Xplenty explains the basics of Data Integration: What is ETL (Extract Transform Load), what are its main functions and when you would need them.
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Which BI architecture is better for your organization, ETL or ELT? This article explains the considerations and also how they relate to Data Warehouses and Data Lakes.
Data warehouses (DWHs) are widely recognized as essential components of business intelligence and analytics operations. But the question of whether the optimal deployment route is in the cloud or on-premise remains hotly debated.
Like so many things, the truth is that there is no one-size-fits-all solution. Every business is different, and there are advantages and disadvantages in both approaches. On the one hand the cloud offers scalability and low entry cost advantages. On the other, there’s the security and flexibility that only an on-prem solution can offer.
Why you need a Data Integration Layer (ETL), reasons to use a SaaS based tool for ETL rather than coding it through scripts.
ETL is a critical, necessary process for almost all analytics projects. But the harsh reality is that ETL is complicated, with many challenges along the way, and implementation can be a daunting task or Extremely Tough to Load :)
Writing your own ETL code is not trivial. What starts out as a simple ETL process gets more complex over time. So does the coding, which becomes less manageable. A short story that morphs into a convoluted volume that rivals Tolstoy’s War & Peace.