diff --git "a/Descritivo da Solu\303\247\303\243o.pdf" "b/Descritivo da Solu\303\247\303\243o.pdf"
new file mode 100644
index 0000000..12eea8c
Binary files /dev/null and "b/Descritivo da Solu\303\247\303\243o.pdf" differ
diff --git "a/dags/dag - solu\303\247\303\243o 1.py" "b/dags/dag - solu\303\247\303\243o 1.py"
new file mode 100644
index 0000000..ec47495
--- /dev/null
+++ "b/dags/dag - solu\303\247\303\243o 1.py"	
@@ -0,0 +1,219 @@
+# --------------------------------------------------------------
+# DAG that orchestrates the execution of the Python script that
+# insert the US census bureau data into the Postgres databse
+# --------------------------------------------------------------
+
+
+import io
+import json
+import boto3
+import psycopg2
+import pandas as pd
+import pyarrow as pa
+from airflow import DAG
+from typing import List
+from io import StringIO
+import pyarrow.parquet as pq
+from datetime import timedelta
+from airflow.utils.dates import days_ago
+from airflow.operators.python_operator import PythonOperator
+
+
+# Contrainsts
+
+file = open('/opt/airflow/dags/constraints.json')
+constraints = json.load(file)
+file.close()
+
+# S3 Client and Resource
+
+s3_client = boto3.client(
+    's3',
+    aws_access_key_id=constraints['aws_access_key_id'],
+    aws_secret_access_key=constraints['aws_secret_access_key']
+)
+
+s3_resource = boto3.resource(
+    service_name='s3',
+    region_name='us-east-1',
+    aws_access_key_id=constraints['aws_access_key_id'],
+    aws_secret_access_key=constraints['aws_secret_access_key']
+)
+
+# Functions
+
+def read_raw_data(s3_client, bucket: str, raw_path: str, refined_path: str, **kwargs):
+    """
+    This function reads the raw datasets from S3 bucket, 
+    apply the needed transformations and adjustments,
+    and write the output dataset to S3 refined folder
+    """
+
+    # Read the raw datasets from S3 raw folder
+
+    datasets = []
+
+    try:
+        result = s3_client.list_objects(Bucket=bucket, Prefix=raw_path)
+        contents = result.get('Contents')
+        for obj in contents:
+            content = s3_client.get_object(Bucket=bucket, Key=obj['Key'])
+            data = content['Body'].read().decode()
+            datasets.append(data)
+
+    except Exception as e:
+        print(e)
+
+    # Apply the needed transformations adjustments
+
+    i = 0
+
+    columns = ['age', 'workclass', 'fnlwgt',
+              'education', 'education-num', 'marital-status',
+              'occupation', 'relationship', 'race',
+              'sex', 'capital-gain', 'capital-loss',
+              'hours-per-week', 'native-country', 'class']
+
+    for dataset in datasets:
+                
+        i = i + 1
+
+        df = pd.read_csv(StringIO(dataset), sep=',', names=columns)
+        df = df.infer_objects()
+        df = df.dropna(how='any')
+    
+        # Adjust the numeric columns
+
+        num_columns = [
+            'age', 'fnlwgt', 'education-num',
+            'capital-gain', 'capital-loss', 'hours-per-week'
+        ]
+
+        for column in num_columns:
+            if df[f'{column}'].dtypes == 'float64' or df[f'{column}'].dtypes == 'int64':
+                pass
+            else:
+                df[f'{column}_check'] = list(map(lambda x: x.isalpha(), df[f'{column}'].str.strip()))
+                df.loc[df[f'{column}_check'] == True, f'{column}'] = 0
+                df.drop(f'{column}_check', axis=1, inplace=True)
+
+        # Adjust the datatypes
+
+        df = df.astype(
+            {
+                "age": int,
+                "fnlwgt": int,
+                "education-num": int,
+                "capital-gain": int,
+                "capital-loss": int,
+                "hours-per-week": int
+            }
+        )
+
+        # Save the dataframe to S3 refined folder
+
+        parquet_table = pa.Table.from_pandas(df)
+        pq.write_table(parquet_table, 'adult')
+
+        s3_client.upload_file('adult', bucket, f'{refined_path}adult{i}.parquet')
+
+def insert_into_database(s3_client, s3_resource, bucket: str, refined_path: str, **kwargs):
+    """
+    This function reads the parquet files from S3 bucket refined folder, 
+    and insert the data into a Postgres database table
+    """
+
+    # Postgres database connection and cursor
+    conn = psycopg2.connect(
+        "dbname='{db}' user='{user}' host='{host}' port='{port}' password='{passwd}'"
+        .format(
+            user='postgres',
+            passwd='Gf2023!@',
+            host='host.docker.internal',
+            port='5432',
+            db='postgres'
+        )
+    )
+
+    cur = conn.cursor()
+
+    # Read the parquet files from the S3 refined folder
+
+    result = s3_client.list_objects(Bucket=bucket, Prefix=refined_path)
+    contents = result.get('Contents')
+
+    dfs = []
+
+    for obj in contents:
+        obj_size = obj['Size']
+        if obj_size == 0:
+            pass
+        else:
+            buffer = io.BytesIO()
+            object = s3_resource.Object(bucket, obj['Key'])
+            object.download_fileobj(buffer)
+            df = pd.read_parquet(buffer)
+            dfs.append(df)
+
+    df = pd.concat(dfs, ignore_index=True)
+
+    # Insert dataframe into Postgres table
+
+    try:
+        truncate_stmt = "TRUNCATE TABLE public.adult"
+        cur.execute(truncate_stmt)
+        output = io.StringIO()
+        df.to_csv(output, sep='\t', header=False, index=False)
+        output.seek(0)
+        contents = output.getvalue()
+        cur.copy_from(output, 'adult', null="")
+        conn.commit()
+    except (Exception, psycopg2.DatabaseError) as error:
+        print("Error: %s" % error)
+        conn.rollback()
+        return 1
+
+DAG
+
+default_args = {
+    'owner': 'Gustavo Franco',
+    'depends_on_past': False,
+    'start_date': days_ago(1),
+    'email': ['gsvfranco@gmail.com'],
+    'email_on_failure': False,
+    'email_on_retry': False,
+    'retries': 1,
+    'retry_delay': timedelta(minutes=5)
+}
+
+with DAG(
+    'desafio_dev_pyton_solucao_1',
+    default_args=default_args,
+    description='Desafio - Dev Python - Solução 1',
+    schedule=timedelta(seconds=60),
+    catchup=False
+) as dag:
+
+    t1 = PythonOperator(
+        task_id='read_raw_data',
+        python_callable=read_raw_data,
+        op_kwargs={
+            's3_client': s3_client,
+            'bucket': constraints['bucket'], 
+            'raw_path': constraints['raw_path'],
+            'refined_path': constraints['refined_path']
+        }
+    )
+
+    t2 = PythonOperator(
+        task_id='insert_into_database',
+        python_callable=insert_into_database,
+        op_kwargs={
+            's3_client': s3_client,
+            's3_resource': s3_resource,
+            'bucket': constraints['bucket'], 
+            'refined_path': constraints['refined_path']
+        }
+    )
+
+    t1 >> t2
diff --git "a/dags/dag - solu\303\247\303\243o 2.py" "b/dags/dag - solu\303\247\303\243o 2.py"
new file mode 100644
index 0000000..24f081e
--- /dev/null
+++ "b/dags/dag - solu\303\247\303\243o 2.py"	
@@ -0,0 +1,239 @@
+# --------------------------------------------------------------
+# DAG that orchestrates the execution of the Python script that
+# insert the US census bureau data into the Postgres databse
+# --------------------------------------------------------------
+
+
+import io
+import time
+import json
+import boto3
+import psycopg2
+import pandas as pd
+import pyarrow as pa
+from airflow import DAG
+from typing import List
+from io import StringIO
+import pyarrow.parquet as pq
+from datetime import timedelta
+from airflow.utils.dates import days_ago
+from airflow.operators.python_operator import PythonOperator
+
+
+# Contrainsts
+
+file = open('/opt/airflow/dags/constraints.json')
+constraints = json.load(file)
+file.close()
+
+# S3 Client and Resource
+
+s3_client = boto3.client(
+    's3',
+    aws_access_key_id=constraints['aws_access_key_id'],
+    aws_secret_access_key=constraints['aws_secret_access_key']
+)
+
+s3_resource = boto3.resource(
+    service_name='s3',
+    region_name='us-east-1',
+    aws_access_key_id=constraints['aws_access_key_id'],
+    aws_secret_access_key=constraints['aws_secret_access_key']
+)
+
+# Functions
+
+def read_raw_data(s3_client, bucket: str, raw_path: str, refined_path: str, **kwargs):
+    """
+    This function reads the raw datasets from S3 bucket, 
+    apply the needed transformations and adjustments,
+    and write the output dataset to S3 refined folder
+    """
+
+    # Read the raw datasets from S3 raw folder
+
+    datasets = []
+
+    try:
+        result = s3_client.list_objects(Bucket=bucket, Prefix=raw_path)
+        contents = result.get('Contents')
+        for obj in contents:
+            content = s3_client.get_object(Bucket=bucket, Key=obj['Key'])
+            data = content['Body'].read().decode()
+            datasets.append(data)
+
+    except Exception as e:
+        print(e)
+
+    # Apply the needed transformations adjustments
+
+    i = 0
+
+    columns = ['age', 'workclass', 'fnlwgt',
+              'education', 'education-num', 'marital-status',
+              'occupation', 'relationship', 'race',
+              'sex', 'capital-gain', 'capital-loss',
+              'hours-per-week', 'native-country', 'class']
+
+    for dataset in datasets:
+                
+        i = i + 1
+
+        df = pd.read_csv(StringIO(dataset), sep=',', names=columns)
+        df = df.infer_objects()
+        df = df.dropna(how='any')
+    
+        # Adjust the numeric columns
+
+        num_columns = [
+            'age', 'fnlwgt', 'education-num',
+            'capital-gain', 'capital-loss', 'hours-per-week'
+        ]
+
+        for column in num_columns:
+            if df[f'{column}'].dtypes == 'float64' or df[f'{column}'].dtypes == 'int64':
+                pass
+            else:
+                df[f'{column}_check'] = list(map(lambda x: x.isalpha(), df[f'{column}'].str.strip()))
+                df.loc[df[f'{column}_check'] == True, f'{column}'] = 0
+                df.drop(f'{column}_check', axis=1, inplace=True)
+
+        # Adjust the datatypes
+
+        df = df.astype(
+            {
+                "age": int,
+                "fnlwgt": int,
+                "education-num": int,
+                "capital-gain": int,
+                "capital-loss": int,
+                "hours-per-week": int
+            }
+        )
+
+        # Save the dataframe to S3 refined folder
+
+        parquet_table = pa.Table.from_pandas(df)
+        pq.write_table(parquet_table, 'adult')
+
+        s3_client.upload_file('adult', bucket, f'{refined_path}adult{i}.parquet')
+
+def insert_into_database(s3_client, s3_resource, bucket: str, refined_path: str, **kwargs):
+    """
+    This function reads the parquet files from S3 bucket refined folder, 
+    and insert the data into a Postgres database table
+    """
+
+    # Postgres database connection and cursor
+    conn = psycopg2.connect(
+        "dbname='{db}' user='{user}' host='{host}' port='{port}' password='{passwd}'"
+        .format(
+            user='postgres',
+            passwd='Gf2023!@',
+            host='host.docker.internal',
+            port='5432',
+            db='postgres'
+        )
+    )
+
+    cur = conn.cursor()
+
+    # Read the parquet files from the S3 refined folder
+
+    result = s3_client.list_objects(Bucket=bucket, Prefix=refined_path)
+    contents = result.get('Contents')
+
+    dfs = []
+
+    for obj in contents:
+        obj_size = obj['Size']
+        if obj_size == 0:
+            pass
+        else:
+            buffer = io.BytesIO()
+            object = s3_resource.Object(bucket, obj['Key'])
+            object.download_fileobj(buffer)
+            df = pd.read_parquet(buffer)
+            dfs.append(df)
+
+    df = pd.concat(dfs, ignore_index=True)
+
+    # Split the dataframe into chunks
+
+    chunks = list()
+    chunk_size = 1630
+    num_chunks = len(df) // chunk_size + 1
+    for i in range(num_chunks):
+        chunks.append(df[i*chunk_size:(i+1)*chunk_size])
+
+    # Insert the chunks into Postgres table
+
+    truncate_stmt = "TRUNCATE TABLE public.adult"
+    cur.execute(truncate_stmt)
+
+    counter = 0
+
+    for chunk in chunks:
+
+        counter += 1
+
+        try:
+            output = io.StringIO()
+            chunk.to_csv(output, sep='\t', header=False, index=False)
+            output.seek(0)
+            contents = output.getvalue()
+            cur.copy_from(output, 'adult', null="")
+            conn.commit()
+            print(f"Chunk {counter} inserted into Postgres table")
+            print("The next chunk will be inserted after 10 seconds")
+            time.sleep(10)
+
+        except (Exception, psycopg2.DatabaseError) as error:
+            print("Error: %s" % error)
+            conn.rollback()
+            return 1
+
+DAG
+
+default_args = {
+    'owner': 'Gustavo Franco',
+    'depends_on_past': False,
+    'start_date': days_ago(1),
+    'email': ['gsvfranco@gmail.com'],
+    'email_on_failure': False,
+    'email_on_retry': False,
+    'retries': 1,
+    'retry_delay': timedelta(minutes=5)
+}
+
+with DAG(
+    'desafio_dev_pyton_solucao_2',
+    default_args=default_args,
+    description='Desafio - Dev Python - Solução 2',
+    schedule=timedelta(minutes=6),
+    catchup=False
+) as dag:
+
+    t1 = PythonOperator(
+        task_id='read_raw_data',
+        python_callable=read_raw_data,
+        op_kwargs={
+            's3_client': s3_client,
+            'bucket': constraints['bucket'], 
+            'raw_path': constraints['raw_path'],
+            'refined_path': constraints['refined_path']
+        }
+    )
+
+    t2 = PythonOperator(
+        task_id='insert_into_database',
+        python_callable=insert_into_database,
+        op_kwargs={
+            's3_client': s3_client,
+            's3_resource': s3_resource,
+            'bucket': constraints['bucket'], 
+            'refined_path': constraints['refined_path']
+        }
+    )
+
+    t1 >> t2
diff --git "a/scripts/Desafio Dev Python - Solu\303\247\303\243o 1.py" "b/scripts/Desafio Dev Python - Solu\303\247\303\243o 1.py"
new file mode 100644
index 0000000..4c5ea31
--- /dev/null
+++ "b/scripts/Desafio Dev Python - Solu\303\247\303\243o 1.py"	
@@ -0,0 +1,188 @@
+# -----------------------------------------------------------
+# Script that transports data from some datasets and to Postgresql table
+#
+# Gustavo Franco
+# email gsvfranco@gmail.com
+# -----------------------------------------------------------
+
+
+import os
+import psycopg2
+import pandas as pd
+from io import StringIO
+from typing import List
+
+
+def read_datasets(path: str, filenames: List[str]):
+    """
+    This function reads the datasets with
+    US Census Bureau data from local file system
+
+    :param path: Path where the datasets files are in local file system
+    :param filenames: Names of the files
+    :return: A list with the datasets appended as strings
+    """
+    try:
+        with open('output_file', 'w') as outfile:
+            for filename in filenames:
+                with open(os.path.join(path, filename)) as infile:
+                    contents = infile.read()
+                    outfile.write(contents)
+            with open('output_file', 'r') as file:
+                dataset = file.read()
+                file.close()
+
+        os.remove('C:/Users/LowCost/PycharmProjects/TOTVS/output_file')
+
+        return dataset
+
+    except Exception as e:
+        print(e)
+
+def create_dataframe(dataset: List[str], columns: List[str]):
+    """
+    This function creates a pandas dataframe
+    with the datasets read from local file system
+
+    :param dataset: List of the datasets read from local file system
+    :param columns: List of the dataframe columns
+    :return: A pandas dataframe of the appended datasets
+    """
+    df = pd.read_csv(StringIO(dataset), sep=',', names=columns)
+    df = df.infer_objects()
+    df = df.dropna(how='any')
+    return df
+
+def adjust_dataframe(df):
+    """
+    This function checks if the numeric columns of the
+    dataframe contain alphanumeric characters, if yes,
+    clean and adjust the inconsistent values, adjusting
+    it to be stored as integers in the database table
+
+    The function adjusts the dataframe columns datatypes as well
+
+    :param df: The dataframe to check/adjust
+    :return: The dataframe adjusted
+    """
+    num_columns = [
+        'age', 'fnlwgt', 'education-num',
+        'capital-gain', 'capital-loss', 'hours-per-week'
+    ]
+
+    for column in num_columns:
+        if df[f'{column}'].dtypes == 'float64' or df[f'{column}'].dtypes == 'int64':
+            pass
+        else:
+            df[f'{column}_check'] = list(map(lambda x: x.isalpha(), df[f'{column}'].str.strip()))
+            df.loc[df[f'{column}_check'] == True, f'{column}'] = 0
+            df.drop(f'{column}_check', axis=1, inplace=True)
+
+    return df
+
+def adjust_dtypes(df):
+    """
+    This function adjusts the numeric columns
+    datatypes, to be correctly stored in the database
+
+    :param df: The dataframe to check/adjust
+    :return: The dataframe with the datatypes of the numeric columns adjusted
+    """
+    df = df.astype(
+        {
+            "age": int,
+            "fnlwgt": int,
+            "education-num": int,
+            "capital-gain": int,
+            "capital-loss": int,
+            "hours-per-week": int
+        }
+    )
+    return df
+
+def insert_into_table(conn, cur, df, table: str):
+    """
+    This function writes the dataframe cleaned and
+    prepared, into a Postgres database table
+
+    :param df: The dataframe to be written
+    :param df: The name of the table
+    """
+    tuples = list(set([tuple(x) for x in df.to_numpy()]))
+    truncate_stmt = "TRUNCATE TABLE {}".format(table)
+    query = "INSERT INTO {} VALUES (" \
+            "%s, %s, %s, %s, %s, %s, %s, %s, " \
+            "%s, %s, %s, %s, %s, %s, %s)".format(table)
+    try:
+        cur.execute(truncate_stmt)
+        cur.executemany(query, tuples)
+        conn.commit()
+    except (Exception, psycopg2.DatabaseError) as error:
+        print("Error: %s" % error)
+        conn.rollback()
+        return 1
+
+def main():
+
+    # Constraints
+
+    path = 'C:/Users/LowCost/Documents/Desafio - Dev Python'
+    filenames = ['Adult.data', 'Adult.test']
+
+    df_columns = ['age', 'workclass', 'fnlwgt',
+                  'education', 'education-num', 'marital-status',
+                  'occupation', 'relationship', 'race',
+                  'sex', 'capital-gain', 'capital-loss',
+                  'hours-per-week', 'native-country', 'class']
+
+    print("1. Creating Postgres connection")
+
+    conn = psycopg2.connect(
+        "dbname='{db}' user='{user}' host='{host}' port='{port}' password='{passwd}'"
+        .format(
+            user='postgres',
+            passwd='Gf2023!@',
+            host='localhost',
+            port='5432',
+            db='postgres'
+        )
+    )
+
+    print(conn)
+    print()
+
+    print("2. Creating Postgres cursor")
+
+    cur = conn.cursor()
+
+    print(cur)
+    print()
+
+    print("3. Reading datasets from file system")
+
+    datasets = read_datasets(path, filenames)
+
+    print("Files: " + str(filenames))
+    print()
+
+    print("4. Creating pandas dataframe")
+
+    df = create_dataframe(datasets, columns=df_columns)
+
+    print("Dataframe rows count: " + str(len(df)))
+    print()
+
+    print("5. Cleaning and adjusting dataframe")
+
+    df = adjust_dataframe(df)
+    df = adjust_dtypes(df)
+
+    print()
+
+    print("6. Inserting dataframe into Postgres database table")
+
+    table = 'public.adult'
+    insert_into_table(conn, cur, df, table)
+
+if __name__ == '__main__':
+    main()
diff --git "a/scripts/Desafio Dev Python - Solu\303\247\303\243o 2.py" "b/scripts/Desafio Dev Python - Solu\303\247\303\243o 2.py"
new file mode 100644
index 0000000..13305c5
--- /dev/null
+++ "b/scripts/Desafio Dev Python - Solu\303\247\303\243o 2.py"	
@@ -0,0 +1,217 @@
+# -----------------------------------------------------------
+# Script that transports data from some datasets and to Postgresql table
+#
+# Gustavo Franco
+# email gsvfranco@gmail.com
+# -----------------------------------------------------------
+
+
+import os
+import time
+import psycopg2
+import pandas as pd
+from io import StringIO
+from typing import List
+
+
+def read_datasets(path: str, filenames: List[str]):
+    """
+    This function reads the datasets with
+    US Census Bureau data from local file system
+
+    :param path: Path where the datasets files are in local file system
+    :param filenames: Names of the files
+    :return: A list with the datasets appended as strings
+    """
+    try:
+        with open('output_file', 'w') as outfile:
+            for filename in filenames:
+                with open(os.path.join(path, filename)) as infile:
+                    contents = infile.read()
+                    outfile.write(contents)
+            with open('output_file', 'r') as file:
+                dataset = file.read()
+                file.close()
+
+        os.remove('C:/Users/LowCost/PycharmProjects/TOTVS/output_file')
+
+        return dataset
+
+    except Exception as e:
+        print(e)
+
+def create_dataframe(dataset: List[str], columns: List[str]):
+    """
+    This function creates a pandas dataframe
+    with the datasets read from local file system
+
+    :param dataset: List of the datasets read from local file system
+    :param columns: List of the dataframe columns
+    :return: A pandas dataframe of the appended datasets
+    """
+    df = pd.read_csv(StringIO(dataset), sep=',', names=columns)
+    df = df.infer_objects()
+    df = df.dropna(how='any')
+    return df
+
+def split_dataframe(df, chunk_size: int):
+    """
+    This function splits the pandas dataframe into chunks of 1630 size
+
+    :param df: The dataframe to split
+    :param chunk_size: The size of the chunks
+    :return: A list with the chunks splitted
+    """
+    chunks = list()
+    num_chunks = len(df) // chunk_size + 1
+    for i in range(num_chunks):
+        chunks.append(df[i*chunk_size:(i+1)*chunk_size])
+    return chunks
+
+def adjust_dataframe(df):
+    """
+    This function checks if the numeric columns of the
+    dataframe contain alphanumeric characters, cleaning
+    and adjusting these inconsistent values, to allow
+    it to be stored as integers in the database
+
+    :param df: The dataframe to check/adjust
+    :return: The dataframe adjusted
+    """
+    num_columns = [
+        'age', 'fnlwgt', 'education-num',
+        'capital-gain', 'capital-loss', 'hours-per-week'
+    ]
+
+    for column in num_columns:
+        if df[f'{column}'].dtypes == 'float64' or df[f'{column}'].dtypes == 'int64':
+            pass
+        else:
+            df[f'{column}_check'] = list(map(lambda x: x.isalpha(), df[f'{column}'].str.strip()))
+            df.loc[df[f'{column}_check'] == True, f'{column}'] = 0
+            df.drop(f'{column}_check', axis=1, inplace=True)
+
+    return df
+
+def adjust_dtypes(df):
+    """
+    This function adjusts the numeric columns
+    datatypes, to be correctly stored in the database
+
+    :param df: The dataframe to check/adjust
+    :return: The dataframe with the datatypes of the numeric columns adjusted
+    """
+    df = df.astype(
+        {
+            "age": int,
+            "fnlwgt": int,
+            "education-num": int,
+            "capital-gain": int,
+            "capital-loss": int,
+            "hours-per-week": int
+        }
+    )
+    return df
+
+def insert_into_table(conn, cur, chunks, table: str):
+    """
+    This function writes the dataframe cleaned and
+    prepared, into a Postgres database table
+
+    :param df: The dataframe to be written
+    :param df: The name of the table
+    """
+    counter = 0
+    truncate_stmt = "TRUNCATE TABLE {}".format(table)
+    cur.execute(truncate_stmt)
+    conn.commit()
+    for chunk in chunks:
+        counter += 1
+        tuples = list(set([tuple(x) for x in chunk.to_numpy()]))
+        query = "INSERT INTO {} VALUES (" \
+                "%s, %s, %s, %s, %s, %s, %s, %s, " \
+                "%s, %s, %s, %s, %s, %s, %s)".format(table)
+        try:
+            cur.executemany(query, tuples)
+            conn.commit()
+            print(f"Chunk {counter} inserted into Postgres table")
+            print("The next chunk will be inserted after 10 seconds")
+            time.sleep(10)
+        except (Exception, psycopg2.DatabaseError) as error:
+            print("Error: %s" % error)
+            conn.rollback()
+            return 1
+
+def main():
+
+    # Constraints
+
+    path = 'C:/Users/LowCost/Documents/Desafio - Dev Python'
+    filenames = ['Adult.data', 'Adult.test']
+
+    df_columns = ['age', 'workclass', 'fnlwgt',
+                  'education', 'education-num', 'marital-status',
+                  'occupation', 'relationship', 'race',
+                  'sex', 'capital-gain', 'capital-loss',
+                  'hours-per-week', 'native-country', 'class']
+
+    print("1. Creating Postgres connection")
+
+    conn = psycopg2.connect(
+        "dbname='{db}' user='{user}' host='{host}' port='{port}' password='{passwd}'"
+        .format(
+            user='postgres',
+            passwd='Gf2023!@',
+            host='localhost',
+            port='5432',
+            db='postgres'
+        )
+    )
+
+    print(conn)
+    print()
+
+    print("2. Creating Postgres cursor")
+
+    cur = conn.cursor()
+
+    print(cur)
+    print()
+
+    print("3. Reading datasets from file system")
+
+    datasets = read_datasets(path, filenames)
+
+    print("Files: " + str(filenames))
+    print()
+
+    print("4. Creating pandas dataframe")
+
+    df = create_dataframe(datasets, columns=df_columns)
+
+    print("Dataframe rows count: " + str(len(df)))
+    print()
+
+    print("5. Cleaning and adjusting dataframe")
+
+    df = adjust_dataframe(df)
+    df = adjust_dtypes(df)
+
+    print()
+
+    print("6. Splitting pandas dataframe into chunks")
+
+    chunk_size = 1630
+    chunks = split_dataframe(df, chunk_size)
+
+    print(str(len(chunks)) + f" chunks of size {chunk_size} created")
+    print()
+
+    print("6. Inserting chunks into Postgres database table")
+
+    table = 'adult'
+    insert_into_table(conn, cur, chunks, table)
+
+if __name__ == '__main__':
+    main()
+