Covers data lakes, ETL, Glue, EMR, Redshift, Kinesis, streaming, storage, security, and cost optimization
π₯ 984 students
π September 2025 update
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- Course Overview
- The “AWS Data Engineer Associate (DEA-C01): 1500 Questions” course is the ultimate preparation for the AWS Certified Data Engineer Associate (DEA-C01) exam. With 1500 meticulously crafted questions, it offers unparalleled practice to test knowledge, solidify understanding, and build confidence across all certification domains.
- This course provides a rigorous, comprehensive review, directly aligned with the official DEA-C01 exam blueprint. It ensures thorough preparation for diverse questions and scenarios covering essential areas: designing robust data lakes, implementing efficient ETL processes, leveraging AWS Glue for data integration, and managing big data with AWS EMR.
- Further topics include advanced data warehousing with Amazon Redshift, real-time ingestion and processing with Amazon Kinesis, architecting scalable streaming solutions, diverse storage strategies, implementing stringent security measures, and optimizing for cost efficiency on AWS.
- Aimed at aspiring AWS Data Engineers, current data professionals, or anyone targeting the DEA-C01 certification, this course delivers a structured, question-driven learning path. The “September 2025 update” guarantees current content, reflecting the latest AWS service updates and exam objectives, making it an invaluable preparation tool.
- Its core objective is to equip you with the strategic thinking and practical knowledge needed to proficiently design, implement, monitor, and maintain scalable data pipelines and architectures on AWS, preparing you for both the exam and real-world challenges.
- Requirements / Prerequisites
- Foundational understanding of core AWS services (EC2, S3, VPC) is highly recommended. Prior exposure to the AWS Management Console can aid conceptualization.
- Basic knowledge of data warehousing, data lakes, and general ETL processes provides a solid starting point.
- Proficiency in SQL is essential, as data manipulation and querying are central to many data engineering tasks.
- A dedicated mindset for rigorous exam preparation is crucial, given the volume of 1500 questions and need to understand rationales.
- An active AWS account for parallel hands-on experimentation can significantly enhance learning, though not strictly mandatory for course content.
- Skills Covered / Tools Used
- Data Lake Architecture: Mastering Amazon S3 for scalable data lakes, including storage classes, lifecycle policies, and integration with AWS Glue Data Catalog.
- ETL & Data Transformation: Proficient use of AWS Glue (ETL jobs, Crawlers, DataBrew), orchestration with AWS Step Functions, and serverless processing via AWS Lambda.
- Big Data Processing: In-depth knowledge of Amazon EMR, including cluster deployment, instance types, and working with Apache Spark, Hive, Presto, and Hadoop.
- Data Warehousing: Expertise in Amazon Redshift for analytical querying, covering cluster configuration, WLM, performance tuning, Redshift Spectrum, and security.
- Real-time Streaming & Analytics: Implementing real-time pipelines using Amazon Kinesis services (Data Streams, Firehose, Data Analytics for SQL/Flink).
- Diverse Data Storage: Understanding and selecting appropriate AWS storage options like Amazon EBS, RDS, and DynamoDB for various requirements.
- Security & Compliance: Implementing robust security with AWS IAM, encryption (KMS, S3), VPC security, and ensuring data governance.
- Cost Optimization: Strategies for optimizing AWS data engineering costs through efficient resource provisioning, right-sizing, Reserved Instances, S3 lifecycle policies, and using AWS Cost Explorer.
- Monitoring & Logging: Utilizing Amazon CloudWatch for service health/performance, and AWS CloudTrail for auditing API calls and user activity.
- Exam Strategy & Scenario Analysis: Developing strong problem-solving skills for scenario-based questions, applying AWS best practices, and designing scalable, fault-tolerant solutions.
- Benefits / Outcomes
- Achieve Certification Readiness: Gain unparalleled readiness for the AWS Certified Data Engineer Associate (DEA-C01) exam, significantly boosting your chances of passing with extensive practice.
- Validate Expertise & Career Growth: Earning DEA-C01 certification validates specialized skills, positioning you for enhanced career opportunities and progression in data engineering roles on AWS.
- Deepened AWS Data Service Understanding: Develop profound understanding of how AWS data services (Glue, EMR, Redshift, Kinesis, S3) integrate to form comprehensive and effective data solutions.
- Practical Problem-Solving Skills: Hone your ability to analyze complex requirements, troubleshoot issues, and design optimal architectures balancing scalability, performance, security, and cost-efficiency.
- Confidence in Implementation: Build significant confidence in implementing AWS data engineering best practices, from ingestion to consumption, ensuring reliable data infrastructures.
- Current and Relevant Knowledge: Benefit from up-to-date content reflecting the latest AWS service offerings and exam objectives, ensuring your knowledge base remains current.
- PROS
- Extensive Question Bank: Offers exceptionally thorough preparation with 1500 exam-style questions covering every DEA-C01 domain.
- Exam-Centric Focus: Designed purely for certification success; questions mirror actual exam format and difficulty for targeted study.
- Up-to-Date Content: “September 2025 update” ensures material aligns with the latest AWS services and exam objectives.
- Comprehensive Coverage: Addresses all critical AWS data services and concepts vital for the Data Engineer Associate role.
- Self-Paced Learning: Provides flexibility to study at your own pace, allowing repeated review and reinforcement of understanding.
- CONS
- Limited Hands-on Labs: As a question-based course, it may not include extensive practical lab exercises, potentially requiring learners to seek supplementary hands-on experience.
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