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easy to use platform for large scale data ETL and analytics
What do you like best about the product?
Has tools like AutoML which reduces human effort and increases better predictions and deeper understanding of the data
What do you dislike about the product?
The platform can be slow sometimes. Other than that not major issues worth mentioning
What problems is the product solving and how is that benefiting you?
Analysis and Analytics. Use case - Labour market research
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Great Experience All Around
What do you like best about the product?
A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script experience and synchronize to git.
What do you dislike about the product?
Debugging code and using interactive applications outside out databricks approved tools can be tricky. It is hard to get a grasp of the documentation for beginners to the platform.
What problems is the product solving and how is that benefiting you?
Highly scalable data pipelines with machine learning tools. Geospatial analyses. The scalability of the platform really increased our efficiency and reaction speed to customer requirements.
High performance with low complexity
What do you like best about the product?
The way that works with the delta format. Providing a lot of possibilities without the necessity to have a dedicated database administrator. It's also nice to talk about their flexibility
What do you dislike about the product?
Sometimes the cluster management is not so well distributed, causing some necessity to restart the cluster. Maybe send some warnings before it gets non workable.
What problems is the product solving and how is that benefiting you?
Problems: Increasing data results in almost a logarithmic increase in the cost. The main benefits were regarding the costs and the flexibility that's delta format provide
One stop place for setting up my entire data analysis pipeline
What do you like best about the product?
It offers multi-cloud support across AWS, GCP, and Azure
New features are aggressively released every quarter.
The UI is relatively user-friendly compared to AWS EMR or other similar products
New features are aggressively released every quarter.
The UI is relatively user-friendly compared to AWS EMR or other similar products
What do you dislike about the product?
Errors are not entirely straightforward sometimes.
Regular Maintenance can sometimes cause downtime or failure, which can be solved with proper scheduling and retry mechanisms.
Regular Maintenance can sometimes cause downtime or failure, which can be solved with proper scheduling and retry mechanisms.
What problems is the product solving and how is that benefiting you?
It is higly efficient in executing queries, analyzing data, performing complex table joins if workloads are properly setup across high-performing clusters.
Lakehouse helps solving Big data , streaming and Analytical problem
What do you like best about the product?
Delta Lake , SQL Analytics , Optimized Photon engine
What do you dislike about the product?
Notebook UI , performance for SQL analytics on huge volume on the fly aggregate calculations
What problems is the product solving and how is that benefiting you?
Big data problem, ML , SQL analytics , Streaming and replacing traditional Oracle Datawarehouse
Recommendations to others considering the product:
Yes highly recommended for setting up Hadoop kind of cluster and dealing with Big data and Analytics and Machine learning ML flow.
Databricks as a market leader
What do you like best about the product?
It is a one-stop shop for all. It is clearly the best data processing solution that I have ever used.
What do you dislike about the product?
There is still room for improvements - for example, when you deploy, it could be nice to set the configuration of DBFS storage (is it LRS or GRS, etc.). When VMs are deployed, there should be more options to configure hard drives. It is a minor issue related to administrative tasks, but anyway, it is the best lakehouse on the market today.
What problems is the product solving and how is that benefiting you?
Data processing, data lake, machine learning, streaming live data...
Data Scientist
What do you like best about the product?
Experiment management, and model deployment.
What do you dislike about the product?
Support for code engineering and version control.
What problems is the product solving and how is that benefiting you?
Predictive modeling.
Its very helpful when we train ml model for tracking
What do you like best about the product?
Machine learning model tracking and find best weight
What do you dislike about the product?
Add support for other programming language like cpp
What problems is the product solving and how is that benefiting you?
Tracking multiple training and find out best weights
MLflow is a very useful open source tool
What do you like best about the product?
MLflow tracking has been a major advantage for keeping up the record of the results of the experiments we carry out on the data using different parameters. Tracking the results and parameters is very iseful for achieving the most optimized solution.
What do you dislike about the product?
One small counter point is that it is not an easy tool and requires all in depth knowledge for making the best use of it.
What problems is the product solving and how is that benefiting you?
I am currently utilizing the MLflow MLflow tracking utility.
Recommendations to others considering the product:
Highly recommend this tool to the users.
MLflow makes ML life cycle management quite streamlined with easy implementation.
What do you like best about the product?
I like how it forces the developer to follow a certain code style which can basically help maintain the codebase much easily over time and have a proper documentation over it.
What do you dislike about the product?
I think there could be improvements within the documentation over how to use MLflow within existing codebases.
What problems is the product solving and how is that benefiting you?
My manager wanted to have a visual UI to track and monitor ml projects and metrics and also be able to import a model quickly and try it out, mlflow makes it really easy to do that.
Recommendations to others considering the product:
Read through the documentation
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