Mitigating Data Quality and Consistency Challenges in Multi-Source Ingestion through Schema Validation and Transformation Techniques
Author(s):
Varun Garg
Ensuring data quality and consistency across data intake from several sources presents a tremendous challenge for companies running large-scale data platforms. These challenges are exacerbated by the numerous data forms—structured, semi-structured, unstructured—that come from many sources—including relational databases, IoT devices, and APIs. Data variances, incompatible schemas, and quality issues cause poor analytics and downstream decision-making. With an eye toward how schema validation and transformation techniques might assist address problems with data quality and consistency across the pipeline, this paper looks at the primary challenges related to receiving multi-format data. We analyze schema validation systems including Apache Avro, JSON Schema, and Protobuf with real-time transformation techniques applied using Apache Kafka, Apache Spark, and AWS Glue. The limits of these techniques and future directions—such as AI-driven data validation and self-healing data pipelines are also discussed at the end of this work.
