Big Data

This page brings together practical insights for teams working with Big Data platforms—covering data pipelines, distributed processing, stream analytics, scalable storage patterns, and the engineering choices that keep large datasets reliable and usable. You’ll find guidance based on real implementations, including how organizations structure ETL/ELT workflows, manage data lakes, optimize Spark jobs, integrate with cloud-native services, and design architectures that support advanced analytics and machine learning.

As you explore these resources, you’ll understand what really matters when you hire Big Data developer talent: experience with distributed systems, comfort designing performant data flows, strong command of cluster-based processing, and the ability to handle data quality, governance, and monitoring at scale. And whenever your team needs deeper expertise, you can review developer profiles, request curated shortlists, or schedule interviews to bring proven Big Data skills into your project.