Data engineering, ecosystems and pipelining solutions

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Business Need

Man­aging and wanting to upscale large projects can be quite a dif­fi­cult problem. They involve catering to big amounts of clien­tele and responding to their needs. Using data to accom­plish this feat is usually rec­om­mended but not all the projects have a healthy ecosystem for data. Much data is lost, has incon­sis­ten­cies, or is hard to combine thus making gath­ering insights or building logic very complex and rigid processes.

Our Solution

The initial data storage place usually is not well suited to ana­lytics and AI processes. We have created data pipelines that move and process the data in a modular manner from the initial source to its pre­de­ter­mined des­ti­na­tions. The steps taken by the data can be adapted to be suit­able to each cus­tomer and their data needs.

The main ingre­di­ents of a pipeline are the nor­mal­iza­tion and san­i­ti­za­tion of data. In short terms: the process is com­prised of preparing the data and making it much more infor­ma­tive. After such processes are applied, the busi­ness analysis and data science teams will find that part of the tedious process of looking at and ana­lyzing data is gone.

Our tech­nology stack is well-main­tained and kept up to state-of-the-art stan­dards but is also adapt­able to each use case. This makes our solu­tions future-proof, easy to work with, and assures the scaling poten­tial needed for big data volumes and intakes.

Screen­shots

Industry: 
Tech­nology and Information

Cat­e­gory: 
Big Data
Data Engineering
Method­olo­gies and Project Management
Data Pipelines
Data Analytics

Tech­nolo­gies:
Python
Airflow
Snowflake
Dbt
Airbyte
Pandas
Amazon Cloud Services