Speed-up value creation
with Data Engineering.
Data does not have to be a bottleneck
Today, even small and medium-sized enterprises (SMEs) find themselves dealing with large datasets. Legacy data engineering systems may no longer be sufficient to keep up with this trend.
Cloud technology has become crucial for companies, particularly for data hosting and processing.
Choosing a technology stack that meets the needs of your company and has the ability to scale is essential to fully leverage this ever-expanding data.
The lost POC
Any data project begins with a business idea, and its value is analyzed through a proof of concept that excites teams.
However, there are often challenges in transitioning from the PoC stage to a fully developed data product. The main reason for this difficulty is the lack of a proper platform. The skillset of a data scientist typically ends at this point, just shy of achieving continuous value delivery.
A data platform supports data scientists and analysts in deploying their projects without requiring a complete "reskill.”
The never-ending maintenance
Analytics teams are skilled in developing use cases quickly. However, over time, resources allocated to new projects erode to make room for the maintenance of developed solutions. This shift is the result of inefficiencies in automation, monitoring, and scaling, leading to a decrease in the quality of data products.
Implementing data engineering best practices can expedite development by reducing the burden of heavy manual data operations tasks.
The Agilytic way
Since 2015, Agilytic supports organizations in the continuous delivery of data projects. Combining technological epertise and pragmatic core beliefs, we have developed accelerators that enable teams to grow efficiently.
A well-managed data platform is crucial for success. Additionally, upskilling is vital for effectively adopting the deployed platform. We advocate for close collaboration with the existing data team to avoid creating a black box effect. We design and develop with the future team in mind, ensuring that the platform and development practices are well-suited to their needs.
Proven impact in Data Engineering with industry leaders.
Certified expertise
Our core beliefs
Consistency: avoid technology disparity, as every new layer in the technology stack will add complexity. Keep the focus on validated technologies that the team can easily adopt.
Simplicity: cloud services allow for simple solutions. With guidance and a set of best practices, a data team can go far in deploying a data product on their own.
Frugality: an effective architecture can provide a flexible data platform on a pay per use basis. Costs stay under control and grow proportionally with the value delivered to the business.
Professionalism: quality must be maintained throughout the lifetime of the product. The more robust the pipeline is, the more time is available on new developments.
Long-term: a good platform is a platform that will scale with the business and does not require an overhaul every couple of years.