There are many techniques for extracting data, depending on the kind of data source and the intended use of the data. And much of it is highly sensitive and personal, and needs to be very carefully guarded for privacy and other concerns. Here are some examples of raw data sources: Archived text and images from paper documents and PDFs Web pages, including text, tables, images, and links Analog audio and video, which can be recorded on media such as magnetic tapes or streaming in real time Survey data, statistical, and economic data Transactional data from business, financial, real estate, and point-of-sale, or POS, transactions Here are more examples of raw data sources: Event-based data such as social media streams Weather data from weather station networks Internet of Things (or IoT) sensor streams Medical records, such as prescription history, medical treatments, and medical images Personal genetic data encoded in DNA and RNA samples Evidently, data is everywhere. Relate use cases with data sources and extraction techniques. After watching this video, you will be able to: List examples of raw data sources. You will be able to outline some of the multiple methods for loading data into the destination system, verifying data quality, monitoring load failures, and the use of recovery mechanisms in case of failure.įinally, you will complete a shareable final project that enables you to demonstrate the skills you acquired in each module. You will also define transformations to apply to source data to make the data credible, contextual, and accessible to data users. You will identify methods and tools used for extracting the data, merging extracted data either logically or physically, and for importing data into data repositories.
During this course, you will experience how ELT and ETL processing differ and identify use cases for both. ELT processes apply to data lakes, where the data is transformed on demand by the requesting/calling application.īoth ETL and ELT extract data from source systems, move the data through the data pipeline, and store the data in destination systems. ETL processes apply to data warehouses and data marts. The other contrasting approach is the Extract, Load, and Transform (ELT) process. One approach is the Extract, Transform, Load (ETL) process. After taking this course, you will be able to describe two different approaches to converting raw data into analytics-ready data.
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