Its big data processing capablity and multiple language interface in one notebook

Workflows monitoring and ui is not mature enough to handle multiple pipeline exceution

Big data processing and it saves lot of our time and space and boost our descion making capablity

It's designed for engineers! Makes accessing cloud cluster so convenient!

It's hard to understand full cost at times.

It is our heart of our data platform at the organization. From reporting to advanced analytics

There are lots of things for example: 1. An excellent Data Engineering tool. 2. Support multiple languages like Scala, Python, R and SQL. 3. Support Analytical work as well. 4. We can create and deploy Machine Learning Models. 5. Very fast as build on top on Apache Spark. 6. Multi language support within one Notebook. 7. Compute is extremely faster and cluster options are available like All-Purpose and Job Clusters. 8. Function like cluster pools are very useful. 9. Using it daily and loving it's UI and functinalities.

I am using Databricks since 2019, and have no complaint or issues as such. There is one area where more functionality can be added is in FS command (File System). More functionality can give more flexibility to the developers.

We are using Databricks for all data related tasks, be it for creation of Data Pipeline for Data Engineering, for Data Analytics to present reports to business user and for machine learning tasks as well. Along with that scheduling jobs and pipelines.

Unified data eco system capability for DE and ML

Steep learning curve, still evolving on basics

Gives you unified lakehouse capability to break down silos in data teams. Being able to create E2E solution in one solution rather than using many solution from seperate companies

There are many ETL tools , but nothing comes near Databricks. We can have n number of complex transformations that can be done in datbricks notebooks using pyspark and Unity catalog is the best feature. Customer support is very good, had to connect with them for some external integrations like prefect.

Costing is more, but if you have proper policies in place as organization and have control over cluster usage then can easily overcome high cost.

Many of our application teams needs ETL processing. So Databricks integartion to our platform made their works easy.

This is the one and only data lake house platform covers everything a DataEngineer needs. Unity catalog feature is best as it helps us maintain Medalian architecture. Maintaining notebooks is easy.

Only thing is its very costly, so need to constantly monitor resource usage.

Helps in developing day to day ETL processes and makes it easy to run notebooks and jobs

The best part about Databricks is we can use many languages like sql , python , scala in same platform for our work , secondly UI is best .

As such nothing I only feel one drawback is thst they are not having there own storage

We were using many platforms like PBI , SQL , IDLE but we got all on same platform after using this product.

It is a single platform covering data engineering and machine learning. People in different roles, such as data scientists, machine learning engineers, and data engineers, can conveniently work together on it.

There is one issue I really hope they can fix. The widgets panel settings of notebooks default to "Run Accessed Commands," which I find really inconvenient. I strongly suggest they change it to "Do Nothing."

Machine learning R&D; data pipeline. I use Databricks every day.

About azure Databricks what I like most is,it's ability of providing multiple services for data like data processing,management,analysis. It is extensively used by data engineer for data extract,tranform and load in professional work. It allows user to write basic and advanced transformation logic within notebook. Also clustered is automatically managed by service provider thus user do not need to worry about it. And the GUI and ease of use is simple. High Customer Support.

Sometime pricing structure create problem for small business. Starting cluster with high dataset take more time to get started.

Databricks helps to handle big data with maximum potential,capacity and with ease ,and it very easy to create connection from databricks to snowflake for data loading. In notebook using pyspark,SQL,python language etc we can create complex transformation logic in databricks,which is very helpful data engineer and data scientist.

It is very easy to use and we do not need to manage the cluster configuration as it is properly automated. It is used by datascientis and data engineer for data transformation and Extract tranform and load (ETL).. I like the Graphical user interfacer and it allow me to use with azure cloud platforms. Also it is less costly then other tools like snowflakes.

When we are using it with large dataset then it get costly. And the major thing that I dislike is,more dependency on microsoft azure cloud provider.

It is very helful in writing transformation scripts inside notebbok using python or sql language. Data loading to snowflake is very easy through databricks. Large volumes of data processing efficiently might be challenging but using Azure Databricks it become easy to handle big data with ease,as it built on Apache Spark that offers scalable and distributed data processing capabilities.