Databricks Valuable Features

Of the available feature set, I like the Imageflow feature a lot. It is very interesting. It gives me clarity on the execution of a process. I can draw the complete flow from start to finish in the exact way that I want it to execute. It is more visual and it is also easier for the people in businesses where I make presentations to understand. When I demonstrate a process to a business and show them the approach I am taking using code and technical language, then of course not many are going to understand that. But when I show them the process in terms of the graphical layout Imageflow helps provide, then they will be able to understand it much easier. They understand why I am choosing a particular way of executing the process and why I am taking certain steps in the way I have chosen to do it. The point is to help other people understand the solution more clearly. View full review »

The elasticity of the solution is excellent. The storage, etc., can be scaled up quite easily when we need it to. It's easy to increase performance as required. The solution runs on Spark very well. View full review »

Immense ease in running very large scale analytics, with a convenient and slick UI. This saved us from having to tweak, tune, dive into deeper abstractions, get involved in procurement, and also having to wait for other workloads to run. The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly. The Delta data format proved excellent. Databricks had already done the heavy lifting and optimized the format for large scale interactive querying. They saved us a lot of time. View full review »

You can spin up an Azure Databricks clustered, and integrating with it is seamless. The integration with Python and the notebooks really helps. View full review »

The most valuable feature is the ability to switch loads between multiple clusters. Automation with Databricks is very easy when using the API. The ability to write code and SQL in the same interface is useful. It is easy to connect notebooks to a cluster. There are a large number of inbuilt functions that help to make things easier. View full review »

Databricks helps crunch petabytes of data in a very short period of time for data scientists or business analysts. It helps with fraud analysis, finance, projections, etc. I like it. This is exactly the purpose of big data and analytics. It provides the mechanism to process and analyze a stream of information. It's best for share analysis and stream analysis. View full review »

Valuable features would have to include the Notebook for piping some models and the future of executing the notebooks in parallel, in batches, which is also something that we use. And we use the Notebook on Spark with Python. View full review »

The most valuable feature is the ability to use SQL directly with Databricks. That is the most relevant thing for my current project. After deployment, it is easy to load files and query data. View full review »

I found that PySpark is the most useful tool. It uses in-memory calculation and when you want to run a model it does it very quickly. We used to use Python and when we migrated to PySpark the performance was much better. View full review »

The time travel feature is the solution's most valuable aspect. View full review »

The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient. View full review »

One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often. You can just spin it off and use that for a lot of your pre-processing, which is very convenient. The normal features are very good in terms of doing some quick development or doing some EDA. Also, one of the newest features brought into this solution provides you with a way to solve, deploy, and train models using the platform itself. Or, it can connect to your Azure Machine Learning in order to train, deploy, and productionalize some of the machine learning models. View full review »

I think the features I like the most are the scalability of the solution as well as its ability to share. We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for. View full review »

Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great. This solution has very good machine learning libraries built-in. The support for big data is good. View full review »

The fast data loading process and data storage capabilities are great. Based on the data loads and the performance, you can easily scale up the clusters. View full review »

I think what I value is more about the technology itself because you don't need to have too much knowledge to be able to use the solution. View full review »