sachinndhimann / pesto_tech_assignment

Data Engineering Solution for AdvertiseX

Data Ingestion:

To handle the influx of data in various formats and high volumes, we'll set up a robust data ingestion system using Apache Kafka. Kafka's design allows for real-time data streaming and can manage both batch and real-time data processing seamlessly.

1. Organizing Data: We'll create distinct Kafka topics for ad impressions (JSON), clicks/conversions (CSV), and bid requests (Avro) to keep the incoming data streams organized and manageable.

2. Data Input: Using Kafka Connect connectors, we'll directly bring in data from different sources into their respective Kafka topics. For JSON and CSV data, we'll utilize the FileStreamSource connector, while a custom Kafka Connect connector will be developed to handle the semi-structured Avro data.

3. Scalability: Deploying Kafka clusters with ample resources ensures they can handle the high volume of data. By employing partitioning and replication strategies, we'll distribute the workload evenly and ensure the system remains resilient to failures.

Data Processing:

1. Transformation: Apache Spark or Apache Flink jobs will be crafted to process and transform the data. Leveraging Spark's DataFrame API or Flink's DataStream API allows for efficient data manipulation.

2. Standardization and Enrichment: We'll standardize the data schema across all sources to simplify integration. Additionally, we'll enrich the data by linking ad impressions with clicks/conversions using user IDs and timestamps.

3. Validation and Filtering: Implementing validation rules helps identify and discard faulty or incomplete data. Filters will be applied to remove duplicate records and maintain data consistency.

Data Storage and Query Performance:

1. Storage Solution: Opting for Apache Hadoop Distributed File System (HDFS) or Apache Parquet ensures efficient storage of processed data. These solutions offer scalability and fault tolerance, essential for handling large datasets.

2. Columnar Storage: Utilizing columnar storage formats like Parquet optimizes both storage and query performance. This format enhances compression and enables quick retrieval of column-wise data for analytical queries.

3. Data Partitioning: Partitioning the data based on relevant attributes, such as timestamp or campaign ID, enhances query performance. Partition pruning techniques help reduce the amount of data scanned during query execution.

Error Handling and Monitoring:

1. Anomaly Detection: Implementing algorithms for anomaly detection helps spot irregular patterns or outliers in the data. We'll utilize statistical methods or machine learning models to detect anomalies in real-time.

2. Monitoring: Setting up monitoring tools such as Prometheus and Grafana allows us to keep an eye on Kafka clusters, Spark/Flink jobs, and data storage systems. Monitoring metrics like throughput, latency, and resource usage ensures the system remains healthy.

3. Alerting: Configuring alerting mechanisms using Apache ZooKeeper or custom scripts ensures stakeholders are promptly notified of any data quality issues or processing delays. Automated remediation workflows will be put in place to address issues swiftly.

By implementing this data engineering solution, AdvertiseX can efficiently handle diverse data sources, gain valuable insights into ad campaign performance, and make informed decisions to optimize advertising strategies in real-time. Continuous monitoring and proactive error handling guarantee data reliability and quality, empowering AdvertiseX to stay competitive in the digital advertising landscape.