What this covers
Follow the decision matrix, orchestration guidance, and automation patterns to stand up resilient nightly or intraday batch pipelines for dozens of data sources.
Implementation trail
- Workload assessment and service fit
- Landing zone and catalog strategy
- Glue-centric orchestration patterns
- EMR Serverless job design
- Cost, observability, and integration guardrails
Start with a workload decision matrix
Map each dataset’s volume, transformations, and operational needs to the right managed service before provisioning compute.
- AWS Glue ETL jobs: Best for curated transformations on moderate datasets (<5 TB per run) where automated schema handling, integrated catalog updates, and serverless capacity fit the budget.
- EMR Serverless (Spark): Choose for wide transformations, heavy joins, or ML feature engineering that benefits from Spark tuning, custom JARs, or larger executor footprints.
- Managed Airflow (MWAA) / Step Functions: Orchestrate heterogeneous tasks (SQL, API calls, ML in SageMaker) and coordinate Glue or EMR jobs with retries, branching, and approvals.
- AWS Batch or Fargate: Reserve for containerized batch workloads (data enrichment APIs, PDF parsing) that complement Glue or EMR outputs without needing Spark semantics.
- Legacy Amazon EMR on EC2: Still relevant for tightly controlled Hadoop ecosystems, but expect higher ops overhead versus the serverless counterparts.
Design landing-to-curated zones for multiple streams
Ingest every producer into a consistent storage and catalog structure so Glue and EMR can operate on the same partitions without duplication.
- Segregate
s3://<env>-landing/<stream>/ and s3://<env>-curated/<domain>/ prefixes with lifecycle policies that expire transient staging files while retaining governed outputs. - Register raw and curated partitions in Glue Data Catalog, using LF-Tags to gate access by domain and streaming sensitivity.
- Adopt event-driven crawlers or Lake Formation blueprints so new streams are discoverable within minutes, even when dozens of producers land files concurrently.
Operationalize Glue-centric batch orchestration
Lean on Glue workflows, triggers, and bookmarks to coordinate dependable curation jobs without maintaining infrastructure.
- Bundle domain-specific Glue jobs into Workflows that fan out by stream, then converge into consolidation steps for warehouse-ready tables.
- Enable job bookmarks and incremental pushdown predicates to process only new partitions, limiting storage scans and cost.
- Integrate AWS DataBrew or Glue Data Quality rules for lightweight data profiling, and push failure events to EventBridge for pager and ticket automation.
Scale complex runs with EMR Serverless
EMR Serverless delivers Spark performance for heavy joins, streaming backfills, or notebook-driven experimentation without provisioning clusters.
- Package shared Python wheels or JARs in S3 and reference them via Spark submit parameters, letting teams standardize business logic across runs.
- Tune executor counts, memory, and auto-stop windows per application to optimize for bursty nightly loads versus all-day trickle updates.
- Capture execution metadata and lineage by emitting Spark event logs to CloudWatch or an S3 audit prefix, then push summaries into the catalog for observability.
Guardrails for cost, integration, and downstream delivery
Protect budgets and keep downstream consumers in sync whether they connect through SQL, APIs, or dashboards.
- Instrument cost allocation tags on Glue, EMR, and supporting buckets; pipe metrics into Cost Explorer and Budgets to spot expensive joins early.
- Expose curated outputs via Redshift Spectrum, Athena, or OpenSearch depending on consumer query patterns to avoid duplicate ETL jobs.
- Standardize logging destinations (CloudWatch Logs, S3, or OpenSearch) and enforce retention so auditors can replay how each stream was processed.
CloudFormation accelerators
Bootstrap both Glue and EMR Serverless patterns with infrastructure as code to keep environments consistent.
- Use the sample template (
batch-data-pipelines.yaml) to provision landing buckets, Glue catalog assets, and daily triggers in minutes. - Extend the template with per-stream Glue Workflows or Lake Formation grants as data domains onboard.
- Pair the EMR Serverless state machine with EventBridge rules so new partitions automatically queue Spark jobs when upstream feeds publish.
