Hafiz Suleman - Big Data Engineer and Data Architect with AWS and Azure | ContraWork by Hafiz Suleman
Hafiz Suleman
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Hafiz Suleman

Big Data Engineer and Data Architect with AWS and Azure

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Hafiz is ready for their next project!

Gujranwala, Pakistan

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Gujranwala, Pakistan

Cover image for Real-Time Analytics Platform on AWS
Real-Time Analytics Platform on AWS designed to ingest, process, and visualize high-volume streaming data from IoT devices and application logs. Ingestion: Device telemetry (IoT Core) and application logs are published into Amazon Kinesis Data Streams, followed by lightweight routing and enrichment using AWS Lambda. Processing & Storage: Amazon EMR performs heavy data transformations via Spark Structured Streaming, landing the curated output into Amazon S3 as partitioned Parquet files. AWS Glue then handles batch transformations before loading the data into Amazon Redshift for the analytics workload. Orchestration & Serving: AWS Step Functions coordinate the batch processing workflows. Finally, Amazon QuickSight generates actionable business dashboards while Amazon CloudWatch monitors operational metrics and alarms
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Cover image for OnPrem .NET to AWS Migration Migrated
OnPrem .NET to AWS Migration Migrated an on‐premises .NET web application and SQL Server database to AWS to achieve scalability, high availability, and cross‐region resilience. The legacy environment ran on Windows servers with a clustered SQL Server, resulting in high maintenance and limited elasticity. The new AWS architecture features EC2 Auto Scaling behind an Application Load Balancer (ALB), Amazon RDS for SQL Server with Multi‐AZ and a cross‐region read replica, and DNS failover via Route 53. ◦ Deployed the .NET application on an EC2 Auto Scaling group (Windows AMI) across private subnets in us‐east‐1. 5- Build Scalable Real-Time Data Analytics on AWS for Instant Insights Designed and implemented a real-time data analytics platform on AWS, enabling stream processing, transformation, and visualization for high-volume IoT and log data. This system was built to process and analyze millions of records per second, providing actionable insights for business decision-making. The solution leveraged AWS native services like Kinesis, Glue, Lambda, and Redshift, integrating Apache Spark Structured Streaming to handle real-time data ingestion and transformation. Airflow and Step Functions were used for workflow automation.
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Cover image for Batch ETL Data Warehouse Modernization Led
Batch ETL Data Warehouse Modernization Led modernization of a legacy batch ETL pipeline into a scalable AWS-based data warehouse. Replaced manual SQL jobs with an automated system loading diverse sources into Amazon Redshift. Ingestion: Daily CSVs and API data land in S3; S3 events trigger Lambda to launch Glue Crawlers and update the Data Catalog. Transformation: Glue PySpark jobs join and cleanse sales, inventory, and customer data, apply quality checks, and move errors to a quarantine path. Output: Partitioned Parquet files stored in S3 for loading into Redshift.
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Cover image for Oracle to Amazon RDS PostgreSQL Designed
Oracle to Amazon RDS PostgreSQL Designed and Implemented a secure, scalable architecture to migrate an on-prem Oracle database to Amazon RDS for PostgreSQL. Data Flow: Modeled end-to-end flow, used AWS Glue (SCT) for schema conversion, and Kinesis (DMS) for full-load and CDC. Stored artifacts in S3 and loaded into RDS. Services: Glue for conversion, Kinesis for replication, S3 for artifacts, and RDS as target. Security: IAM roles with least privilege, KMS for encryption at rest. HA & DR: Architected in us-east-1 with Glue, DMS, RDS, S3, and monitoring via CloudWatch.
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