Building Efficient Serverless Data Lake on AWS: Accelerate development time by 33%
The future of Digital Service Providers (DSPs) will be driven by agile and data-driven decision making. DSPs generate data from a variety of sources every day. Hence, integrating and storing their massive, heterogeneous, and siloed volumes of data in a centralized data lake is a key imperative.
The demand for every DSP is a data storage and analytics solution of high quality, which could offer more flexibility and agility than the conventional systems. A serverless data lake is a popular way of storing and analyzing data in a single repository. It features huge storage, autonomous maintenance, and architectural flexibility for diverse kinds of data. In addition, a serverless data lake accelerates integration with the analytics engine and improves time to insights.
Storing data of all types and varieties in a central platform sounds good. But, it can create additional issues. According to Gartner, 80% of data lakes do not include effective metadata management capabilities, which makes them inefficient. DSPs’ data lakes are not living up to expectations due to reasons like data lake becoming a data swamp, lack of business impact, and complexities in data pipeline replication.
This insight further details on the critical parameters that can help DSPs mitigate these challenges and implement a high-performance and efficient Amazon Web Services (AWS) serverless data lake. The parameters include Interface Control Template, Data Architectural Workshop, Data Cataloging Approach, Infrastructure as Code (IaC), and Event-driven Orchestration.
Fig: Infrastructure as Code (IaC) to accelerate the building and scaling of the data pipeline
Implementing the five-step methodology elaborated in this insight helps DSPs to accelerate the integration time with BI tools and ML models, improve the data processing time and accelerate the data lake building time by 33%.
- Manoj Kumar
- Sriram V
- Sathya Narayanan