Data

[yogeshpm273]

Data Engineering Case Study: AdvertiseX
Introduction
As a data engineer at AdvertiseX, I am tasked with addressing challenges related to managing data generated by ad impressions, clicks, conversions, and bid requests. The goal is to design a robust data engineering solution that can handle various data formats, ensure scalability, process data efficiently, store it appropriately, and monitor for data anomalies.

Solution Overview

1. Data Ingestion
   Apache Kafka:
   Implement Apache Kafka for scalable and real-time data ingestion.
   Create Kafka topics for ad impressions (JSON), clicks/conversions (CSV), and bid requests (Avro).
   Producers for each data source will publish data to the respective Kafka topics.
2. Data Processing
   Apache Flink:
   Utilize Apache Flink for real-time stream processing and batch processing.
   Develop Flink jobs to standardize, enrich, validate, filter, and deduplicate incoming data.
   Implement logic to correlate ad impressions with clicks and conversions for meaningful insights.
3. Data Storage and Query Performance
   Apache Hadoop (HDFS) and Apache Hive:
   Store processed data efficiently using Hadoop Distributed File System (HDFS).
   Use Hive for schema-on-read to enable fast querying for campaign performance analysis.
   Partition data by relevant attributes (e.g., date, ad campaign) to optimize query performance.
4. Error Handling and Monitoring
   Apache Kafka Streams and Prometheus/Grafana:
   Implement Kafka Streams for real-time anomaly detection during data ingestion.
   Use Prometheus and Grafana for monitoring and alerting on data quality issues.
   Set up alerts for discrepancies or delays, triggering immediate corrective actions.
   Assumptions and Considerations
   Scalability:

Assumes the need for a scalable solution due to high data volumes.
Can horizontally scale Kafka and Flink based on demand.
Data Validation:

Implement thorough data validation checks during processing to ensure data integrity.
Correlation Logic:

Define a correlation key to link ad impressions with clicks and conversions.
Storage Optimization:

Optimize storage based on the query patterns, partitioning, and indexing.
Conclusion
This proposed solution leverages Apache Kafka, Flink, Hadoop, and Hive to address the data engineering challenges presented by AdvertiseX. It provides a scalable, real-time, and batch-capable system for processing, storing, and analyzing digital advertising data effectively. The chosen technologies align with industry best practices and enable efficient handling of diverse data formats in the ad tech domain.