Implementing a Scalable Data Engineering Platform

The client required a robust data engineering platform to collate and transform data from multiple ERPs. The goal was to create a thin, scalable solution suitable for the cloud with pay-per-use, minimizing costs and supporting the addition of micro-strategies over the data architecture.

Challenge

The primary challenge was to design and implement a scalable, cost-effective data engineering platform capable of handling data from various ERPs while ensuring minimal service delays and high performance.

Scalability and Cost Efficiency

Designing a thin and scalable data engineering layer suitable for the cloud, with a pay-per-use model to minimize costs.

Integration and Transformation

Handling the integration and transformation of data from multiple ERPs to ensure seamless data flow and accurate analytics.

Solution

Entrans developed a comprehensive data engineering platform using AWS architecture and a variety of technologies to meet the client's objectives.

Result: The platform enabled efficient data integration, transformation, and storage, ensuring scalability and cost-effectiveness while supporting future growth.

Detailed Solution:

AWS Architecture: The solution leveraged AWS Redshift for faster data loading from S3, extremely fast transformations, and the ability to pause the compute node when not in use to save costs. The columnar data store provided no limitations on concurrent queries, optimized for structured data processing and traditional data warehousing use cases. The curated data lake was an optional stage before loading to Redshift.
Data Processing with EMR: Amazon EMR was used for handling unstructured/semi-structured data and transformations that were difficult to express in SQL, ideal for data science use cases. Spot instances helped keep costs low, though they required startup time as transient clusters.
Data Storage and Synchronization: Curated data was split into multiple marts using Redshift based on business and geographical demarcations. The transformed data was then pushed into S3 buckets, and multiple query options were provided on files (S3 using Athena) and databases.

Impact: The implementation led to a scalable, efficient, and cost-effective data engineering platform that streamlined data integration and transformation, enhancing the client's ability to make data-driven decisions.