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
637 reviews
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External reviews are not included in the AWS star rating for the product.
services are easy to implement at scale
What do you like best about the product?
I like the Delta lake model, cool features are incremental data updates, change data capture, and streaming data can be done under the hood.
What do you dislike about the product?
sharing the data out of azure has some limitations.
What problems is the product solving and how is that benefiting you?
building data lake using delta lake. Storing wide variery of data and applying data cleaniess, and performing aggregations as needed.
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.
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.
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
Excelent platform
What do you like best about the product?
The best part about databricks is how easy is to start working. There is no need for setup and I can use either SQL, Python or Pyspark in the same notebook. It makes the work easier and faster.
What do you dislike about the product?
For data engineering studying purposes is rather expensive.
What problems is the product solving and how is that benefiting you?
Analysis for my team.
Helps users with data processing and analytics
What is our primary use case?
I use Databricks to manage the setting up of data lakes for SaaS.
What needs improvement?
The biggest problem associated with the product is that it is quite pricey. We cannot find a better solution than Databricks in the market currently.
For how long have I used the solution?
I have been using Databricks for a year.
What's my experience with pricing, setup cost, and licensing?
It is an expensive tool. The licensing model is a pay-as-you-go one.
What other advice do I have?
The tool helps with data processing and analytics with large-scale data or big data since it is associated with managing data at a large scale.
For my general use cases, I would say that I am not a technical person, so I cannot explain to you how the tool helps with the area of data engineering tasks.
There is another team in my company that is involved in the use of machine learning and AI features in Databricks. My team is mostly into operations. The tool is used in a multi-country project.
For example, in my company, they make some shopping decisions related to solutions based on what is the product chosen by the whole company.
I rate the tool an eight out of ten.
Databricks Review
What do you like best about the product?
The greatest upside to the Databricks Platform that it's constantly being developed. Databricks as well as other companies are developing code and utilities to run on this platform. Notably Mosaic AI, has a tool called Mosaic Composer that is a low-code acellerator for training AI models which has been very benefical to use.
What do you dislike about the product?
I dislike that Databricks is beginning to abstract some of the configurability options available. For example, Databricks serverless. I want to keep the ability to tailor a cluster and libaries specific to my use-case rather than it handled by Databricks.
What problems is the product solving and how is that benefiting you?
Decreasing time from data to model.
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