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Apache Cassandra Lunch #46: Apache Spark Jobs in Scala for Cassandra Data Operations - Business Platform Team


This resource is based on an article originally published here.

In Apache Cassandra Lunch #46: Apache Spark Jobs in Scala for Cassandra Data Operations, we discuss how we can do Apache Spark jobs in Scala Cassandra data operations. 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 #46, we discuss how we can use Apache Spark jobs written in Scala to do Cassandra data operations. We have a walkthrough to show you how you can run Apache Spark jobs to do some Cassandra Data operations below, but also check out this blog for an additional walkthrough on how to do other Cassandra data operations that we did not cover in this Apache Cassandra Lunch session. The live recording embedded below contains a live demo as well, so be sure to watch that as well!


In this walkthrough, we will run a few different spark jobs to do some ETL data operations of Cassandra data. You can follow along on this blog, or check out this GitHub repo and follow along with the there.


  • Docker
  • sbt
  • Apache Spark 3.0.x

1. Build Fat JAR

1.1 – Clone repo and cd into it

git clone
cd example-cassandra-spark-job-scala

1.2 – Start sbt server in directory


1.3 – Run assembly in sbt server


2. Navigate to Spark Directory and Start Spark

2.1 – Start Master


2.2 – Get Master URL

Navigate to localhost:8080 and copy the master URL.

2.3 – Start Worker

./sbin/ <master-url>

3. Start Apache Cassandra Docker Container

docker run --name cassandra -p 9042:9042 -d cassandra:latest

3.1 – Run CQLSH

docker exec -it cassandra CQLSH

3.2 – Create demo keyspace

CREATE KEYSPACE demo WITH REPLICATION={'class': 'SimpleStrategy', 'replication_factor': 1};

4. Read Spark Job

In this job, we will look at a CSV with 100,000 records and load it into a dataframe. Once read, we will display the first 20 rows.

./bin/spark-submit --class sparkCassandra.Read \
--master <master-url> \
--files /path/to/example-cassandra-spark-job-scala/previous_employees_by_title.csv \

5. Manipulate Spark Job

In this job, we will do the same read; however, we will now take the first_day and last_day columns and calculate the absolute value difference in days worked. Again, then display the top 20 rows.

./bin/spark-submit --class sparkCassandra.Manipulate \
--master <master-url> \
--files /path/to/example-cassandra-spark-job-scala/previous_employees_by_title.csv \

6. Write to Cassandra Spark Job

In this job, we will do the same thing we did in the manipulate job; however, we will now write the outputted dataframe to Cassandra instead of just displaying it to the console.

./bin/spark-submit --class sparkCassandra.Write \
--master <master-url> \
--conf \
--conf spark.cassandra.connection.port=9042 \
--conf spark.sql.extensions=com.datastax.spark.connector.CassandraSparkExtensions \
--files /path/to/example-cassandra-spark-job-scala/previous_employees_by_title.csv \

7. SparkSQL Spark Job

In this job, we will write the CSV data into one Cassandra table and then pick it up using SparkSQL and transform it at the same time. We will then write the newly transformed data into a new Cassandra table.

./bin/spark-submit --class sparkCassandra.ETL \
--master <master-url> \
--conf \
--conf spark.cassandra.connection.port=9042 \
--conf spark.sql.extensions=com.datastax.spark.connector.CassandraSparkExtensions \
--files /path/to/example-cassandra-spark-job-scala/previous_employees_by_title.csv \

And that will wrap up the walkthrough on how to do some Cassandra data operations with Apache Spark jobs. Again, check out this blog as well for more Cassandra data operations that we can do with Apache Spark. As mentioned above, the live recording which includes a live walkthrough of this demo is embedded below, so be sure to check it out and subscribe to keep up to date with


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