The data engineer creates dataflows and pipelines and makes it possible to transfer data for use in applications or reporting. In other words, they make data accessible for different users and consumers.
Any organisation has multiple data sources, systems and applications available. In order to make well informed decisions, information from all these different sources is often required. With the setup of ETL jobs (Extract, Transform, Load), one is able to take the load of production systems and make data and information more easily available to different consumers. By having queryable datasets in place, data can flow through organisations and applications more easily. This allows organisations to do more with their data on a timely basis.
Data Engineering sets the basis for any future data initiatives.
Data ingestion is the process of obtaining and importing data for immediate use or storage in a database.
Data can be ingested in batch, near real-time or realtime.
The underlying data architecture should facilitate this streaming, CDC, Event-driven or Batch setup.
Data cleansing is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data.
It usually follows these steps: Identify, Standardise, Validate, Correct and Monitor.
Data transformation is the process of converting data from one format or structure into another format or structure.
It is usually defined in these types: Constructive, Destructive, Aesthetic and Structural.
Extract, transform, load is the general procedure of copying data from one or more sources into a destination system which represents the data differently from the source(s) or in a different context than the source(s).
ETL transforms your data before loading, while ELT transforms data only after loading to your warehouse.
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