diff --git a/.github/workflows/docker-build-push.yml b/.github/workflows/docker-build-push.yml
index 3544a68..87b7af1 100644
--- a/.github/workflows/docker-build-push.yml
+++ b/.github/workflows/docker-build-push.yml
@@ -4,6 +4,9 @@ on:
   push:
     branches:
       - '**'
+    paths:
+      - 'src/**'
+      - 'Docker/**'
   workflow_dispatch:
 
 jobs:
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..de045ba
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.env
+.mypy_cache
\ No newline at end of file
diff --git a/Docker/Dockerfile b/Docker/Dockerfile
index 5d94ed2..3206e08 100644
--- a/Docker/Dockerfile
+++ b/Docker/Dockerfile
@@ -32,13 +32,13 @@ RUN ln -sf ${PREFIX}/bin/python3.11 /usr/local/bin/python \
 ###########################################
 # Stage 2: Get entrypoint from official Spark
 ###########################################
-FROM apache/spark:3.5.6 AS spark-official
+FROM apache/spark:3.5.7 AS spark-official
 
 ###########################################
 # Stage 3: Spark + Delta + Cloud connectors
 ###########################################
 FROM ubuntu:22.04 AS spark-base
-ARG SPARK_VERSION=3.5.6
+ARG SPARK_VERSION=3.5.7
 ARG HADOOP_VERSION=3
 ARG DELTA_VERSION=3.2.1
 ENV DEBIAN_FRONTEND=noninteractive
diff --git a/src/bronze/get_full_tables.py b/src/bronze/get_full_tables.py
new file mode 100644
index 0000000..a2745e9
--- /dev/null
+++ b/src/bronze/get_full_tables.py
@@ -0,0 +1,104 @@
+from pyspark.sql import SparkSession #type:ignore
+import pyspark.sql.functions as F #type:ignore
+import argparse
+import os
+import logging
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+def get_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--savepath", type = str,required= True, help = "The S3 bucket intended for the data to be stored")
+    parser.add_argument("--undesired_column", type = str,required= True, help = " the undesired column for a table")
+    args = parser.parse_args()
+    return args
+
+def get_spark_session(S3_ACCESS_KEY: str,S3_SECRET_KEY: str , S3_ENDPOINT: str) -> SparkSession:
+    
+    spark = SparkSession.builder \
+        .appName("incremental_table_ingestion") \
+        .config("spark.jars", "/opt/spark/jars/hadoop-aws-3.3.4.jar,/opt/spark/jars/aws-java-sdk-bundle-1.12.375.jar,/opt/spark/jars/delta-spark_2.12-3.2.1.jar,/opt/spark/jars/delta-storage-3.2.1.jar,/opt/spark/jars/delta-kernel-api-3.2.1.jar,/opt/spark/jars/mysql-connector-j-8.3.0.jar") \
+        .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")\
+        .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")\
+        .config("spark.hadoop.fs.s3a.access.key", S3_ACCESS_KEY) \
+        .config("spark.hadoop.fs.s3a.secret.key", S3_SECRET_KEY) \
+        .config("spark.hadoop.fs.s3a.endpoint", S3_ENDPOINT) \
+        .config("spark.hadoop.fs.s3a.path.style.access", "true") \
+        .config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem") \
+        .getOrCreate()
+    return spark 
+
+###################################################################################
+#                           GET MYSQL CREDENTIALS                                 #
+###################################################################################
+def main() -> None:
+    MYSQL_DATABASE = os.getenv("MYSQL_DATABASE")
+    MYSQL_HOST = os.getenv("MYSQL_HOST")
+    MYSQL_PORT = os.getenv("MYSQL_PORT")
+    MYSQL_USER = os.getenv("MYSQL_USER")
+    MYSQL_SECRET = os.getenv("MYSQL_SECRET")
+    jdbc_url = f"jdbc:mysql://{MYSQL_HOST}:{MYSQL_PORT}/{MYSQL_DATABASE}"
+
+
+
+    ###################################################################################
+    #                           GET S3 CREDENTIALS                                    #
+    ###################################################################################
+    S3_ACCESS_KEY = str(os.getenv("S3_ACCESS_KEY"))
+    S3_SECRET_KEY = str(os.getenv("S3_SECRET_KEY"))
+    S3_ENDPOINT = str(os.getenv("S3_ENDPOINT"))
+
+    args = get_args()
+    S3_SAVEPATH = args.savepath
+    undesired_column = args.undesired_column
+
+    TABLES = [
+    "course_overviews_courseoverview", 
+    "student_courseenrollment", 
+    "certificates_generatedcertificate",
+    "student_courseaccessrole",
+    "auth_userprofile",
+    "student_userattribute",
+    "organizations_organization",
+    "auth_user"
+    ]
+
+    for table in TABLES:
+
+        logging.info(f"getting table {table}")
+        try:
+        
+            spark = get_spark_session(S3_ACCESS_KEY=S3_ACCESS_KEY,S3_SECRET_KEY=S3_SECRET_KEY,S3_ENDPOINT=S3_ENDPOINT)
+
+            df = spark.read.format("jdbc") \
+                .option("url", jdbc_url) \
+                .option("user", MYSQL_USER) \
+                .option("password", MYSQL_SECRET) \
+                .option("driver", "com.mysql.cj.jdbc.Driver") \
+                .option("dbtable", table) \
+                .load()
+            if table == "auth_user":
+                df = df.drop(undesired_column)
+
+            df = df.withColumn("ingestion_date", F.current_timestamp()) \
+                   .withColumn("source_name", F.lit(table))
+            if table == "auth_user" and undesired_column and undesired_column in df.columns:
+                raise Exception("THE undesired column stills in the dataframe")
+            output_path = f"{S3_SAVEPATH}/{table}"
+
+            df.write.format("delta").mode("append").save(output_path)
+
+            logging.info(f"Data saved as Delta table to {output_path}")
+
+        except Exception as e:
+            logging.error(f"Pipeline failed: {e}")
+    spark.stop()
+
+
+if __name__=="__main__":
+    main()
\ No newline at end of file
diff --git a/src/bronze/incremental_load.py b/src/bronze/incremental_load.py
new file mode 100644
index 0000000..d472c95
--- /dev/null
+++ b/src/bronze/incremental_load.py
@@ -0,0 +1,198 @@
+from datetime import datetime
+from pyspark.sql import SparkSession # type: ignore
+import pyspark.sql.functions as F # type: ignore
+import pyspark.sql.types as T # type: ignore
+import argparse
+import logging
+import os
+from typing import List, Union, Optional,Tuple
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+
+
+
+
+def get_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--savepath", type = str,required= True, help = "The S3 bucket intended for the data to be stored")
+    parser.add_argument("--metadatapath", type = str, required = True, help ="The S3 bucket that contains stores the metada for the process")
+    parser.add_argument("--table", type = str, required = True, help ="The S3 bucket that contains stores the metada for the process")
+    parser.add_argument("--first_ingestion_flag",type = int,default=0,help="flag to indicate if it is the first ingestion on regular ingestion")
+    args = parser.parse_args()
+    return args
+
+def update_metadata(metadatapath: str ,spark: SparkSession,table:str,last_date:str) -> bool:
+    try:
+        tmp_df = spark.createDataFrame(
+        [
+            (table, last_date)
+        ],
+        T.StructType([T.StructField("table_name", T.StringType(), True),T.StructField("last_date", T.StringType(), True),]),)
+
+        tmp_df.coalesce(1).write.format("csv").mode("overwrite").save(f"{metadatapath}/{table}/last_updated_date")
+        return True 
+    except Exception as e:
+        logging.error(f"Exception {e}")
+        return False
+    
+
+
+def get_spark_session(S3_ACCESS_KEY: str,S3_SECRET_KEY: str , S3_ENDPOINT: str) -> SparkSession:
+    
+    spark = SparkSession.builder \
+        .appName("incremental_table_ingestion") \
+        .config("spark.jars", "/opt/spark/jars/hadoop-aws-3.3.4.jar,/opt/spark/jars/aws-java-sdk-bundle-1.12.375.jar,/opt/spark/jars/delta-spark_2.12-3.2.1.jar,/opt/spark/jars/delta-storage-3.2.1.jar,/opt/spark/jars/delta-kernel-api-3.2.1.jar,/opt/spark/jars/mysql-connector-j-8.3.0.jar") \
+        .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension")\
+        .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog")\
+        .config("spark.hadoop.fs.s3a.access.key", S3_ACCESS_KEY) \
+        .config("spark.hadoop.fs.s3a.secret.key", S3_SECRET_KEY) \
+        .config("spark.hadoop.fs.s3a.endpoint", S3_ENDPOINT) \
+        .config("spark.hadoop.fs.s3a.path.style.access", "true") \
+        .config("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem") \
+        .getOrCreate()
+    return spark 
+
+
+
+
+
+
+
+
+
+def full_initial_ingestion(spark: SparkSession, table: str, savepath: str, jdbc_url:str, MYSQL_USER:str, MYSQL_SECRET:str) -> Tuple[bool, str]:
+    years = [i for i in range(2019,2100)]
+    months = [i for i in range(1,13)]
+    current_year = datetime.now().year
+    current_month = datetime.now().month
+    path = f"{savepath}/{table}"
+    
+    for year in years:
+        for month in months:
+            
+            if (year == 2019 and month ==1):
+                query = f"(SELECT * FROM {table} WHERE YEAR(created) = {year} AND MONTH(created) = {month}) AS limited_table"
+                logging.info(query)
+                df = spark.read.format("jdbc") \
+                    .option("url", jdbc_url) \
+                    .option("user", MYSQL_USER) \
+                    .option("password", MYSQL_SECRET) \
+                    .option("driver", "com.mysql.cj.jdbc.Driver") \
+                    .option("dbtable", query) \
+                    .load() 
+
+                df = df.withColumn("created", F.date_format("created", "yyyy-MM-dd'T'HH:mm:ss.SSSSSS")) \
+                       .withColumn("year", F.year(F.col("created"))) \
+                       .withColumn("month", F.month(F.col("created")))
+                
+                df = df.withColumn("ingestion_date", F.current_timestamp()) \
+                   .withColumn("source_name", F.lit(table))
+                
+                df.write.format("delta") \
+                    .mode("overwrite") \
+                    .partitionBy("year", "month") \
+                    .save(path)
+                continue
+            
+            if (year > current_year) or (year == current_year and month > current_month):
+                break 
+            else:
+                
+                query = f"(SELECT * FROM {table} WHERE YEAR(created) = {year} AND MONTH(created) = {month}) AS limited_table"
+                logging.info(query)
+                new_df = spark.read.format("jdbc") \
+                    .option("url", jdbc_url) \
+                    .option("user", MYSQL_USER) \
+                    .option("password", MYSQL_SECRET) \
+                    .option("driver", "com.mysql.cj.jdbc.Driver") \
+                    .option("dbtable", query) \
+                    .load()
+                if (year == current_year and month == current_month):
+                    last_update =  datetime.now().isoformat()
+                incremental_df = new_df.withColumn("created", F.date_format("created", "yyyy-MM-dd'T'HH:mm:ss.SSSSSS")) \
+                                       .withColumn("year", F.year(F.col("created"))) \
+                                       .withColumn("month", F.month(F.col("created")))
+                
+                incremental_df = incremental_df.withColumn("ingestion_date", F.current_timestamp()).withColumn("source_name", F.lit(table))
+
+                # Append new partitions directly
+                incremental_df.write.format("delta").mode("append").partitionBy("year", "month").save(path)
+    return (True, last_update)
+
+def get_metadata(metadatapath: str, spark: SparkSession, table:str) -> str | bool:
+    metadatapath = f"{metadatapath}/{table}/last_updated_date"
+    customSchema = T.StructType([      
+        T.StructField("table_name", T.StringType(), True),
+        T.StructField("last_date", T.StringType(), True)
+    ])
+    row = spark.read.csv(metadatapath,schema=customSchema).filter( F.col("table_name") == table).first()
+    if row is None or row["last_date"] is None:
+        return False
+    return str(row["last_date"])
+
+def delta_load(spark: SparkSession, jdbc_url:str, MYSQL_USER:str, MYSQL_SECRET:str,last_updated:str,table:str,savepath: str) -> Tuple[bool, str]:
+    path = f"{savepath}/{table}"
+    query = f"(SELECT * FROM {table} WHERE created >= '{last_updated}') AS limited_table"
+    logging.info(query)            
+    new_df = spark.read.format("jdbc") \
+        .option("url", jdbc_url) \
+        .option("user", MYSQL_USER) \
+        .option("password", MYSQL_SECRET) \
+        .option("driver", "com.mysql.cj.jdbc.Driver") \
+        .option("dbtable", query) \
+        .load()
+    last_update =  datetime.now().isoformat()
+    incremental_df = new_df.withColumn("created", F.date_format("created", "yyyy-MM-dd'T'HH:mm:ss.SSSSSS")) \
+                           .withColumn("year", F.year(F.col("created"))) \
+                           .withColumn("month", F.month(F.col("created")))
+    
+    incremental_df = incremental_df.withColumn("ingestion_date", F.current_timestamp()).withColumn("source_name", F.lit(table))
+
+                # Append new partitions directly
+    incremental_df.write.format("delta").mode("append").partitionBy("year", "month").save(path)    
+    
+    return (True,last_update)
+
+
+def main() -> None:
+
+    MYSQL_DATABASE = os.getenv("MYSQL_DATABASE")
+    MYSQL_HOST = os.getenv("MYSQL_HOST")
+    MYSQL_PORT = os.getenv("MYSQL_PORT")
+    MYSQL_USER = str(os.getenv("MYSQL_USER"))
+    MYSQL_SECRET = str(os.getenv("MYSQL_SECRET"))
+    jdbc_url = f"jdbc:mysql://{MYSQL_HOST}:{MYSQL_PORT}/{MYSQL_DATABASE}"
+
+    S3_ACCESS_KEY = str(os.getenv("S3_ACCESS_KEY"))
+    S3_SECRET_KEY = str(os.getenv("S3_SECRET_KEY"))
+    S3_ENDPOINT = str(os.getenv("S3_ENDPOINT"))
+
+    args = get_args()
+    savepath = args.savepath
+    metadata = args.metadatapath
+    is_full_ingestion_flag = args.first_ingestion_flag
+    table = args.table
+    spark = get_spark_session(S3_ACCESS_KEY,S3_SECRET_KEY,S3_ENDPOINT)
+    if is_full_ingestion_flag == 1:
+        result = full_initial_ingestion(spark,table,savepath,jdbc_url,MYSQL_USER,MYSQL_SECRET)
+        logging.info(result)
+        if result[0]:
+            update_metadata(metadatapath=metadata,spark=spark,table=table,last_date=result[1])
+    if is_full_ingestion_flag == 0:
+        last_date = get_metadata(metadatapath=metadata,spark=spark,table=table)
+        if last_date == False:
+            raise Exception("No date Found")
+        last_date = str(last_date)
+        result = delta_load(spark=spark,jdbc_url=jdbc_url,MYSQL_USER=MYSQL_USER,MYSQL_SECRET=MYSQL_SECRET,last_updated=last_date,table=table,savepath=savepath)
+        if result[0]:
+             update_metadata(metadatapath=metadata,spark=spark,table=table,last_date=result[1])
+    spark.stop()
+
+if __name__=="__main__":
+    main()
\ No newline at end of file
diff --git a/src/tmp.txt b/src/tmp.txt
deleted file mode 100644
index 1348a35..0000000
--- a/src/tmp.txt
+++ /dev/null
@@ -1 +0,0 @@
-temp file
\ No newline at end of file