Data Ingestion and Transformation
Data Engineering Code and Delivery
CoreOptimize runtime behavior, Lambda capacity, toolchains, testing, observability, infrastructure as code, serverless packaging, mounted storage, CI/CD, and distributed data structures at the level of patterns and runtime behavior.
Aligned to AWS Certified Data Engineer - Associate (DEA-C01) Version 1.1, verified August 25, 2026.
Why this matters
Reliable pipelines are software and infrastructure products. Reproducibility, safe retries, measured performance, and controlled promotion matter as much as a transformation producing the right row once.
Must Know
- Profile before optimizing: prune data early, avoid repeated parsing, reduce unnecessary I/O and shuffle, and correct the measured bottleneck.
- Lambda reserved concurrency caps capacity available to a function and can protect downstream systems; provisioned concurrency prepares execution environments and does not serve as a downstream throttle.
- Select a language or framework for engine support, libraries, runtime, and team operations. Do not memorize language-specific syntax for this exam.
- Use version control, automated tests, structured logs, metrics, and stable operation identifiers so a change can be reproduced and diagnosed.
- Represent resources, dependencies, parameters, roles, and event targets in reviewed infrastructure as code instead of relying on console drift.
- AWS SAM provides serverless-oriented templates and tooling; AWS CDK synthesizes CloudFormation; CloudFormation remains the deployment lifecycle for synthesized CDK stacks.
- Lambda can mount Amazon EFS through an access point when functions need shared durable POSIX storage and have the required VPC and role configuration.
- CI builds and tests a controlled artifact; delivery makes it releasable; deployment changes an environment. Promote the same immutable artifact through approved stages.
- Distributed work needs partitioning, coordination, shuffle control, and fault recovery. More workers can worsen a shuffle-bound job.
- Graphs represent arbitrary relationships such as many-to-many lineage; trees represent a strict hierarchy.
Compare and Distinguish
- Reserved versus provisioned concurrency: downstream capacity control differs from reducing cold-start preparation.
- SAM versus CloudFormation versus CDK: serverless tooling, declarative deployment model, and code-defined synthesis are related but not interchangeable.
- CI versus delivery versus deployment: validation, release readiness, and environment change are separate stages.
- Graph versus tree: arbitrary connected relationships differ from single-root hierarchical structure.
Scenario examples
- A database-backed Lambda is capped with reserved concurrency based on connection capacity and reuses connections through an appropriate strategy.
- A CDK application synthesizes a reviewed CloudFormation change set that is deployed consistently to test and production parameters.
- A shuffle-heavy distributed job repartitions on the join key before adding workers.
Exam traps
- Using provisioned concurrency to limit total database connections.
- Treating a source-code repository alone as a testing and observability strategy.
- Believing CDK bypasses CloudFormation stack ownership.
- Adding parallel workers when cross-worker data movement is the dominant cost.
Key takeaways
- Optimize from measurements and preserve correctness.
- Deploy code and infrastructure repeatably through tested artifacts.
- Reason about distributed coordination, not only worker count.
How it works
- Source changes pass automated tests and packaging before one identified artifact is promoted through controlled environments.
- Infrastructure definitions parameterize environment differences, preview resource changes, and keep roles, events, and dependencies under review.
When to use it
- Use reserved concurrency to bound Lambda capacity against a downstream limit and EFS when functions need shared durable POSIX files.
- Use SAM for serverless-focused templates, CDK for reusable code constructs, or CloudFormation for direct declarative stacks.
Security and governance implications
- Keep deployment roles separate from runtime roles and scope each to the resources and stages it must change or use.
- Store environment-specific values outside code and prevent build logs or artifacts from carrying long-lived credentials.
Common failure modes and diagnosis
- For Lambda pressure, compare concurrency, duration, initialization, downstream connections, retries, and event-source backlog.
- For a distributed slowdown, measure skew, shuffle, partition sizes, and repeated parsing before adding workers.
More detail
- 1.4.1: Optimize code to reduce runtime for data ingestion and transformation.
- 1.4.2: Configure Lambda functions to meet concurrency and performance needs.
- 1.4.3: Use programming languages and frameworks for data engineering (for example, Python, SQL, Scala, R, Java, Bash, PowerShell).
- 1.4.4: Use software engineering best practices for data engineering (for example, version control, testing, logging, monitoring).
- 1.4.5: Use Infrastructure as Code (IaC) to deploy data engineering solutions.
- 1.4.6: Use AWS SAM to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables).
- 1.4.7: Use and mount storage volumes from within Lambda functions.
- 1.4.8: Use infrastructure as code (IaC) for repeatable resource deployment (for example, AWS CloudFormation and AWS CDK).
- 1.4.9: Describe continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines).
- 1.4.10: Define distributed computing.
- 1.4.11: Describe data structures and algorithms (for example, graph data structures and tree data structures).
Ready for the quiz?
- Is the constraint function concurrency, per-invocation performance, distributed shuffle, packaging, or environment drift?
- Does the delivery path promote one tested artifact with parameterized infrastructure and a controlled rollback boundary?
Related objectives
- D1.4 — Task 1.4: Apply programming concepts
- 1.4.1 — Optimize code to reduce runtime for data ingestion and transformation.
- 1.4.2 — Configure Lambda functions to meet concurrency and performance needs.
- 1.4.3 — Use programming languages and frameworks for data engineering (for example, Python, SQL, Scala, R, Java, Bash, PowerShell).
- 1.4.4 — Use software engineering best practices for data engineering (for example, version control, testing, logging, monitoring).
- 1.4.5 — Use Infrastructure as Code (IaC) to deploy data engineering solutions.
- 1.4.6 — Use AWS SAM to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables).
- 1.4.7 — Use and mount storage volumes from within Lambda functions.
- 1.4.8 — Use infrastructure as code (IaC) for repeatable resource deployment (for example, AWS CloudFormation and AWS CDK).
- 1.4.9 — Describe continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines).
- 1.4.10 — Define distributed computing.
- 1.4.11 — Describe data structures and algorithms (for example, graph data structures and tree data structures).