State-of-the art data engineering
Robust, scalable & resilient.
title: Data Intensive Pipelines header: State-of-the art data engineering subtitle: Robust, scalable & resilient. type: service serviceTag: data-engineering
Data pipelines are essential in order to generate insights or to create data-driven solutions and train smart algorithms.
They collect data from operational and third-party data sources and make them available for analysis in a trustable manner.
A plethora of tools exist that support the development and deployment of data pipelines. This abundance means that an opinionated approach is required in order to build a system that is sustainable and scalable in the long run.
Most likely you are also in a context where the amount of data you have at hand grows day after day. This makes for a need for a robust data pipelines system that can handle growing loads of data and can scale as needed. Resilience comes into play when something fails, if despite best efforts something goes wrong, you need to get back up & running as soon as possible without losing data.
Data quality is key. Analytical data often serves the purpose of providing a business with insights on its efficiency and performance. And it is the main input for any data driven solution. To make sure data quality is of the highest standards quality assurance needs to happen at data ingestion and monitored over time.
Data ingestion can take multiple forms in between pure batch and pure streaming. Making sure that your ingestion layer supports your current and future needs is important in order to have access to a uniform ingestion layer that supports multiple modes of transports and tries to minimize overall maintenance needs.
sign up to our weekly AI & data digest ❤️