Intelligent Metadata-Driven Data Engineering: Accelerating Standardized, Scalable Data Pipelines

Abstract

The swift transition to cloud-native and data-driven domain platforms has proven cruel constraints to the previous, code-intensive ETL pipelines, namely scalability, standardization, and promptness in delivery. To overcome the above issues, the current paper proposes an Intelligent Metadata-Driven Data Engineering Framework that enhances the design, coordination, and implementation of a scalable data pipeline by the implementation of a metadata-first design approach. The proposed pipeline separates the pipeline logic and implementation, where the declarative pipeline configurations form a source of truth in written form, in YAML, and thereby sensible version control, CI/CD integration, and reproducible multi-environment deployment are possible.

The architecture facilitates the dynamic configuration processes that can be used to harmonize heterogeneous source systems that automatically detect incoming entities and relocate data of big sizes into other target systems. These combined data quality (DQ) rule definitions offer the situation of continuously validating the information in motion, and it is done by the column-based constraints, pattern matching, threshold-motivated inspections, and conditional enforcing actions. The records that do not pass the quality validation are automatically transferred to the quarantine status, according to the audit repositories, through automated ServiceNow ticket creation to aid the remedy of activities to be performed.

The implemented configuration modules were initially introduced as an Apache Spark implementation, where the layers of configuration modules are configured as source configuration, data quality specification, transformation and aggregation, target system definition, and runtime execution management. A centralized controller is a dynamic metadata reader that creates and executes Spark jobs that can handle an extensive variety of data types, including Parquet, CSV, Excel, ORC, and JSON. Multi-target sinks and reusable templates of transformation make it possible to perform effective batch as well as incremental processing in the same pipeline execution model.

As has been shown in the experience observed in the analysis of modernization projects in large business data, great gains have been achieved regarding the efficiency and reliability of the engineering data. The enforcement of the rules through the automation enabled improving the work on the pipeline development by 65%-75%, the stability of the execution grew to a significant extent, and the compliance with the data quality was achieved by 90 percent or more on a regular basis. Besides this, the metadata architecture enhances the clarity of the operations, reduces the mistakes in manufacturing, and reinforces the entire data management at the enterprise level.

Finally, the given framework will also offer a solid base of scalable ETL automation, cloud-native data modernization, and AI-ready data platforms. It supports the high-profile demands of the emerging generation of enterprise data ecosystems with support for automated configuration, self-service pipeline development, and abundant integration patterns.