Data Integration Strategies in Cloud-Based ETL Systems

Abstract

To guarantee the correctness, consistency, and completeness of data throughout the ETL process, ETL (Extract, Transform, and Load) testing is a crucial step. An outline of the ETL testing procedure is given in this article, along with an examination of many ETL testing methods, such as regression testing, performance, user acceptability, transformation, correctness, integrity, and completeness of data. Technologies for data streaming and reverse extract, transform, load (Reverse ETL) provide ways to close the gap between analytics tools and operational systems, enabling quicker and more intelligent decision-making. The transfer of data from centralized data warehousing back into functional systems, such as marketing platforms, ERP systems, and CRM (client relationship management) tools, is made easier by reverse ETL. This article's goal is to present how IT staff members use various cloud technologies to create data solutions. The objectives are based on two study questions: which are the main challenges in data collection and which technologies and solutions are most popular among employees? This study explores the fundamental data engineering methods that provide strong business intelligence capabilities. Three key aspects are highlighted: data integration, real-time reporting, extract, transform, and load (ETL). The foundation of BI data preparation is the ETL procedure. We look at a number of ETL approaches, including as micro batching, incremental loading, and conventional batch processing. The study examines the advantages and disadvantages of each strategy, taking into account variables including resource restrictions, latency requirements, and data volume. We also explore data transformation methods, including schema development, data cleansing, and normalization. The study examines sophisticated methods for managing intricate structures of data and semi-structured/unstructured data sources in addition to conventional ETL. We go over the function of data lakes and warehousing in BI design, evaluating how well-suited they are for various data access and storage requirements. As an alternative to the conventional ETL technique, the article also explores the idea of Extract, Load, Transform (ELT), pointing out both its possible advantages and disadvantages in certain situations.