The platform as a service allows us to ramp up a new database pretty fast. We deploy some of the infrastructure as a code. End users can access data immediately and connect with Power BI for reporting. 

The tool helps us manage large amounts of data. 

It has allowed our data engineers, data scientists, and analysts to collaborate and work on the same platform. 

Using Databricks enables us to use the Data Mesh methodology, where every team performs their own ETL.

We didn't have any good tooling for ELT processing prior to Databricks. We were using Microsoft HD Insight, but it was taking too long to process the data. When we changed our data-processing ELT processes over to Databricks, the amount of time to process the data was reduced to a fraction of what HD Insight used, so we were able to run jobs much faster.

The way Databricks has improved my organization is definitely through giving us improved performance and the processing power. We are usually never able to achieve it using traditional data warehouses. When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks.

Databricks has helped us have a good presence in data. 

Data is open to everyone; they can access it through many channels, including notebooks or SQL. That on its own democratizes the data.

Databricks collaborates very well with the Azure platform, Dataiku, and enterprise AI tool. Databricks is a new connection to pull the data or connect to the Spark cluster. It is helpful for us to instance it or distribute the load through the Spark cluster, and it is very user-friendly.

Before I used Databricks it took me a long time to do some functions and now with Databricks I can do them much quicker. It scales very well.

Databricks provides a consistent interface for data engineers to work with data in a consistent language on a single integrated platform for ingesting, processing, and serving data to the end user.

Databricks took care of all the underlying cluster management seamlessly. We could configure our clusters to run and deliver results without any delays due to hardware configuration or installation issues.

Databricks allowed us to go from non-existent insights (because the datasets were just too large) to immediate and rich insights once the datasets were ingested into our PySpark notebooks.

Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things.

Whenever we send data to downstream applications for creating a file, multiple business rules are involved, and this solution assists with quickly computing a considerable amount of historical data.

Databricks (delta lake) and the underlying files storage (data lake) is in the centre of the organisation's enterprise data hub. Most of our data is structured (csv files), have some semi-structured (json files) but we are beginning to ingest unstructured (pdf files) and use Natural Language Processing (Textract) to obtain insights driven by key words.  

Prior to using Azure Databricks in the cloud, we had Databricks installed in clusters. Since our implementation, the performance has increased and our cost has been reduced.