PrettyWhale.ai
The problem everyone faces
Ingestion code is 80% boilerplate, 20% expertise – and 100% pain
Today, 80% of the time and budget in Data projects is consumed by the production and maintenance of ingestion code. – Source Mc kinsey Study *
Problems
with
Ingestion
Code
– Long ingestion code development time
– Dependence on rare and expensive experts
– Multiple tools and formats
– Long and risky deployment
– Complex long-term maintainability
– Differentiation for calls for tenders
– Difficulties in standardizing deliverables
– Low quality and reliability
Ingestion code is the critical infrastructure that makes or breaks data operations
Invisible but essential
This code, invisible and repetitive, is what powers every analytical system. It extracts data from sources, transforms it, and loads it into warehouses.
Critical and tedious
It requires expert engineers, extreme rigor, and deep knowledge of architectures
Without this code, nothing works
It is the first link of the Data Chain
The Data Engineer is the hidden pillar behind ingestion code — essential, complex, and under pressure.
The Data Engineer is the most technical of all Data Roles
He is the person who designs and maintains ingestion code – the foundation that makes the data accessible and usable for Data Scientists and Data Analysts
His time is monopolized by mechanical, low-value tasks: the boilerplate code.
It is needed between 2 and 3 data engineers for every 1 data scientist on staff.
Between 2025 and 2030
Companies will seek to hire 3 times more Data Engineers than the number actually available
It is time to fully recognize the value of Data Engineer profiles by enabling them to dedicate 100% of their time to their real expertise —
instead of repetitive, tedious, time-consuming tasks that require extreme rigor while offering little to no recognition.
Experience the Power of PrettyWhale.ai
Fill in this contact form and we will come back to you PrettySoonon.
* Source = Mc Kinsey Study – https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-missing-data-link-five-practical-lessons-to-scale-your-data-products)