Ever since E. F. Codd introduced the first relational model for storing data at IBM in 1970, the industry has picked up the database technology and used it for its competitive advantage. The relational database management system - or RDBMS - was the default technology for storing and accessing data for a long time. It supported transactional data storage, the building of data products, and was the go-to model for data that was used in data-driven decisioning.

ETL and ELT refer to two patterns of data storage architecture within your data pipelines. The letters in both acronyms stand for: So both ETL (extract, transform, load) and ELT (extract, load, transform) processes help you collect data, transform it into a usable form and save it to permanent storage, where it can be accessed by data scientists and analysts to extract insights from the data. What is the difference?

The world of big data is getting bigger every day. As the volume of data increases exponentially, businesses of all sizes try to capture raw data, process it, and extract insights for competitive decision-making. The end-to-end operation of extracting value from data is called the ETL process. It stands for: A crucial component of the ETL process is the data storage aspect. The two main contentious architectures for storage solutions are databases and data warehouses. But how do they differ?

Whether you saw or missed our webinar, we thought it would be useful to provide a step-by-step guide on how to set up quick competition monitoring (or, any other web scraping and data processing automation) with Apify and Keboola. Thank you Apify and Revolt.bi for the collaboration! So what can you do with automated competition data processing? In this article, we’ll take an example of daily monitoring of the best-sellers list at Amazon.