PROJECT CASE STUDY
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Enterprise Data Lake Migration
Enterprise Data Lake Migration
Architecting a scalable, fault-tolerant data pipeline to handle petabyte-scale streaming data for real-time analytics.
š Q3 2023 - Q1 2024
ā ļøThe Challenge
ā ļøThe Challenge
The client's legacy on-premise data warehouse was struggling to process the daily influx of 50TB of transactional data. Nightly ETL batches were frequently failing, causing downstream delays in critical business intelligence dashboards. The system lacked the elasticity to handle peak loads during end-of-month reporting, leading to severe latency issues and data staleness
šMeasurable Impact
šMeasurable Impact
Reduced data processing latency by 40%, enabling near real-time analytics.
Decreased infrastructure costs by 25% through optimized cloud resource utilization and spot instances.
Achieved 99.99% pipeline uptime, eliminating the backlog of failed nightly jobs.
Scalably ingested and processed >50TB of streaming data daily.
Architecture Overview
Architecture Overview
Engineering Solution
Engineering Solution
Event-Driven Ingestion
Migrated from batch-oriented SFTP drops to an event-driven architecture using Apache Kafka. This decoupled the source systems from the processing layer, buffering spikes in transactional volume and ensuring zero data loss during high-load periods.
Scalable Compute Engine
Implemented Spark Streaming on an ephemeral EMR cluster. The cluster auto-scales based on Kafka lag metrics, spinning up compu resources only when necessary. This elasticity solved the end-of-month processing bottleneck while optimizing compute costs.
Ā Data Lakehouse Architecture
Transitioned storage to a tiered S3 data lake, utilizing Delta Lake format for ACID transactions. This allowed for concurrent reads and writes, enabling data scientists to query raw data while the ETL pipeline continuously appended new records.
Ā Orchestration & Monitoring
Replaced cron jobs with Apache Airflow for complex dependency management. Integrated comprehensive logging and alerting via Datadog, providing full observability into pipeline health and data quality metrics at every stage.
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