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Using data from Data sources. Using data from Data sources. Using data from Data sources. • Take a look at the examples below to see how to use Spark SQL to analyze data with multiple table schemas. Finding Out Spark SQL supports multiple data sources as a result of the No Data sources. Using data from Data sources. In addition to using data from multiple sources, Spark SQL supports filtering and joining across data sources. 8.10 Spark SQL and Parallel Execution Let's build a parallel execution framework that processes data, and then run our SQL queries against the results. 1. Create the scripts to start up the workload Running big data processing jobs using Spark involves two things: We need to prepare our data, and then we need to execute our code against this data. 1.1 Setting up the scripts to run the job To create these scripts, we’ll be making use of a utility called Databricks Notebook. We’ll use the Databricks Notebook in this example because it’s very well suited for creating and managing the scripts for this example. 1.2 Preparing the data In this case, we’ll be using the BigQuery data source to load some data into our environment. This will be a great starting point for running more queries against this data. 2. Run the jobs in parallel The next step is to run our job in parallel. With the Notebook, we can use several different methods to run the job in parallel. We’ll be using the Spark-submit script to run the job. 2.1 A simple BigQuery to Spark program We’ll create a simple script to copy data from BigQuery to our environment. This will be a great starting point for running Spark SQL against this data. 2.2 Running the Spark job To run our script in parallel, we’ll use the Databricks Notebook’s Execution Settings to define the configuration for our Spark job. In the above screenshot, we’re using two resources (a VPC and an EC2 instance). The resources are distributed across several nodes. As we’ll see in the next section, we can use this configuration to have the job run on as many nodes as we need. 2.3 Adding Spark SQL to the program Next,

 

 


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