DATA ENGINEERING

[ WHAT WE DO ]

Sets the stage for smart decisions by organizing and prepping your data

Data engineering is vital for ensuring the quality, integrity, and availability of data. It forms the backbone of analytics, supporting accurate and reliable analyses that underpin informed decision-making.

The key steps of data engineering

At Algorythme, our data engineering process involves several key steps to ensure data is accessible, reliable, and primed for insights:

Step 1

Data collection

This initial step involves gathering data from various sources such as databases, web services, local files, or live data feeds. The focus is on capturing high-quality, relevant data

Step 2

Data storage

Once collected, data needs to be stored in a way that it can be easily accessed, managed, and retrieved. This could involve databases, data lakes, delta lakes, data warehouses, or cloud storage solutions

Step 3

Data processing

This step involves refining the data by cleaning out inaccuracies, filling missing values, correcting errors, and sometimes transforming data formats

Step 4

Data integration

Data from different sources may need to be combined (integrated) or transformed into a structure suitable for analysis. This could involve normalization, aggregation, or other methods to standardize data interactions

Step 5

Data modelling

Structuring or modeling data in a way that supports effective querying and analysis is crucial. This often involves organizing data into tables and relations in a data warehouse

Step 6

Data archiving

Not all data needs to be kept accessible at all times. Older data that is not needed for day-to-day operations but may still be important for historical analysis or compliance purposes is archived