Data swamp to a data lake – Not a child’s play

How a scalable and flexible cloud-native data lake enabled by serverless technologies helped a leading service provider in LatAm make data-driven decisions

Data is the essence of modern business - it powers decisions and operating models enabling a business to move in the right direction.

Organizations today face challenges in managing diverse data types coming in from various sources.

With growing volumes of large and heterogeneous data, businesses find it challenging to derive insights on time.

What must a business do to access insightful data that is available anywhere, anytime?


36% faster building of cloud-native serverless data lakes

58% reduction in ML integration efforts

Lower costs than a traditional on-premises data lake infrastructure

Client Situation

The client, a LatAm-based telecom conglomerate, had accumulated a huge amount of data from multiple geographies over the years. Due to on-premises storage and swamping of data over time, they faced challenges in managing the data, analyzing it, and extracting meaningful insights.

Prodapt partnered with the client to build a solution that could simplify their data management and infrastructure with its adeptness in cloud operations across all their locations.

Prodapt enabled the client to thrive in an era of hyper-competition by delivering faster access to data-driven insights.


There were inefficiencies in integrating systems which led to problems in accessing data, retrieving it, and deriving actionable insights. Due to siloed data across multiple geographies, the client lacked a holistic view of data and couldn’t derive actionable insights.

Solving It

Prodapt enabled a flexible and scalable serverless data architecture on the AWS platform to mitigate the problems. We took a ‘business value first’ approach to build a flexible data model that could address the changing needs of business, IT, and other functions.

We built a cloud-native data lake to ingest and manage data from different geographies. Our data cataloging approach and governance model prevented the data lake from becoming a data swamp.

In addition, we built an architecture blueprint with serverless technology and capabilities to stitch serverless apps together, route data from different sources to the data lake, and extract actionable insights.

Our solutions helped the client achieve a 36% acceleration in the time to build a fully operational cloud-native serverless data lake as compared to traditional methods to build a data lake. They could also launch analytics and machine learning use cases faster, with a 58% reduction in machine learning integration efforts. They significantly lowered their costs than a traditional on-premises data lake infrastructure.

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