AWS Certified Data Engineer - Associate

Elevate your career in data engineering with our specialized AWS Certified Data Engineer - Associate course. Delve into the intricacies of data processing, analysis, and visualization on the AWS platform. Our comprehensive curriculum is designed to prepare you for the AWS Certified Data Engineer - Associate certification exam, ensuring you have the skills and expertise to excel in the field of data engineering. You're a seasoned data professional or looking to transition into the field, our course equips you with the knowledge and certification to succeed in the rapidly evolving world of big data. Enroll now and unlock the full potential of data engineering on AWS!

|| What will I learn?

  • Implement ETL processes for data transformation and integration using AWS Glue, AWS Data Pipeline, or custom scripts.
  • Analyze and visualize data using AWS analytics services such as Amazon Redshift, Amazon Athena, and Amazon QuickSight.
  • Implement big data processing and analytics solutions using AWS services like Amazon EMR and AWS Lambda.
  • Understand data engineering principles, concepts, and methodologies.
  • Design and implement data processing architectures using AWS services.
  • Build and manage data lakes on AWS using services like Amazon S3 and AWS Glue.

|| What will I learn?

  • Implement ETL processes for data transformation and integration using AWS Glue, AWS Data Pipeline, or custom scripts.
  • Analyze and visualize data using AWS analytics services such as Amazon Redshift, Amazon Athena, and Amazon QuickSight.
  • Implement big data processing and analytics solutions using AWS services like Amazon EMR and AWS Lambda.
  • Understand data engineering principles, concepts, and methodologies.
  • Design and implement data processing architectures using AWS services.
  • Build and manage data lakes on AWS using services like Amazon S3 and AWS Glue.

|| Requirements

  • Basic understanding of data engineering concepts and principles.
  • Proficiency in at least one programming language (Python, Java, Scala, etc.).
  • Experience with data analytics and visualization tools (optional but beneficial).
  • Familiarity with AWS services and cloud computing concepts.

|| Requirements

  • Basic understanding of data engineering concepts and principles.
  • Proficiency in at least one programming language (Python, Java, Scala, etc.).
  • Experience with data analytics and visualization tools (optional but beneficial).
  • Familiarity with AWS services and cloud computing concepts.

    • Introduction to AWS Big Data
    • Overview of Big Data concepts and AWS Big Data services.
    • Understanding the AWS Shared Responsibility Model for data management and security.
    • Overview of AWS Free Tier and AWS Billing for Big Data services.


    • Data Collection and Ingestion
    • Introduction to Amazon S3 (Simple Storage Service) for data storage and ingestion.
    • Implementing data ingestion pipelines using AWS Glue, AWS Data Pipeline, or AWS Batch.
    • Real-time data ingestion using Amazon Kinesis streams and firehose.


    • Data Storage and Processing
    • Configuring and managing Amazon S3 buckets for data storage and archiving.
    • Implementing data lake architectures using Amazon S3 and AWS Glue.
    • Processing and analyzing data using AWS Big Data services such as Amazon EMR (Elastic MapReduce) and Amazon Redshift.


    • Data Transformation and ETL
    • Building and orchestrating ETL (Extract, Transform, Load) workflows using AWS Glue.
    • Writing and executing Spark and PySpark scripts for data transformation.
    • Leveraging AWS Glue Data Catalog for metadata management and schema inference.


    • Data Analytics and Visualization
    • Implementing data analytics solutions using Amazon Athena and Amazon QuickSight.
    • Creating and optimizing SQL queries for querying data in Amazon S3.
    • Designing dashboards and visualizations to gain insights from data using Amazon QuickSight.


    • Real-time Data Processing
    • Setting up and configuring Amazon Kinesis Data Streams for real-time data processing.
    • Implementing stream processing applications using Amazon Kinesis Data Analytics and AWS Lambda.
    • Integrating real-time analytics with other AWS services for event-driven architectures.


    • Data Security and Compliance
    • Implementing encryption at rest and in transit for data security using AWS KMS and AWS Certificate Manager.
    • Configuring access control and permissions for AWS Big Data services using IAM policies and resource policies.
    • Implementing data governance and compliance controls using AWS services like AWS Config and AWS CloudTrail.


    • Machine Learning and AI Integration
    • Integrating machine learning models with AWS Big Data solutions using Amazon SageMaker.
    • Implementing predictive analytics and anomaly detection using Amazon SageMaker and AWS Glue.
    • Building and deploying machine learning pipelines for data processing and analysis.


    • Data Monitoring and Management
    • Monitoring and logging data ingestion and processing workflows using AWS CloudWatch and AWS CloudTrail.
    • Setting up alarms and notifications for data quality and performance metrics.
    • Managing and optimizing AWS Big Data infrastructure for cost efficiency and performance.


    • Data Governance and Compliance
    • Implementing data governance policies and best practices for data management and compliance.
    • Ensuring data privacy and compliance with regulatory requirements such as GDPR and HIPAA.
    • Auditing and reporting on data access and usage using AWS services like AWS Config and AWS CloudTrail.


    • Case Studies and Best Practices
    • Analyzing real-world use cases and architectural patterns for AWS Big Data solutions.
    • Best practices for designing scalable, reliable, and cost-effective Big Data architectures on AWS.
    • Review of sample questions and exam preparation tips.

    • Setting Up AWS Data Lake
    • Create an Amazon S3 bucket to serve as the data lake storage.
    • Configure AWS Glue crawlers to catalog data stored in the S3 bucket.
    • Use AWS Glue to create a data catalog and define schema for different data sources.


    • Data Ingestion and Processing with AWS Glue
    • Implement a data ingestion pipeline using AWS Glue ETL jobs to extract, transform, and load data into the data lake.
    • Schedule AWS Glue crawlers and jobs to run at specific intervals for incremental data updates.
    • Monitor and troubleshoot AWS Glue jobs using AWS Management Console and CloudWatch logs.


    • Real-time Data Streaming with Amazon Kinesis
    • Set up an Amazon Kinesis Data Stream to ingest real-time data from simulated sources (e.g., IoT devices).
    • Implement Kinesis Data Analytics applications to process and analyze streaming data in real-time.
    • Integrate AWS Lambda functions with Kinesis Data Streams for real-time data processing and transformation.


    • Data Analysis and Visualization with Amazon Athena and QuickSight
    • Create a database and tables in Amazon Athena to query data stored in the S3 data lake.
    • Write SQL queries to analyze and aggregate data using Athena.
    • Design and publish dashboards using Amazon QuickSight to visualize insights derived from data queries.


    • Big Data Processing with Amazon EMR
    • Launch an Amazon EMR cluster with Hadoop, Spark, or Presto for distributed data processing.
    • Submit Spark or Hive jobs to the EMR cluster to process large datasets stored in the S3 data lake.
    • Monitor EMR cluster performance and resource utilization using AWS Management Console and CloudWatch metrics.


    • Data Orchestration and Workflow Automation
    • Create and configure AWS Data Pipeline to orchestrate data processing workflows across different AWS services.
    • Define pipeline activities, dependencies, and schedules using AWS Data Pipeline.
    • Monitor pipeline execution and troubleshoot errors using AWS Management Console and CloudWatch logs.


    • Data Security and Compliance
    • Implement encryption at rest and in transit for data stored in the S3 data lake using AWS KMS and SSL/TLS.
    • Configure access control and permissions for AWS Glue, Kinesis, Athena, and other Big Data services using IAM policies.
    • Implement data governance and compliance controls to ensure data privacy and regulatory compliance.


    • Optimizing Big Data Workloads
    • Implement cost optimization strategies for AWS Big Data services such as EMR, Glue, and Athena.
    • Analyze data usage patterns and performance metrics to right-size resources and optimize costs.
    • Implement data lifecycle management policies to manage data retention and archival in the S3 data lake.


    • Machine Learning Integration
    • Integrate Amazon SageMaker with AWS Glue to build and deploy machine learning models for data processing and analysis.
    • Use SageMaker notebooks for exploratory data analysis and model training on datasets stored in the S3 data lake.
    • Evaluate model performance and accuracy using SageMaker built-in algorithms and metrics.


    • Data Monitoring and Management
    • Set up CloudWatch alarms and metrics for monitoring AWS Big Data services.
    • Configure automated alerts and notifications for critical data processing events and errors.
    • Implement log aggregation and analysis using CloudWatch Logs and CloudWatch Insights for data troubleshooting and debugging.

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|| Frequently asked question

The AWS Certified Data Engineer - Associate course is designed to prepare individuals for the AWS Certified Data Engineer - Associate certification exam. It covers key concepts, tools, and best practices for designing, building, and maintaining data processing systems on the AWS platform.

Candidates should have basic knowledge of data analytics, databases, and AWS services. It's recommended to have achieved the AWS Certified Solutions Architect - Associate or AWS Certified Developer - Associate certification before attempting the Data Engineer - Associate certification.

Completion of the course does not automatically grant certification. Candidates must pass the AWS Certified Data Engineer - Associate certification exam to earn the certification credential.

The AWS Certified Data Engineer - Associate certification validates skills in designing and implementing data solutions on AWS, enhancing career opportunities for professionals in roles such as data engineer, database administrator, data analyst, or cloud architect. Additionally, the comprehensive curriculum of the course prepares students for further education or specialization in advanced AWS certifications or data engineering methodologies.

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