How To Build Data Pipelines: Full Process & Best Practices
To be successful, a company’s data pipelines must be scalable, organized, usable to the correct stakeholders, and above all, crafted to align with business goals.
To be successful, a company’s data pipelines must be scalable, organized, usable to the correct stakeholders, and above all, crafted to align with business goals.
ETL stands for extract, transform, and load. Together, this set of processes moves data from a source to a destination system.
If we’re not careful, the modern data stack and ELT can cause new incarnations of problems that have been plaguing us for years.
If you set up your architecture right, you just need one, robust data pipeline system.
For the past several years, the workforce has been chronically short on data engineers. But what will happen as vendors build services that automate much of their daily work?
There is a cost associated with putting your data to work, and the benefits you gain depend on the systems you put into place. To maximize net value, you need to strike a balance between minimizing costs and maximizing gain.
At first glance, ETL and ELT seem rigidly defined and mutually exclusive. But to understand our data integration options, we need to look closer.