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
External reviews are not included in the AWS star rating for the product.
Best review
Anyone can access these platform which is the main point
A well thought out evolving data platform
Best Online data management platform for AIML
Best ETL tool for Data Engineers
Lead Developer
services are easy to implement at scale
High speed Data Management tool
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.
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.
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.