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Grokking the AWS Certified Solutions Architect – Professional

Big Data Processing

Explore AWS big data processing frameworks including Amazon EMR, AWS Glue, and AWS Batch. Understand how to select the right model based on workload needs, design integrated data pipelines, and apply security and cost optimization strategies using S3-centric data lakes. This lesson helps you master scalable and flexible analytics architectures for real-world enterprise environments.

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Modern enterprise data platforms process petabytes daily, yet many scenarios challenge architects to move beyond the assumption that “big data equals Hadoop clusters.” AWS provides three distinct execution models for large-scale data processing, each optimized for different workload characteristics, operational requirements, and cost profiles. The architectural foundation across all three is an S3-centric data lakeAn architectural pattern where Amazon S3 serves as the durable, scalable storage layer while compute services attach ephemerally, enabling independent scaling of processing capacity without data movement or lock-in to a specific compute engine., where compute is fully decoupled from storage. This enables elastic scaling without data migration. This lesson examines how Amazon EMR, AWS Glue, and AWS Batch serve complementary roles in enterprise data pipelines, and how you select the right execution model based on workload semantics rather than defaulting to any single service.

The following diagram illustrates how these three services operate within a secure VPC boundary. Each consumes data from S3 through VPC endpoints while serving distinct processing patterns.

Big data processing architecture with EMR, Glue and Batch paths in VPC
Big data processing architecture with EMR, Glue and Batch paths in VPC

Understanding when each path applies requires examining its architectural characteristics, starting with migrating existing Hadoop and Spark workloads to Amazon EMR.

Migrating Hadoop and Spark to Amazon EMR

Amazon EMR provides managed infrastructure for Apache Hadoop, Spark, Hive, Presto, and HBase while maintaining full API compatibility with existing big data frameworks. The migration strategy from on-premises clusters centers on decoupling storage from compute by replacing HDFS with EMRFS, which presents S3 as a Hadoop-compatible file system. Jobs are refactored to read and write directly to S3, eliminating the need for persistent cluster storage and enabling clusters to scale independently of data volume.

Instance fleets and managed scaling

EMR offers two capacity models with distinct trade-offs for common scenario questions:

  • Instance groups define a single instance type per group (primary, core, task), providing simplicity but limiting flexibility when specific Spot capacity is unavailable.

  • Instance fleets allow multiple instance types and purchasing options within a single logical group, enabling EMR to select from a diversified pool of On-Demand, Spot, and Reserved capacity to maximize availability and minimize cost. ...