Massive data analysis with BigQuery

Massive Data Analysis with BigQuery

Description

When a client tells us they have chosen Google Cloud BigQuery for their data office, we know they are serious about data analysis.

Starting with BigQuery is always a good step, but it’s essential to ensure it’s being implemented in the best possible way.

For Adamo , daily data analysis is crucial, and the volume of information is enormous. Adamo, a telecommunications operator born in Sweden, began operating in Spain in 2007, where it became the first operator to offer 1 Gbps fiber optic – the highest bandwidth available for households today.

They want to ensure that necessary best practices are being implemented and need Taniwa to perform an analysis and report on improvements.

Value provided by Taniwa

We discuss and review the needs of the different client departments. We meet with their technology provider to review their workflow processes.

We review the architecture and code of all ETL and BI processes and present a consensus report including:

Strengths
Areas for improvement
Best practices
Performance and security enhancements
Action plan

One of our key advantage was having Diego Rodriguez as an expert in Google Cloud and BigQuery.

Technologies

ETL - DBT
BIG Query
Python
Data mesh (more a methodology than a technology)

DBT stands for Data Build Tool. It’s an open-source command-line tool written in Python that helps data analysts and engineers transform data in their data warehouses more effectively. DBT is particularly popular within the data analytics and data engineering communities because it simplifies the process of transforming and modeling data in a scalable and maintainable way.