Building Resilient Data Pipelines: Techniques for Fault-Tolerant Data Engineering
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P106Keywords:
Data Pipelines, Fault Tolerance, Observability, Idempotency, Immutability, Data Validation, Self-Healing ArchitecturesAbstract
Data pipelines are pivotal to organizations consuming large volumes of information, especially in the current data-focused prognosis. These pipelines must be reliable and robust because any breakdown in these results may result in late analysis, slower business operations or even serious business hitches. There are some main difficulties with data pipelines, such as data corruption, the change of data schema, failures in the transportation network, and limited capabilities of the computer hardware, thus making the question of fault tolerance an essential element in designing the pipeline. Through fault tolerance, it is thus very easy to design systems so that data can pass through without many hitches in the process. So, a highly tolerant data pipeline should be capable of identifying and self-correcting mistakes while preserving data reliability and reducing the time necessary for error correction. To this end, the now common data engineering practices of redundancy retries or retry-on-failure and check pointing are used to work around the problem. This paper presents an in-depth review of enabling solution approaches to construct robust data pipelines. It discusses the first approach, which consists of numerous components, including observability, structured computation, data validation, and self-healing systems. Real-time alerting emanates from observability, enabling engineers to prevent issues from worsening before they are detected. Idempotent and immutable characteristics provide a structure which guarantees the success of data operations even when there are attempts for repetition. Able validation systems ensure incorrect or structured data are not blurred in the pipeline to affect analytics or decision-making. Finally, self-healing architectures make it easier for systems to recover from failures since they can reallocate resources or reroute information without human intervention. This paper also provides real-life examples of the actualization of these techniques through case studies and empirical findings to justify that modern data pipelines are not only reactive but can be proactive and even self-healing, and most importantly, they can grow and expand with the Data-ning empire
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