From Signup to Subsecond Dashboards in Minutes with Dremio Cloud
Alex MercedDeveloper Advocate
Introducing Dremio Cloud
Have you heard the good news? Dremio released Dremio Cloud, the forever free open lakehouse platform. Incur no software costs or licensing costs and have the power of the Dremio’s lakehouse query engine, Sonar, and its intelligent metastore for Iceberg tables, Arctic. Let me show you how easy it is to get started.
Signing up for Dremio Cloud
To sign up for your free account, go to the Get Started page.
On the next page, configure your cloud connection. The easiest way to do this is via the provided CloudFormation template.
Adding Users and Roles
After you are done with CloudFormation you have the optional step of adding additional users and roles to your account. Once complete, click Next and you will be on the dashboard of the Dremio interface.
Add Sample Data
From the user interface, we will want to add sample data to our Sonar project. Click the dd Sample Data button and a new sample source will be added. Next:
Click on the “Samples” data source
Click on the “samples.dremio.com” folder
Next to the “NYC-taxi-trips” folder, click the button to the right to promote the folder to a dataset
Creating a New Column and Saving a Virtual Dataset
It is easy to curate data for many purposes in Dremio Sonar. Let’s take the field that represents the distance in miles and use it to create a new column to show us the distance in kilometers. To do this:
Click on the right corner of the distance_in_mi field to reveal a dropdown and select “calculated field”
On the next screen, let’s multiply the miles field by 1.6 to get the values for our kilometer field (a mile is roughly 1.6 kilometers)
Uncheck the box to keep the existing miles field
Click the dropdown in the upper right and select “Save View As” and name it NYC-taxi-data
Go back to the main screen and select new View/Virtual Dataset
Creating Data Reflections
Dremio Sonar has many performance capabilities to bring you blazing-fast queries at scale. But we want our queries to be subsecond on our 340 million-record dataset, especially when we’re creating BI dashboards in Tableau. Dremio uses the Data Reflections tool to optimize queries. Reflections automates the optimization of queries on high-priority datasets. To use Reflections:
Click Reflections in the top nav
Turn on the toggle next to “Aggregate Reflections”
Remove passenger_count from the dimension list, leaving pickup_datetime as the only dimension
Add passenger_count to the list of measures
Right-click on history to see the clock. When the clock stops, that indicates that the Reflection has been generated.
Return to the main screen for the dataset
With a few clicks we can connect tools like Tableau to our Dremio account to work directly and performantly with our data lake data, no need for cubes or marts. To connect our sample dataset to Tableau:
Click on the Tableau logo to download the .tds file
Open the .tds file to open Tableau
Once Tableau opens, it will open a new tab in your browser so you can sign on and establish a connection with your Dremio account
Return to Tableau and you’ll see all your columns from your dataset
Move the “migrated_data” field into rows to see a full count of the dataset (this should be about 340 million records)
Remove the field from rows and replace with passenger_count to see a count of all passengers from every ride. Note that each time we update the rows and columns, it is undergoing a new query on our 340 million-record dataset, which is completing in subsecond times thanks to the Reflection we created earlier.
Move pickup_datetime to be a column
Right-click on pickup_datetime to change it to measure by days
Go back to the Dremio UI in your browser and click on the Jobs page to see all the queries Tableau sent and how quickly they have completed
Now you’ve experienced how easy Dremio Cloud makes data lakehouse architectures. Within a few moments of signup, you can be performantly querying and discovering insights from the data in your data lake.
Your Dremio Cloud account comes with additional sample datasets that you can use. Also, since you’re running Dremio Cloud in your AWS account, you can connect Sonar to any dataset that’s in your AWS account in Amazon S3 or other sources.
From your Dremio Cloud account you can also try out Dremio Arctic, an intelligent metastore for Apache Iceberg. Just open up your organization settings and create a new project. Look for upcoming articles on getting started with Dremio Arctic.
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