Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics
Keywords:
Cloud-Based Data Integration, Enterprise Analytics Enablement, Cloud-Native Integration Architectures, Managed Cloud Services, Scalable Data Pipelines, Cost And Latency Optimization, Throughput-Oriented Design, Data Preparation Techniques, Multi-Source Data Handling, Heterogeneous Data Formats, Governance And Compliance, Integration Patterns And Principles, Enterprise Data Infrastructure, Analyst And Data Scientist Productivity, Ad-Hoc Pipeline Risks, Point-To-Point Integration, Pipeline Maintainability Challenges, Scalability Constraints, Manual Integration Overhead, Modern Data Platform Foundations.Abstract
Cloud-based data integration is an overlooked area of enterprise infrastructure despite being a crucial enabler of scalable enterprise analytics. The integration patterns, principles, considerations, tools, and techniques relevant to data integration and preparation in cloud-based environments are presented in this article. The emphasis is on cloud-native integration architectures, which take advantage of managed services to eliminate undifferentiated heavy lifting. Such architectures are typically optimised for cost, throughput, and latency rather than for simplicity and ease of management. Attention is also given to scalability and governance concerns.Scalable enterprise analytics relies on cloud data integration implementations that handle data from a multitude of sources and deliver data in a variety of formats, using an eclectic collection of preparation methods. Effective data integration enables modern data analysts and data scientists to focus on analytics. However, cloud data integration architectures represent an area of enterprise infrastructure that has received relatively little attention relative to other areas, such as data analytics and machine learning. Consequently, cloud data integration architectures are often manually constructed, involving an ad-hoc collection of point-to-point data pipelines used for moving data between sources, intermediate sinks, and targets. Although such architectures meet initial needs, they quickly become unwieldy as demand grows, with the overhead of maintaining manually constructed data pipelines reaching a tipping point.
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