In Apache Cassandra Lunch #53: Cassandra ETL with Airflow and Spark, we discussed how we can do Cassandra ETL processes using Airflow and Spark. The live recording of Cassandra Lunch, which includes a more in-depth discussion and a demo, is embedded below in case you were not able to attend live. If you would like to attend Apache Cassandra Lunch live, it is hosted every Wednesday at 12 PM EST. Register here now!
In Apache Cassandra Lunch #53: Cassandra ETL with Airflow and Spark, we show you how you can set up a basic Cassandra ETL process with Airflow and Spark. If you want to hear why we used the bash operator vs the Spark submit operator like in Data Engineer’s Lunch #25: Airflow and Spark, be sure to check out the live recording of Cassandra Lunch #53 below!
In this walkthrough, we will cover how we can use Airflow to trigger Spark ETL jobs that move data into and within Cassandra. This demo will be relatively simple; however, it can be expanded upon with the addition of other technologies like Kafka, setting scheduling on the Spark jobs to make it a concurrent process, or in general creating more complex Cassandra ETL pipelines. We will focus on showing you how to connect Airflow, Spark, and Cassandra, and in our case today, specifically DataStax Astra. The reason we are using DataStax Astra is that we want everyone to be able to do this demo without having to worry about OS incompatibilities and the sort. For that reason, we will also be using Gitpod, and thus the entire walkthrough can be done within your browser!
For this walkthrough, we will use 2 Spark jobs. The first Spark job will load 100k rows from a CSV and then write it into a Cassandra table. The second Spark job will read the data from the prior Cassandra table, do some transformations, and then write the transformed data into a different Cassandra table. We also used PySpark to reduce the number of steps to get this working. If we used Scala, we would be required to build the JAR’s and that would require more time. If you are interested in seeing how to use the Airflow Spark Submit Operator and run Scala Spark jobs, check out this walkthrough!
You can also do the walkthrough using this GitHub repo! As mentioned above, the live recording is embedded below if you want to watch the walkthrough live.
This will be a pay-as-you-go method, but they won’t ask for a payment method until you exceed $25 worth of operations on your account. We won’t be using nearly that amount, so it’s essentially a free Cassandra database in the cloud.
create databaseand wait a couple minutes for it to spin up and become
dashboard/<your-db-name>, click the
Admin Userfor role and hit generate token.
Connecttab in the menu
Node.js(doesn’t matter which option under
Connect using a driver)
Secure Bundleinto the running Gitpod container.
We will be using the quick start script that Airflow provides here.
Open port 8081 in the browser, copy the master URL, and paste in the designated spot below
mv spark_dag.py ~/airflow/dags
If it does not exist yet, give it a few seconds to refresh.
example_cassandra_etl, and drill down by clicking on
Adminsection of the menu, select
spark_defaultand update the host to the Spark master URL. Save once done.
DAGmenu item and return to the dashboard. Unpause the
example_cassandra_etl, and then click on the
select * from <your-keyspace>.previous_employees_by_job_title where job_title='Dentist';
select * from <your-keyspace>.days_worked_by_previous_employees_by_job_title where job_title='Dentist';
And that will wrap up our walkthrough. Again, this is an introduction on how to set up a basic Cassandra ETL process run by Airflow and Spark. As mentioned above, these baby steps can be used to further expand and create more complex and scheduled/repeated Cassandra ETL processes run by Airflow and Spark. The live recording of Apache Cassandra Lunch #53: Cassandra ETL with Airflow and Spark is embedded below, so if you want to watch the walkthrough live, be sure to check it out!
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