Build a cloud-native serverless data analytics platform on Amazon Web Services: ingesting raw logs into S3, cataloging with AWS Glue Crawlers, querying ad-hoc with Athena, and loading into Amazon Redshift.
Amazon Web Services (AWS) powers over 40% of the cloud data pipelines deployed across Indian IT leaders and global capability centers. Understanding how core AWS analytics services—S3, AWS Glue, Amazon Athena, and Amazon Redshift—fit together is the most direct route to landing a Cloud Data Engineer position. In this project blueprint, we construct a production-ready serverless analytics warehouse.
Amazon S3 (Data Lake Storage): Multi-tier bucket architecture organizing data into /raw, /cleansed, and /curated folders with year/month/day hive partitioning.
AWS Glue Data Catalog & Crawlers: Automatically inspects raw JSON/CSV files in S3, infers schemas, detects schema evolution, and registers tables in the metadata catalog.
Amazon Athena: Serverless Presto/Trino SQL query engine that executes queries directly on S3 Parquet files with zero provisioned infrastructure.
Amazon Redshift Serverless: High-performance columnar cloud data warehouse for low-latency BI dashboards and executive queries.
Here is the AWS Glue PySpark script converting raw JSON logs into partitioned Snappy Parquet tables registered with the Glue Data Catalog:
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.sql.functions import col, year, month, dayofmonth
# Initialize Glue Context
args = getResolvedOptions(sys.argv, ['JOB_NAME'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)
# Read from Raw S3 Bucket using Glue DynamicFrame
datasource = glueContext.create_dynamic_frame.from_options(
connection_type="s3",
connection_options={"paths": ["s3://datadrix-enterprise-lake/raw/web_logs/"]},
format="json"
)
# Convert to PySpark DataFrame for transformations
df = datasource.toDF()
# Clean & Partition by Date
clean_df = df.filter(col("user_id").isNotNull()) \
.withColumn("year", year(col("timestamp"))) \
.withColumn("month", month(col("timestamp"))) \
.withColumn("day", dayofmonth(col("timestamp")))
# Write to Curated S3 Bucket in Snappy Parquet format
clean_df.write \
.partitionBy("year", "month", "day") \
.mode("append") \
.parquet("s3://datadrix-enterprise-lake/curated/web_logs/")
job.commit()
print("Glue ETL Job Completed Successfully!")Once cataloged, data analysts can instantly query terabytes of S3 logs in Athena using standard ANSI SQL without spinning up servers:
Query performance tip: Partitioning by Year/Month/Day reduces Athena scanned data volume by over 90%, slashing AWS costs directly.
Redshift Spectrum enables querying S3 tables directly from within your Redshift cluster without loading data onto Redshift local disks.
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