Our Data Engineering Process .
Requirements analysis
At the very first step, we determine users’ detailed needs and expectations for a new or modified product. It is a plan for all the subsequent data-related processes.
Data architecture design
We establish a framework that shows the sources of information, and how this information is being transported, secured, and stored. Data architecture manages the data strategy.
Data ingestion
We transport the data to a storage medium or import it for immediate use.
Data cleaning
Before the data makes it to the pipeline, it needs to be cleaned first. We correct or remove all the irrelevant and incorrect parts of the records.
Data Lake building
We create Data Lakes to store all sorts of data, from raw and structured to unstructured, in one place. You can use programs like Hadoop, GCS, or Azure to build them. And if you need to do some fancy data engineering with Python, we’ve got you covered!
ETL/ELT pipelines
After preparing the stored data, the ETL engineer starts the data processing operations. It is the most critical act in the data pipeline because it turns raw data into relevant information.
Data modelling
In this phase, we dive into the data and explore its structures. Our aim is to show how the data is related and to highlight the different types of data and how they can be grouped together.
Quality assurance
Before sending the data any further, it needs to be tested and get quality-approved. Our specialists create test cases for verification and validation of all elements of data architecture.
Automation & deployment
This is one of the most important steps in the whole process. Our team creates the DevOps strategy that automates the data pipeline. This process saves a lot of time, money, and effort spent on pipeline management.