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Brilliant on developing the best collaborative platform for data scientists and data engineers
What do you like best about the product?
An interface that is better than Jupyter notebooks that allows SQL, Scala, PySpark, Python, R and the ability to collabortate on notebooks
What do you dislike about the product?
DPU based billing is fixed and minimum is 3 node cluster. For a small entity the advantages of using AWS Glue interface to Spark 2.x outweighs the benefits of a permenant cluster runnig with Databricks.
What problems is the product solving and how is that benefiting you?
Big data management in lake type architecture using Parquet formats, PySpark developments and enhancements.
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Databricks is the best option for your data workloads and pipelines
What do you like best about the product?
It is a highly adaptable solution for data engineering, data science, and AI
What do you dislike about the product?
I wouldn't say I like the lack of an easier way to import personalized code files or libraries from notebooks.
What problems is the product solving and how is that benefiting you?
I've solved emergency telephone data processing and insights. The performance of the solution is desirable.
Senior Cloud Evangelist and Architect
What do you like best about the product?
Spark Distribution of query and speed of batch query so does performance
What do you dislike about the product?
Interface can be make better and more intutive
What problems is the product solving and how is that benefiting you?
Big Batch bulk Parallel programming
Great platform for our Big Data needs
What do you like best about the product?
Easy administration, easy to create jobs from notebooks, great development environment, new and exciting features coming.
What do you dislike about the product?
Taking away our dedicated customer service rep and replacing this with just a support GUI.
What problems is the product solving and how is that benefiting you?
All our data pipelines are on Databricks. Benefitted from improved performance on Spark.
Staging data for insights
What do you like best about the product?
It makes the power of Spark accessible and innovative solutions like Delta Lake.
What do you dislike about the product?
Fewer solutions that aren't wholly or partially on the cloud.
What problems is the product solving and how is that benefiting you?
We are staging large datasets for reporting and multiple BI solutions.
Best tool for big data
What do you like best about the product?
Easy to use multiple languages based command in same notebook. Direct connection to Redshift.
What do you dislike about the product?
Sometime it takes lot of time to load data. Should show better suggestions.
What problems is the product solving and how is that benefiting you?
We are using databricks to analyse big data and get business insights.
One stop shop for all your data problems
What do you like best about the product?
It has got everything in it. IDE, Version Control, Scheduling whatnot.
What do you dislike about the product?
I didn't find something that discomforts me yet.
What problems is the product solving and how is that benefiting you?
Currently, I'm using it as an ETL tool. It's easy to use and connects with any data source—excellent documentation and help from the community.
Recommendations to others considering the product:
Just go for it. You can do many things you want to do with your data.
Very powerful yet easy to use distributed computing and data warehousing platform
What do you like best about the product?
Databricks had very powerful distributed computing built in with easy to deploy optimized clusters for spark computations. The notebooks with MLFlow integration makes it easy to use for Analytics and Data Science team yet the underlying APIs and CICD integrations make it very customizable for the Data Engineers to create complex automated data pipelines. Ability to store and query and manipulate massive Spark SQL tables with ACID in Delta Lake makes big data easily accessible to all in the organization.
What do you dislike about the product?
It lacks built in data backup features and ability to restrict data access to specific users. So if anyone accidentally deletes data from Delta Table or DBFS, the lost data cannot be retrieved unless we setup our own customized backup solution.
What problems is the product solving and how is that benefiting you?
I have worked with big data with hundreds of millions of rows using databricks. We do most of the ELT, data cleaning and prepping works on databricks. The ease and speed of querying bid data using databricks SparkSQL is very useful. It is also very easy to create prototype codes utilizing real sized data using the available Python and R notebooks.
Reduced database network redistributions & run-time of key models by 99+%!
What do you like best about the product?
Incidentally, the thing I like most about Databricks isn't a product feature at all; I love Databricks's proactive and customer-centric service, always willing to make an exception or create a unique feature, all the while minimizing costs for the customer - as @Heather Akuiyibo & Shelby Ferson et al. have done for me and my former teams!
What do you dislike about the product?
Broadening programming logic and syntax.
What problems is the product solving and how is that benefiting you?
To name seven (7):
(1) User segmentation using a proprietary variation of a hierarchical DBSCAN clustering algorithm of high-dimensional data with novel distance [quasi] metric, based on hubness analysis;
(2) Leveraging the above in email targeting and invoking multi-armed bandit testing methodologies for email timing, frequency, and content, using decreasing-epsilon strategy;
(3) Modeling predicted underwriting criteria with a binary approval odds classification algorithm;
(4) Using a dynamic panel data, fixed effects model to predict the effect of changes in credit reports on user credit score;
(5) Employing an Autoregressive Integrated Moving Average (ARIMA) with optimized Akaike Information Criterion exploits to predict future revenue and growth (lagged results led to average error bounds of only 5 percent; cross-validation results were even stronger, though I was conservative in guaranteeing 7 percent error, on average);
(6) Refining a multiverse (context-aware) recommendation engine as an n-dimensional tensor (rather than the typical two-dimensional user-item matrix) for partner product recommendations, using High-Order Singular Value Decomposition to solve;
(7) Invoking a Convolutional Neural Network framework with a novel architecture and results of a Fourier Transform as input to classify dental x-rays and highlight to the dentist which teeth require fillings (after approximately two months, the model reached ~95 percent accuracy - in terms of actual agreement by dentists using the app - with F1 score in cross-validation performing on par).
(1) User segmentation using a proprietary variation of a hierarchical DBSCAN clustering algorithm of high-dimensional data with novel distance [quasi] metric, based on hubness analysis;
(2) Leveraging the above in email targeting and invoking multi-armed bandit testing methodologies for email timing, frequency, and content, using decreasing-epsilon strategy;
(3) Modeling predicted underwriting criteria with a binary approval odds classification algorithm;
(4) Using a dynamic panel data, fixed effects model to predict the effect of changes in credit reports on user credit score;
(5) Employing an Autoregressive Integrated Moving Average (ARIMA) with optimized Akaike Information Criterion exploits to predict future revenue and growth (lagged results led to average error bounds of only 5 percent; cross-validation results were even stronger, though I was conservative in guaranteeing 7 percent error, on average);
(6) Refining a multiverse (context-aware) recommendation engine as an n-dimensional tensor (rather than the typical two-dimensional user-item matrix) for partner product recommendations, using High-Order Singular Value Decomposition to solve;
(7) Invoking a Convolutional Neural Network framework with a novel architecture and results of a Fourier Transform as input to classify dental x-rays and highlight to the dentist which teeth require fillings (after approximately two months, the model reached ~95 percent accuracy - in terms of actual agreement by dentists using the app - with F1 score in cross-validation performing on par).
Recommendations to others considering the product:
Be open to the pitch. You may think things are "going fine" or proffer the idea of "if it ain't broke, don't fix it," but these represent short-term thinking traps such that scaling becomes inherently and implicitly constrained and limited. Databricks amounts to the forward-thinking businessperson.
How I experienced databricks
What do you like best about the product?
It is great when you have large amount of data, excellent for collaboration, perfect for using with visualisation tools and functions with many programming languages.
What do you dislike about the product?
Difficult to get a grasp on how many applications and funcrions it has.
What problems is the product solving and how is that benefiting you?
It s great for ELT of date to use with power BI
Recommendations to others considering the product:
Use it it s the best available and it s great!
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