Inside the AWS Certified Data Engineer – Associate Certification

The AWS Certified Data Engineer – Associate (DEA-C01) certification represents a comprehensive assessment of one’s ability to design, build, secure, and maintain data pipelines and analytics solutions using AWS services. It is tailored for professionals who operate within data-driven roles and need to manage data across different storage, processing, and analytical layers. Understanding how to organize and manipulate data efficiently using AWS tools is essential for success in this certification.

This article explores the foundational areas of data modeling, cloud storage optimization, secure migration strategies, and effective data governance practices—all of which are critical to both the exam and real-world applications.

Understanding Entity-Relationship Modeling in the Cloud

At the heart of data engineering lies the discipline of data modeling. This is the process by which raw, unstructured data is transformed into formats that are easier to store, retrieve, and analyze. One of the most commonly used modeling techniques is the entity-relationship diagram (ERD). ERDs illustrate the logical structure of data, including entities, their attributes, and relationships between entities.

In a cloud environment like AWS, the principles of ERD still apply, but they integrate with scalable services such as Amazon RDS or Amazon Aurora. Data engineers must understand how to translate ERDs into relational schemas that can be implemented efficiently on cloud-managed databases. These diagrams are not only helpful in design but also guide permissions, replication strategies, and data normalization efforts.

Structuring S3 for High-Performance Data Lakes

When building a data lake on Amazon S3, performance is heavily influenced by the way data is organized. A common mistake is to store all data in a single flat structure. While technically possible, this results in slower query responses and inefficient scanning.

Best practice involves partitioning data based on commonly accessed attributes such as date, region, or customer ID. For instance, logs might be stored with a prefix like logs/year=2025/month=07/day=21/. This format helps services like Amazon Athena and AWS Glue identify and scan only relevant partitions during query execution. Additionally, using consistent naming conventions and file formats like Parquet or ORC enables faster columnar access and reduces costs associated with data scanning.

Schema Evolution and Format Selection

Choosing the right file format for your data pipeline directly impacts the pipeline’s flexibility and compatibility with evolving schemas. Structured formats like CSV offer simplicity but lack support for schema enforcement or evolution. JSON supports semi-structured data but is not optimal for analytical workloads due to its text-heavy format.

Columnar formats such as Apache Avro and Apache Parquet support schema evolution and allow changes without breaking compatibility across systems. Avro is particularly effective when schema changes are expected frequently, while Parquet is optimized for read-heavy, analytics-centric use cases. Understanding the tradeoffs among these formats is essential for data engineers tasked with long-term pipeline maintenance.

Secure Migration with AWS Database Migration Service

Migrating databases to the cloud while maintaining business continuity is a common task for data engineers. AWS Database Migration Service (DMS) offers a streamlined way to migrate data from on-premises databases to AWS-managed services like Amazon Aurora, Amazon RDS, or Redshift.

One of the most valuable features of DMS is its ability to perform full-load migrations combined with ongoing replication. This means that the source database remains operational during the migration, allowing minimal disruption to applications. This feature is critical when organizations want to move workloads without downtime or loss of transactions. For successful implementation, engineers must ensure both the source and target databases are compatible and monitor replication lags to validate consistency before final cutover.

Encryption Strategies for S3 Data

Ensuring that data at rest in Amazon S3 is encrypted is not just a best practice—it is often a compliance requirement. AWS offers several server-side encryption options to choose from.

Server-side encryption using AWS Key Management Service (SSE-KMS) offers strong control because it allows users to rotate keys, audit access through CloudTrail, and manage permissions via IAM. Unlike SSE-S3, which uses AWS-managed keys without user-level control, SSE-KMS allows for more granular policy application and integration with data governance protocols.

It’s important for data engineers to architect storage systems that align with the organization’s compliance and governance needs. Encryption must be implemented transparently without affecting performance or pipeline functionality.

Simplifying Access Management with S3 Access Points

Managing access to shared S3 buckets can become complex as the number of users, applications, or teams grows. Rather than rely solely on bucket policies or multiple IAM roles, S3 Access Points allow the creation of dedicated access configurations for different use cases.

Each access point has a unique hostname and can be tailored with specific permissions that apply to the corresponding IAM roles or user groups. This simplifies permission management by isolating concerns and reducing policy errors. For instance, an analytics team might receive read-only access to a specific prefix, while a machine learning team might be granted write access to their own namespace.

Using access points also provides audit clarity, as requests are routed through these controlled interfaces, allowing better tracking and enforcement of usage patterns.

Identifying and Managing Data Skew in Distributed Workloads

In distributed computing environments such as AWS EMR or Apache Spark on Amazon EMR, an uneven distribution of data can lead to performance bottlenecks. This issue, known as data skew, occurs when some compute nodes process significantly more data than others.

Data skew can manifest when partitions are based on attributes with highly uneven value distributions. For example, if most records in a dataset share the same key, they will all be processed by a single executor. Engineers must proactively identify skew by analyzing value distributions and apply strategies such as salting keys or using broadcast joins where applicable.

Resolving data skew can lead to significant improvements in pipeline throughput and cost efficiency, particularly in large-scale analytics or transformation workflows.

Tracing Data Lineage for Transparency and Debugging

Data lineage refers to the ability to trace how data moves through a pipeline—from source to transformation to consumption. It is a critical component of modern data engineering, as it supports debugging, auditing, and quality control.

In AWS, lineage can be tracked using services such as AWS Glue, which automatically captures the data catalog and transformation steps. This information is crucial for understanding data dependencies, especially in complex pipelines where multiple jobs might alter or enrich data.

Having clear lineage enables teams to detect anomalies, identify upstream causes of data quality issues, and ensure that compliance standards are upheld by proving where data originated and how it was handled.

Data Formats and Their Analytical Value

Choosing the right data format can significantly influence the performance of downstream analytics. While traditional formats like CSV and JSON are widely used due to their simplicity and human readability, they are not ideal for large-scale querying.

Columnar formats like Parquet and ORC store data in a way that allows queries to retrieve only the necessary columns, reducing I/O and speeding up queries. This makes them especially useful in services like Amazon Athena, Redshift Spectrum, or AWS Glue. Furthermore, these formats support compression and schema evolution, enhancing both storage efficiency and pipeline adaptability.

Engineers must evaluate the analytical patterns of their organization to determine the most appropriate format for their datasets.

Structured, Semi-Structured, and Unstructured Data

Different data types require different handling techniques. Structured data is highly organized and typically resides in relational databases with predefined schemas. This includes customer records, transaction logs, and operational metrics.

Semi-structured data, such as JSON or XML, contains tags or keys but does not follow a strict schema. It often appears in logs, APIs, or telemetry data. Unstructured data, including images, videos, and free-form text, lacks inherent structure and is more difficult to analyze without preprocessing.

A skilled data engineer must be able to process all three data types, often combining them into unified analytics environments using services like AWS Glue, Lambda, and SageMaker.

Transforming Rows and Columns with SQL Techniques

Data transformation is a key aspect of data engineering. One important SQL technique is pivoting, which involves converting rows into columns to facilitate better analysis. For example, a table that tracks daily sales might be pivoted to show monthly totals in separate columns for each product.

AWS data services such as Amazon Redshift support SQL-based pivoting, allowing engineers to transform raw data into more usable formats. This is particularly useful in dashboards or reports where summarized views are needed.

A strong grasp of SQL functions, including pivot, unpivot, windowing, and aggregation, is essential for transforming datasets efficiently and enabling business insights.

Designing Scalable Ingestion Pipelines

Data ingestion marks the beginning of any analytics solution. AWS offers a variety of services—such as Kinesis, Glue, Snowball, and DataSync—to intake data at scale. Each comes with strengths depending on data volume, velocity, and variety.

For real-time streaming, Amazon Kinesis Data Streams can capture high-frequency event data. Producers push records into shards, and consumers process them using applications like Lambda, Kinesis Data Analytics, or custom consumers. Scaling shards and optimizing consumer parallelism is crucial for performance and cost control.

For bulk file transfers, such as transferring logs stored on edge servers, AWS DataSync or Snowball proves useful. These services simplify movement of terabytes to petabytes of data without overwhelming network links. Picking the right service depends on latency tolerance, bandwidth availability, and transfer cost.

Implementing ETL with AWS Glue

Once data is ingested, transformation pipelines reshape it for analytics. AWS Glue, a serverless ETL platform, simplifies the process of cataloguing, cleaning, and enriching data. The Glue Data Catalog holds schema metadata and tracks dependencies across jobs.

Glue jobs use Apache Spark under the hood. Engineers define extraction, transformation, and load steps using Python or Scala. Glue supports dynamic frames and built-in transforms for common operations like filtering, aggregating, or joining multiple datasets.

Integration with AWS Lake Formation allows fine-grained access control and auditing, ensuring different teams can safely share and consume data.

Orchestrating Complex Workflows

Modern pipelines often involve multiple execution steps. AWS Step Functions provides a visually managed state machine to orchestrate Glue jobs, Lambda functions, and external API calls. Engineers define execution paths, retry logic, error handling, and parallel execution.

Utilizing choice states and checkpoints ensures reliability and observability. Easier to manage than ad-hoc scripts, Step Functions also link well with CloudWatch logs and alarms.

Analytics with Amazon Redshift and Athena

Once data is structured and catalogued, analytics platforms come into play. Amazon Athena queries data directly in S3 using SQL—ideal for ad hoc reporting without managing infrastructure. For higher performance, columnar formats and partitioning significantly reduce query time and cost.

Amazon Redshift, especially with Redshift Serverless or managed clusters, allows for ultra-fast analytics. Best practices include using CTAS (Create Table As Select), materialized views, and workload management queues to optimize performance.

Machine Learning Integration

Data engineering and machine learning go hand-in-hand. AWS SageMaker enables building models directly with transformed data sets. ETL pipelines can produce features optimized for training, while SageMaker endpoints inject predictions back into analytics tables.

Glue, Kinesis, and Lambda can orchestrate feature pipelines, model inference triggers, and feedback loops to retrain models as fresh data arrives.

Ensuring Quality and Reliability

Data validation is critical for trustworthy pipelines. Implementing checks for schema conformance, null thresholds, and anomaly detection helps detect issues early. AWS Glue Schema Registry and DataBrew empower engineers to enforce and visualize data quality constraints.

Monitoring pipelines using CloudWatch and custom metrics helps maintain reliability. Alerts should trigger on job failures, high error rates, or delayed executions.

Managing Security and Compliance

Security is woven throughout a data pipeline. Encryption at rest and in transit protects sensitive information. AWS Key Management Service ensures keys are centralized and managed, while S3 bucket policies and IAM controls govern access.

Network-level controls—like VPC endpoints, private subnets, and security groups—help isolate data services. Audit trails via CloudTrail ensure traceability of user and service actions.

Cost Optimization for Data Workloads

Running data workflows cost-effectively requires smart choices. Serverless options—like Athena, Glue, and Redshift Serverless—help avoid infrastructure costs. For provisioned systems, using Reserved Instances or concurrency queues can reduce expenses.

Partitioning data, downsizing unnecessary resources, and cleaning stale files contributes to lower S3, compute, and storage bills.

Observability in Data Pipelines

Observability is essential in data engineering to ensure that pipelines behave as expected, deliver accurate data, and allow quick recovery from failures. AWS offers a range of tools to instrument, monitor, and debug data workflows.

Amazon CloudWatch collects metrics and logs from services like Glue, Lambda, Kinesis, and Redshift. Setting up custom metrics, dimensions, and dashboards provides visibility into job execution times, throughput, data size, and error rates. Engineers should implement alarms to catch anomalies such as job failures, out-of-bound memory usage, or increased latency.

AWS X-Ray traces distributed components across microservices, which is useful when data is ingested through APIs, Lambda layers, or container-based transformations. Engineers can use this trace information to locate performance bottlenecks or identify specific operations failing in a pipeline.

Metadata Management and Data Cataloging

Metadata enables discoverability, trust, and lineage tracking in data systems. AWS Glue Data Catalog serves as a central metadata repository, storing schema information, partitioning details, and transformation history for S3-based datasets. It integrates with services like Athena, Redshift Spectrum, and SageMaker.

When working with datasets from various departments, tagging metadata with owner, purpose, update frequency, and quality score improves governance. Engineers can automate catalog updates using crawlers or insert metadata manually via the API or console.

Lake Formation enhances the catalog with row- and column-level security, resource sharing, and audit logs. These tools together make metadata useful for both technical and business teams.

Automating Data Quality Checks

Ensuring data quality at ingestion and transformation layers is critical. AWS Glue DataBrew offers a no-code interface to analyze data profiles, detect nulls, and validate patterns before pushing the dataset into production.

For automated pipelines, engineers can integrate Python-based validations within Glue jobs or run validation scripts in AWS Lambda. Common patterns include checking for missing fields, value constraints, duplicate detection, and schema drift.

Data quality metrics should be stored and visualized using CloudWatch or QuickSight. Trend analysis on quality scores helps identify slowly degrading pipelines before they cause business impact.

Advanced Job Optimization Techniques

Glue and Redshift support several features for tuning performance and reducing cost. In Glue, engineers can optimize Apache Spark jobs by choosing the right worker type, managing partition sizes, and avoiding data skew in joins. Broadcasting smaller tables in joins and pruning unnecessary columns improves efficiency.

AWS Glue supports pushdown predicates that allow filtering at the source before data reaches the transformation stage. Using partition indexes and catalog hints can speed up job execution significantly.

In Redshift, sort keys and distribution styles impact query performance. Engineers should match sort keys with commonly used query filters and optimize vacuum frequency to reclaim disk space. Query queues can isolate workloads based on priority and usage profile.

Building Resilient Pipelines

Data pipelines must handle failures gracefully to avoid cascading errors across downstream systems. Best practices include idempotent job design, retry logic, dead-letter queues, and failure notifications.

AWS Step Functions and EventBridge help automate error recovery by triggering fallback mechanisms. If a Glue job fails, Step Functions can retry with exponential backoff, notify operators via SNS, or execute a secondary job to reprocess stale data.

Event-driven architecture improves resilience by decoupling pipeline stages. For example, ingesting data into S3 can trigger a Lambda that updates the catalog, which then triggers a Glue job. Each component can fail or succeed independently.

Troubleshooting Common Pipeline Failures

Failures in data engineering are inevitable. Knowing where and how to investigate is a vital skill. For Glue jobs, logs stored in CloudWatch are the first place to examine. Look for stack traces, out-of-memory messages, schema mismatch errors, or timeouts.

For streaming pipelines, delays in Kinesis delivery or Lambda execution failures can indicate throttling, malformed records, or service limits. CloudWatch Insights enables log filtering and correlation across services.

S3-based issues often stem from permissions, bucket policy misconfigurations, or versioning conflicts. Engineers should verify IAM roles, object access logs, and encryption settings during debugging.

Serving Data to Machine Learning and BI Tools

Data pipelines culminate in either data products or actionable insights. Engineers must design pipelines that serve multiple consumers such as BI tools, ML platforms, or APIs.

Redshift and Athena provide JDBC/ODBC endpoints that BI tools like Tableau or QuickSight can use. Engineers can create summary tables, precomputed joins, and data marts to optimize dashboard responsiveness.

For machine learning, pipelines should export data into S3 in formats like Parquet or ORC. Feature engineering pipelines may include SageMaker Feature Store integration, which allows versioned and timestamped feature access for model training and inference.

Lambda and API Gateway can be used to expose processed data via secure, scalable APIs. These APIs enable other applications to consume real-time insights or prediction results.

Managing Access and Governance at Scale

Access control is a non-negotiable aspect of any large-scale data solution. AWS Lake Formation simplifies setting up least-privilege access to S3 data. It allows table-, row-, and column-level access policies, making it easier to support multi-tenant data lakes.

IAM policies should be structured using roles for each function—data ingestion, transformation, analytics, and machine learning. By segmenting roles and using condition keys, access can be precisely controlled without hardcoding user identities.

Governance processes should also include tagging datasets with sensitivity levels, purpose, and retention policy. This metadata enables automation in data archival, deletion, and legal compliance workflows.

Data Versioning and Time Travel

One often-overlooked feature in analytics workflows is the ability to retrieve past versions of data. S3 supports versioning, which can be used alongside partitioned folders to simulate time-travel access. This is useful when rolling back datasets or performing model audits.

Delta Lake, Iceberg, and Hudi are open table formats that support transactional writes, schema evolution, and ACID compliance on S3. Glue now supports these formats, allowing engineers to build robust, versioned datasets natively in the AWS ecosystem.

Cost Monitoring and Forecasting

Understanding pipeline costs helps avoid overrun and maximize ROI. AWS Cost Explorer and Budgets allow tagging Glue jobs, S3 buckets, and Redshift clusters with identifiers like project name or department.

Engineers should estimate costs for storage (S3), compute (Glue/SageMaker), and data transfer. Using S3 lifecycle rules to archive or delete cold data helps manage storage expenses.

Auto-scaling and auto-pausing features in Redshift Serverless, Athena’s pay-per-query model, and serverless Glue jobs make billing more predictable. Cost anomaly detection should be enabled for early warnings.

Understanding the Exam Pattern and Domains

The AWS Certified Data Engineer – Associate exam evaluates candidates across multiple domains. These domains include data ingestion, transformation, storage optimization, observability, security, and data sharing. The question pattern includes multiple-choice and multiple-response formats, with scenario-based problems being predominant.

Candidates are expected to demonstrate practical decision-making skills. Questions often provide a real-world data architecture problem and ask for the most appropriate AWS service, configuration, or performance optimization. Understanding how services interact and how configurations impact cost, performance, and scalability is essential.

A balanced preparation approach should involve both conceptual clarity and hands-on skills. Knowing service limits, optimal storage formats, data partitioning strategies, and orchestration logic can make a noticeable difference during the exam.

Scenario-Based Problem Solving

A significant portion of the DEA-C01 exam revolves around scenario-based questions. These questions simulate real-world challenges such as:

Selecting the right ingestion pattern for streaming vs. batch data
Optimizing Glue job configuration to handle a skewed dataset
Troubleshooting failed Athena queries on a large partitioned table
Designing cost-effective archival storage using tiered S3 classes

Candidates should practice dissecting each scenario into its core components: source data format, volume, velocity, processing needs, security constraints, and consumer expectations. Mapping this to AWS service features is a skill that needs repetition.

For example, if a question involves ingesting data from IoT devices with sub-second latency requirements, using Kinesis Data Streams instead of SQS or S3 is a better choice. If the question asks about reducing scan cost for analytics workloads, using compressed columnar formats like Parquet and leveraging partition pruning in Athena or Redshift Spectrum is essential.

Time Management and Question Triage

The DEA-C01 exam has a strict time limit, and managing that time wisely is crucial. Candidates should aim to complete a first pass of all questions within 75% of the total time. During this pass, skip questions that involve lengthy reading or require multi-step calculations.

Flag challenging or confusing questions for review. Often, other questions in the exam provide helpful context or hints that can aid in solving a previously flagged question.

Questions involving specific services like Glue, Redshift, or Lake Formation often have fine-grained differences between answer choices. Pay close attention to keywords like “least cost,” “most performant,” “fully managed,” or “with minimum operational overhead.” These phrases can often guide you toward the best fit.

Practicing with Hands-On Labs

Theory alone is not sufficient for this certification. AWS recommends practical experience, and rightly so. Candidates should build several end-to-end pipelines using free or sandbox-tier services.

Recommended lab exercises include:

Creating a Glue job to process JSON files and write into partitioned Parquet format in S3
Setting up a Kinesis Data Stream to ingest simulated logs and use Lambda for real-time transformation
Designing an S3 data lake with lifecycle policies and integrating with Athena via Glue Catalog
Performing cross-account Redshift queries using Redshift Spectrum and sharing datasets via Lake Formation

Using the AWS Console, CLI, and SDKs interchangeably helps solidify your understanding and mirrors real-world responsibilities.

Services like CloudFormation and AWS CDK can also be used to deploy repeatable data workflows. This practice helps understand deployment aspects, versioning, rollback, and dependency resolution between services.

Key Services to Master Before the Exam

The exam focuses heavily on core services used by data engineers in the AWS ecosystem. These include but are not limited to:

Amazon S3: Versioning, lifecycle rules, storage classes, event notifications
AWS Glue: Job types (Spark vs. Ray), triggers, bookmarks, schema registry, and Data Catalog
AWS Lake Formation: Fine-grained access control, table sharing, and data governance
Amazon Kinesis: Data Streams, Firehose, and analytics integration with S3 and Lambda
Amazon Redshift: Spectrum queries, copy commands, sort/dist keys, and workload management
Amazon Athena: SQL queries over S3, partitioning, SerDe libraries, and performance tuning
AWS Lambda: Used for lightweight transformation and event-driven orchestration
Amazon CloudWatch: Monitoring jobs, log correlation, setting alarms and metrics dashboards

Each of these services comes with unique configuration options, service limits, and best practices. Candidates should understand how to tune them based on varying requirements.

Leveraging Sample Questions and Mock Exams

Mock exams are an essential part of DEA-C01 preparation. They help identify weak areas, reinforce memory, and develop exam stamina. Use mock questions that are scenario-heavy and aligned with the current exam guide.

When reviewing mock questions:

Do not just memorize answers; understand why other options are wrong
Focus on service interactions, not just standalone features
Create mind maps or diagrams for each question topic to reinforce the logic

Mock exams also expose patterns in question design. For example, questions about Glue jobs often involve decisions about worker types, retry strategy, or bookmark usage. Athena-related questions typically ask about query costs or improving performance.

Taking at least 3-4 full-length practice exams before attempting the actual test can significantly boost confidence and pacing.

Tackling Niche Topics and Hidden Concepts

While the majority of the DEA-C01 exam focuses on well-known services, niche topics occasionally appear. These include:

AWS DMS for migrating relational databases into S3 for analytics
Use of Step Functions for job orchestration with retry logic
Time travel and versioning with Apache Hudi, Iceberg, or Delta Lake
IAM condition keys and access controls based on tags
Streaming ingestion into Redshift via Materialized Views or Firehose

Candidates should at least be familiar with these services at a conceptual level. Often, a high-level understanding is sufficient to eliminate incorrect choices.

Another hidden concept is the importance of schema evolution. Many questions indirectly refer to backward- or forward-compatible schema changes. Knowing how formats like Avro and Parquet support schema evolution helps in answering such questions.

Creating a Personal Study Guide and Review Strategy

During preparation, create a personalized cheat sheet or reference guide. Group services by domain (ingestion, processing, storage, etc.), list key configurations, and capture error messages or performance tuning tips.

This personalized material becomes highly useful for final-day review. Revisiting your own summaries often creates stronger retention than reading vendor documentation.

Also, maintain a checklist of the services and features you’ve practiced hands-on. This helps ensure no major topic is missed before the exam day.

You can also create flashcards for error codes, job types, best practices, and typical service limits such as S3 request limits, Glue job timeout, or Kinesis retention period.

Maintaining Confidence on Exam Day

On exam day, confidence and a calm mindset are just as important as technical knowledge. Sleep well the night before, avoid last-minute cramming, and allocate time to arrive at the test center or prepare your remote exam setup early.

Start the exam with easy wins. If a question seems familiar, tackle it quickly and bank the time. If a scenario appears long or complex, scan for keywords and revisit it after covering the simpler ones.

Use the review feature to flag questions and verify them later. Stay focused on eliminating clearly wrong choices and don’t overthink rare edge cases unless explicitly mentioned in the scenario.

After submitting the exam, take a mental break before evaluating your performance. If you’ve followed a consistent, hands-on, and logic-oriented preparation strategy, your chances of success will be high

Final Thoughts

The AWS Certified Data Engineer – Associate certification represents a vital stepping stone for professionals aiming to validate their skills in designing and managing modern data solutions on the cloud. This certification is not just about knowing the services but about understanding how they work together to solve real-world business challenges across diverse industries and use cases.

Throughout the preparation journey, candidates should focus on deepening their conceptual knowledge while reinforcing it with hands-on practice. The exam tests your ability to make architectural decisions that are scalable, secure, cost-effective, and performance-oriented. Whether it’s designing resilient ingestion pipelines with Kinesis and Lambda, optimizing transformation jobs in Glue, enforcing fine-grained access with Lake Formation, or querying massive datasets with Redshift and Athena, the exam mirrors the kind of tasks data engineers face in production environments.

Scenario-based questions will challenge your decision-making ability. Success in this exam requires attention to detail, understanding of cloud-native design patterns, and an ability to align AWS service capabilities with specific business needs. Candidates who prioritize clarity over memorization, and logic over guesswork, will stand out.

This certification also provides long-term benefits beyond the exam. It enhances your visibility in the job market, increases your credibility within engineering teams, and helps you lead initiatives that drive data maturity in your organization. With a data-driven future unfolding rapidly, this certification signals your readiness to lead and innovate responsibly.

As with any meaningful achievement, consistent effort, smart planning, and a commitment to understanding the “why” behind AWS services will pay off. Stay curious, stay hands-on, and keep evolving your skills. The DEA-C01 exam is not the destination but a powerful milestone on a much larger journey into the world of modern data engineering on the cloud.