Data Engineering and ChatGPT (Part2)
Tags: datascience, chatgpt, aws, glue
Published: 2022-12-06 20:43:23

In my previous [post](https://dev.to/aws-builders/data-engineering-and-chatops-3h6a) i must have hit some issue with ChatGPT. It's way more impressive then i thought..

---
## question 1

> can you write a pyspark job that inserts data into an iceberg table

Sure, here is an example of a PySpark job that inserts data into an iceberg table:

```
from pyspark.sql import SparkSession

# create a Spark session
spark = SparkSession.builder \
    .appName("Insert into iceberg table") \
    .getOrCreate()

# read the data to be inserted into the iceberg table
data = spark.read.csv("/path/to/data.csv")

# specify the name of the iceberg table and the name of the data table
# that will be used to store the data in the iceberg table
iceberg_table = "my_iceberg_table"
data_table = "my_data_table"

# write the data to the data table
data.write.format("iceberg").mode("append") \
    .option("table", iceberg_table) \
    .option("dataTable", data_table) \
    .save()

# commit the transaction
spark.catalog.commit_transaction()
```

This PySpark job reads data from a CSV file and inserts it into the iceberg table my_iceberg_table using the data table my_data_table. It uses the append mode, which means that the data will be appended to the existing data in the iceberg table.

Note that this is just an example, and it may need to be modified to fit your specific needs. For example, you may need to adjust the path to the input data and the names of the tables to match your environment.

---
## question 2

> can you do the same in athena sql?

Yes, here is an example of how you can insert data into an iceberg table using Athena SQL:

```
-- create the data table that will be used to store the data in the iceberg table
CREATE TABLE my_data_table (
  ... -- specify the schema of the data table here
);

-- load the data into the data table
LOAD DATA INPATH '/path/to/data.csv' INTO TABLE my_data_table;

-- create the iceberg table that will store the data from the data table
CREATE TABLE my_iceberg_table (
  ... -- specify the schema of the iceberg table here
)
USING iceberg
LOCATION '/path/to/iceberg/table'
WITH (
  data_table = 'my_data_table'
);

-- insert the data from the data table into the iceberg table
INSERT INTO my_iceberg_table
SELECT * FROM my_data_table;
```

This Athena SQL script first creates the data table my_data_table, loads data into it from a CSV file, creates the iceberg table my_iceberg_table, and then inserts the data from the data table into the iceberg table.

As with the PySpark example, this is just an example, and you may need to adjust it to fit your specific needs. For example, you may need to adjust the path to the input data, the names of the tables, and the schema of the tables to match your environment.

---

## question 3

> and convert this into a dbt model

Here is an example of how you can convert the SQL code for inserting data into an iceberg table into a dbt model:

```
{% set data_table = "my_data_table" %}
{% set iceberg_table = "my_iceberg_table" %}

{{ config(materialized='table') }}

-- create the data table that will be used to store the data in the iceberg table
CREATE TABLE {{ data_table }} (
  ... -- specify the schema of the data table here
);

-- load the data into the data table
LOAD DATA INPATH '/path/to/data.csv' INTO TABLE {{ data_table }};

-- create the iceberg table that will store the data from the data table
CREATE TABLE {{ iceberg_table }} (
  ... -- specify the schema of the iceberg table here
)
USING iceberg
LOCATION '/path/to/iceberg/table'
WITH (
  data_table = '{{ data_table }}'
);

-- insert the data from the data table into the iceberg table
INSERT INTO {{ iceberg_table }}
SELECT * FROM {{ data_table }};
```

This dbt model first sets the names of the data and iceberg tables as model variables, which makes it easier to customize the names of the tables without having to modify the SQL code. It then uses these variables to create the data table, load data into it, create the iceberg table, and insert the data from the data table into the iceberg table.

As with the previous examples, this is just an example, and you may need to adjust it to fit your specific needs. For example, you may need to adjust the path to the input data, the schema of the tables, and the model configuration to match your environment.

---

## Comments

I'm speechless. It's missing some context and it might need some tweaks. But this is good. Searching some examples from the documentation, StackOverflow or even github code search, normally helps a little bit in the right direction. But with this you can directly start to test.



## And more

Let's try what more it can do.

---
## Question 4

> Create the terraform code for a glue job to read data from s3 and write to redshift including the IAM policy

To create a Terraform configuration that defines a Glue job that reads data from Amazon S3 and writes it to Amazon Redshift, you can use the following code:

```
# Define the AWS provider
provider "aws" {
  region = "us-east-1"
}

# Define the IAM role for the Glue job
resource "aws_iam_role" "glue_job_role" {
  name = "glue_job_role"

  assume_role_policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": "sts:AssumeRole",
      "Principal": {
        "Service": "glue.amazonaws.com"
      },
      "Effect": "Allow",
      "Sid": ""
    }
  ]
}
EOF
}

# Define the IAM policy for the Glue job
resource "aws_iam_policy" "glue_job_policy" {
  name = "glue_job_policy"
  path = "/"

  policy = <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Action": [
        "s3:GetObject",
        "s3:ListBucket",
        "redshift:CopyFromS3"
      ],
      "Resource": [
        "arn:aws:s3:::my-bucket",
        "arn:aws:s3:::my-bucket/*",
        "arn:aws:redshift:us-east-1:xxxxxxxxxxxx:dbname/mydatabase"
      ],
      "Effect": "Allow"
    }
  ]
}
EOF
}

# Attach the IAM policy to the IAM role
resource "aws_iam_policy_attachment" "glue_job_attachment" {
  name = "glue_job_attachment"
  roles = [aws_iam_role.glue_job_role.name]
  policy_arn = aws_iam_policy.glue_job_policy.arn
}

# Define the Glue job
resource "aws_glue_job" "glue_job" {
  name = "glue_job"
  role_arn = aws_iam_role.glue_job_role.arn
  command {
    script_location = "s3://my-bucket/scripts/glue_job.py"
    python_version = "3"
  }
  default_arguments = {
    "--s3_bucket" = "my-bucket"
    "--s3_prefix" = "input"
    "--redshift_cluster" = "my-cluster"
    "--redshift_database" = "mydatabase"
    "--redshift_table" = "mytable"
  }
}
```

In this example, the aws_iam_role and aws_iam_policy resources define an IAM role and policy for the Glue job, respectively. The aws_iam_policy_attachment resource attaches the policy to the role, and the aws_glue_job resource defines the Glue job

---
## Question 5


> and the pyspark job that loads all data from s3 to redshift

To create a PySpark Glue job that reads all data from Amazon S3 and writes it to Amazon Redshift, you can use the following code:

```
# Import the necessary modules and classes
from pyspark import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.sql import SparkSession

# Create a SparkContext and a GlueContext
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

# Get the job parameters
args = getResolvedOptions(sys.argv, ["s3_bucket", "s3_prefix", "redshift_cluster", "redshift_database", "redshift_table"])

# Read all data from S3
input_frame = glueContext.create_dynamic_frame.from_options(connection_type="s3", connection_options={ "paths": ["s3://${args['s3_bucket']}/${args['s3_prefix']}"] })

# Convert the DynamicFrame to a DataFrame
input_df = input_frame.toDF()

# Write the DataFrame to Redshift
input_df.write \
    .format("com.databricks.spark.redshift") \
    .option("url", "jdbc:redshift://${args['redshift_cluster']}.xxxxxxx.us-east-1.redshift.amazonaws.com:5439/${args['redshift_database']}") \
    .option("dbtable", "${args['redshift_table']}") \
    .option("tempdir", "s3://${args['s3_bucket']}/tmp") \
    .mode("overwrite") \
    .save()

# Stop the SparkContext and close the SparkSession
sc.stop()
spark.stop()
```

In this example, the Glue job takes four parameters: the S3 bucket and key prefix where the input data is stored, and the Redshift cluster, database, and table where the output data should be written. The awsglue.utils.getResolvedOptions method is used to get the values of these parameters from the command line or the Glue job environment. The glueContext.create_dynamic_frame.from_options method is used to read all data from the specified S3 bucket and key prefix, and the toDF and write methods are used to convert the data to a DataFrame and write it to Redshift, respectively.

Note that this is just a simple example to illustrate the basic steps for reading all data from Amazon S3 and writing it to Amazon Redshift using PySpark. You may need to modify this code to handle the specific requirements and constraints of your data and your Redshift cluster.

---

## question 6

> change input to parquet

To modify the PySpark Glue job to read input data from Amazon S3 in the Apache Parquet format and write it to Amazon Redshift, you can use the following code:


```
# Import the necessary modules and classes
from pyspark import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.sql import SparkSession

# Create a SparkContext and a GlueContext
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

# Get the job parameters
args = getResolvedOptions(sys.argv, ["s3_bucket", "s3_prefix", "redshift_cluster", "redshift_database", "redshift_table"])

# Read all data from S3 in the Parquet format
input_frame = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={
        "paths": ["s3://${args['s3_bucket']}/${args['s3_prefix']}"],
        "format": "parquet"
    }
)

# Convert the DynamicFrame to a DataFrame
input_df = input_frame.toDF()

# Write the DataFrame to Redshift
input_df.write \
    .format("com.databricks.spark.redshift") \
    .option("url", "jdbc:redshift://${args['redshift_cluster']}.xxxxxxx.us-east-1.redshift.amazonaws.com:5439/${args['redshift_database']}") \
    .option("dbtable", "${args['redshift_table']}") \
    .option("tempdir", "s3://${args['s3_bucket']}/tmp") \
    .mode("overwrite") \
    .save()

# Stop the SparkContext and close the SparkSession
sc.stop()
spark.stop()
```

In this modified code, the glueContext.create_dynamic_frame.from_options method takes an additional format parameter, which specifies that the input data is in the Apache Parquet format. This causes the Glue job to automatically parse the data as Parquet and convert it to a DynamicFrame. The rest of the code is unchanged, and it still uses the toDF and write methods to convert the data to a DataFrame and write it to Redshift, respectively.

---

## Conclusion

ChatGPT is a very valuable tool to start with development. Scaffolding is easy. 

It's not perfect (yet), but it improving fast. I will be  using it as an alternative for googling examples.

But if you use it without knowing that you do, you might end up like this guy:
![chatgpt meme](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/y8x8bbpwa1tvvniys822.png)