SQL and Spark are both powerful tools for Big Data processing.
Both of these tools have different strengths and weaknesses depending on the specific use case.
Spark is generally faster than SQL for Big Data processing.
Spark is designed to scale horizontally to handle massive amount of data.
On the other hand SQL may have limitations in terms of scalability depending on the specific database
SQL is generally easier to learn and use than Spark.
Spark can be more expensive to run than SQL, as it requires more resources and infrastructure.
Ultimately, the choice between SQL and Spark will depend on the specific requirements and the resources available to you.
SQL for time series data analysis