Extract, Transform, and Load Example: S3

In this example, we will demonstrate how to extract, transform, and load data from Amazon Simple Storage Service (S3) using Apache Spark on Databricks. This process is essential for making raw data available for further processing and analysis.

To begin, we need to create a cluster in Databricks. Once the cluster is created, open a new notebook and enter the following code into the first cell:

```python

from pyspark.sql.types import DoubleType, IntegerType, StringType, StructType, StructField

file_path = '/databricks-datasets/songs/data-001/'

table_name = ''

checkpoint_path = '/tmp/pipeline_get_started/_checkpoint/song_data'

schema = StructType('')

spark = spark.createDataFrame('', schema)

```

This code imports the necessary libraries and defines variables for the file path, table name, checkpoint path, and schema. The `StructType` function is used to define the structure of our data.

Next, we need to configure Auto Loader to automatically detect the schema of our loaded data. This eliminates the need to manually track and apply schema changes over time.

```python

option(checkpointLocation, checkpoint_path)

```

This code sets the checkpoint location for our data processing pipeline.

Now that our cluster is set up and Auto Loader is configured, we can load the raw data into a table using the `spark.read.format` function:

```python

songs_data = spark.read.format('csv').option('inferSchema', 'true').load(file_path)

```

This code loads the CSV file from S3 into a Spark DataFrame named `songs_data`. The `inferSchema` option tells Auto Loader to automatically detect the schema of our data.

Once the data is loaded, we can transform it as needed using various Spark functions. For example, we could use the `dropDuplicates` function to remove duplicate rows:

```python

transformed_songs_data = songs_data.drop_duplicates()

```

Finally, we can load the transformed data into a table in our Databricks cluster using the `write.saveAsTable` function:

```python

transformed_songs_data.write.saveAsTable(table_name)

```

This code saves the transformed data to a table named `songs_data`.

In this example, we demonstrated how to extract, transform, and load data from S3 using Apache Spark on Databricks. By using Auto Loader and configuring our schema correctly, we can automate the process of loading and transforming large datasets.

Extract transform load example s3