Data Pipeline Architecture & Spark Workload Assessment

Applogika evaluates existing or planned data-processing workloads to design scalable AWS Glue and Apache Spark pipeline architectures. The engagement reviews data volumes, source systems, transformation complexity, processing frequency, SLA requirements, current bottlenecks, failure handling, monitoring requirements, and expected future growth.

Based on the assessment, Applogika develops an AWS-native data pipeline architecture covering AWS Glue worker types, Spark configuration, partitioning strategies, job design, orchestration, retries, monitoring, security, and cost considerations. Integration requirements with Amazon S3 and other AWS data services are also considered. Customers receive implementation-ready architecture recommendations, sizing guidance, design patterns, operational considerations, and a phased plan for moving workloads to scalable AWS data processing.