Engineering a strong foundation
Collecting source data and transforming them in a systematic way to a clean and comprehensible model is the cornerstone for unleashing their potential.
Data Engineering is more that data movement
Simply copying data from one place to another does not guarantee they can be used to generate insights.
Although computers “do not make mistakes”, people do. Large volumes of real-world transactions, frequent changes to introduce new features or products, technical mishaps – all end up as records in a digital system that need to be extracted, normalized and correlated. Data Engineering does with data what a chef does with the food ingredients: chooses the good ones, prepares them and serves them to the consumer.
What is Data Engineering
Data Engineering is the process of taking source data in any form and processing them to produce a data set appropriate for reporting or advanced analytics and machine learning.
Your data is usually generated in a transactional system. A data model is keeping and using them to serve the operational needs of a specific business function. However this model is not easily comprehensible or usable by a data analyst. Even an AI-system would have a hard time extracting something useful from it.
Using Extract-Transform-Load (ETL or ELT) practices, data engineering will be the magic gateway through which databases, files, event streams, APIs and any possible source will traverse to end up as a new model for analytical processing. A semantic layer hides technical details and allows focusing on business dimensions and metrics.
What makes Data Engineering essential
- Data Engineering ensures the availability, quality and timeliness of data.
- Sets up processes to frequently and automatically update the data used for analytics.
- Constantly enhances data reliability by incorporating controls to repair or discard problematic data.
- Aggregates and associates data to ensure good analytics performance and foster experimentation.
- On average, Data Engineering effort accounts for more than 80% of any analytics problem that does not rely on readily-available data.
