Automatically scales resources to handle large data volumes efficiently.
2. Collaborative Environment: Supports collaborative development with shared notebooks and real-time co-authoring.
3. Unified Analytics: Combines data engineering, data science, and machine learning in a single platform.
The seamless integration of big data and machine learning workflows, enabling efficient data processing, collaborative development, and scalable analytics in a unified environment
There are numerous upsides but I will go with my top THREE:
1. Scalability: Automatically scales resources to handle large data volumes efficiently.
2. Collaborative Environment: Supports collaborative development with shared notebooks and real-time co-authoring.
3. Unified Analytics: Combines data engineering, data science, and machine learning in a single platform.
What do you dislike about the product?
I can only suggest a more robust and technical customer support.
What problems is the product solving and how is that benefiting you?
Several problems are solved but I will limit my response to three:
1. Complex Data Processing: It simplifies the handling of large and complex datasets by leveraging Apache Spark, enabling faster data processing and analysis.
2. Unified Environment: It provides a unified platform for data engineers, data scientists, and analysts to collaborate seamlessly on data projects, reducing silos and improving productivity.
3. Scalability: The platform automatically scales resources based on workload demands, ensuring efficient resource utilization and performance even with varying data volumes.
1. Complex Data Processing: It simplifies the handling of large and complex datasets by leveraging Apache Spark, enabling faster data processing and analysis.
2. Unified Environment: It provides a unified platform for data engineers, data scientists, and analysts to collaborate seamlessly on data projects, reducing silos and improving productivity.
3. Scalability: The platform automatically scales resources based on workload demands, ensuring efficient resource utilization and performance even with varying data volumes.
Provides seamless integration capabilities, but the cluster management features need improvement
What is our primary use case?
We use the product as a data science platform that enables me to handle and analyze large datasets efficiently.
What is most valuable?
Databricks can switch easily between cloud providers, such as Azure and GCP. It allows seamless integration with various data platforms and cloud providers, facilitating better data handling and analysis.
What needs improvement?
The product could be improved regarding the delay when switching to higher-performing virtual machines compared to other platforms like Snowflake. The ease and speed of managing clusters can also be enhanced, especially when scaling up resources. They could add more advanced data storage solutions like Iceberg and Delta files.
For how long have I used the solution?
I have been using Databricks for approximately two years.
What do I think about the stability of the solution?
I rate the product stability a seven out of ten.
What do I think about the scalability of the solution?
I rate the product scalability an eight.
How are customer service and support?
The technical support services are good.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup was straightforward. However, configuring policies could have been simpler.
What's my experience with pricing, setup cost, and licensing?
The product pricing is moderate.
Which other solutions did I evaluate?
I evaluated other options, including Snowflake, before choosing Databricks.
What other advice do I have?
Databricks is a robust solution for big data processing, offering flexibility and powerful features. While there are areas for improvement, especially in performance and cluster management, it remains a highly valuable tool in my data science toolkit.
I rate it a seven.
Databricks platform
What do you like best about the product?
Databricks is a platform where we can implement our spark and ml logics for all our data engineering, analysis and ml workloads with ease
What do you dislike about the product?
There's nothing such but we can have more pipeline kind of things added
What problems is the product solving and how is that benefiting you?
We get everything under a single platform we can say
it has made the integration with different sources hassel free and we can more focus on data
What do you like best about the product?
ease of use ,ease of integration,variert of features which are added often to improve user experiences,Assistant AI
What do you dislike about the product?
sometimes the error is not accurate hence debugging takes time,
ai assistant also provide incorrect suggestion few times
ai assistant also provide incorrect suggestion few times
What problems is the product solving and how is that benefiting you?
Databricks is solving the problem of processing huge data through parallel processing,also with help of unity catalog we can maintain the governance and integrity of data,as the features are easy to use we can focus on data and decisions.
The new era of Databricks Data Intelligence Platform for all the AI solutions
What do you like best about the product?
Databricks has come up with innovative idea to streamline the idea of creating new solutions that helps the customer leverage their business with their vast services they provide whether it's a retail or fintech, they provide with upbringing and transforming with the public sectors. It has an ease of implementation and integration.
What do you dislike about the product?
There are no dislikes about the Data Intelligence Platform.
What problems is the product solving and how is that benefiting you?
Databricks is revolutionizing the modern industry. As an academia I mostly work to research and build products with Databricks Data Intelligence Platform.
Complete platform but a bit confuse
What do you like best about the product?
The ease of use and the number of tools available. Without much knowledge, it is very easy to start generating value with little understanding of the tool.
What do you dislike about the product?
The usability its a bit confuse. The are a lot of amazing tools but, maybe, the best practices it's a bit confuse to deploy, however, there are a lot of knowledge outside (provided by themshelves or google)
What problems is the product solving and how is that benefiting you?
Data Intelligence Plataform is solving usability of new features and implementation.
Process large-scale data sets and integrates with Apache Spark with notebook environment
What is our primary use case?
I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB.
What is most valuable?
Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.
What needs improvement?
While Databricks allows you to upload your packages, we encountered some limitations with its capabilities, particularly with Apache Spark, which also affected Databricks. We had issues working with spatial data. You had to go through many steps to find libraries that could process spatial data in a distributed fashion.
For how long have I used the solution?
I have been using Databricks since 2018.
What do I think about the scalability of the solution?
I might have a project that runs for one or two months, and perhaps I won't use it for six months. Self-service is one of its strengths. I can shut down everything and easily spin up resources when I need to use them again. We have a dedicated group of fifty people who consistently use Databricks for analytics.
How was the initial setup?
The initial setup was very easy and took around 10-15 people. We have a data science infrastructure team helping with this.
What was our ROI?
Databricks stands out among most data platforms mainly because of its ease of use. The learning curve is not as steep, making it accessible for anyone to handle large-scale data processing on Databricks. This ease of use contributes positively to our return on investment. However, in our line of work, converting this efficiency into direct monetary gains can be challenging, given our nonprofit nature.
What's my experience with pricing, setup cost, and licensing?
We purchased high-performance laptops to reduce our reliance on the cloud. The main issue was the cost. Internally, if I used Databricks, that cost would return to my team. There was a time when my monthly cost was around ten thousand dollars, which was quite high. Due to these costs, several teams, including ours, move away from using Databricks and other cloud providers. It became prohibitive, so we invested in our high-performance computers internally instead.
What other advice do I have?
Databricks provides ease of use for me, particularly due to its seamless integration with Apache Spark. This integration simplifies the process of conducting machine learning on large-scale datasets.
I recommend this solution 100%. Overall, I rate the solution an eight out of ten.
DataBricks Data Intelligence Platform Review
What do you like best about the product?
easy to use
community to help as well
best for begineer as well.
community to help as well
best for begineer as well.
What do you dislike about the product?
none i like everythinng about the databricks
What problems is the product solving and how is that benefiting you?
Yes , it help me in big data analytics and data goverence
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