AWS – Data Engineering – AI school

AWS – Data Engineering

Categories AWS

About Course

AWS Data Engineering encompasses the design, implementation, and management of data solutions using a variety of AWS services and tools, enabling organizations to process, store, and analyze data efficiently and at scale in the cloud.

Course Curriculum

Data Storage
Understanding and utilizing Amazon storage services like Amazon S3, Amazon Redshift, and Amazon DynamoDB for efficient data storage.

Amazon S3 (Simple Storage Service)

00:00
Amazon EBS (Elastic Block Store)

00:00
Amazon EFS (Elastic File System)

00:00

Databases

Data Ingestion
Strategies and tools for collecting and importing data from various sources into the Amazon ecosystem.

Data Transformation
Techniques and tools for transforming raw data into a format suitable for analysis and processing, such as using AWS Glue, Apache Spark, or AWS Data Pipeline.

Data Catalogs
Building and managing data catalogs using tools like AWS Glue Data Catalog to provide a centralized view of data assets.

Data Modeling
Designing and implementing data models to support the analytical and reporting needs of the organization, collaborating with data architects and analysts

Data Warehousing
Building and optimizing data warehouses using Amazon Redshift, including schema design, data distribution, and query performance tuning.

Streaming Data Processing:
Handling and processing real-time streaming data using frameworks like Apache Kafka, Amazon Kinesis, or AWS Lambda.

Big Data Technologies
Leveraging Amazon EMR (Elastic MapReduce), Amazon Athena, and AWS Glue to process and analyze large volumes of data.

Data Pipeline Orchestration
Designing and managing data pipelines using AWS Data Pipeline or AWS Step Functions for efficient data flow across various stages.

Monitoring and Performance Optimization:
Monitoring data pipelines, storage, and processing systems, and optimizing performance using monitoring tools and techniques.

Machine Learning Integration:
Integrating machine learning capabilities into data pipelines using services like Amazon SageMaker or AWS Glue for advanced data analysis and predictions.

Data Versioning and Lineage:
Tracking data versioning and lineage information to understand the origin and impact of data changes using tools like AWS Glue DataBrew or third-party solutions.

Data Security:
Implementing data security measures such as encryption, access controls, and data classification using AWS services like AWS Key Management Service (KMS) or AWS Identity and Access Management (IAM).

Data Migration:
Planning and executing data migration strategies to move data from on-premises environments to AWS using services like AWS Database Migration Service (DMS).

Serverless Data Processing:
Leveraging AWS Lambda, AWS Glue, and other serverless services for data processing tasks without the need for infrastructure management.

Data Archiving and Backup:
Implementing data archiving and backup strategies using services like Amazon Glacier, AWS Backup, or other AWS storage solutions.

Student Ratings & Reviews

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AWS – Data Engineering

About Course

What I will learn?

Course Curriculum

Data Storage
Understanding and utilizing Amazon storage services like Amazon S3, Amazon Redshift, and Amazon DynamoDB for efficient data storage.

Amazon S3 (Simple Storage Service)

Amazon EBS (Elastic Block Store)

Amazon EFS (Elastic File System)

Databases

Amazon RDS (Relational Database Service)

Amazon DynamoDB

Amazon Aurora:

Amazon Redshift:

Data Ingestion
Strategies and tools for collecting and importing data from various sources into the Amazon ecosystem.

Data Transformation
Techniques and tools for transforming raw data into a format suitable for analysis and processing, such as using AWS Glue, Apache Spark, or AWS Data Pipeline.

Data Catalogs
Building and managing data catalogs using tools like AWS Glue Data Catalog to provide a centralized view of data assets.

Data Modeling
Designing and implementing data models to support the analytical and reporting needs of the organization, collaborating with data architects and analysts

Data Warehousing
Building and optimizing data warehouses using Amazon Redshift, including schema design, data distribution, and query performance tuning.

Streaming Data Processing:
Handling and processing real-time streaming data using frameworks like Apache Kafka, Amazon Kinesis, or AWS Lambda.

Big Data Technologies
Leveraging Amazon EMR (Elastic MapReduce), Amazon Athena, and AWS Glue to process and analyze large volumes of data.

Data Pipeline Orchestration
Designing and managing data pipelines using AWS Data Pipeline or AWS Step Functions for efficient data flow across various stages.

Monitoring and Performance Optimization:
Monitoring data pipelines, storage, and processing systems, and optimizing performance using monitoring tools and techniques.

Machine Learning Integration:
Integrating machine learning capabilities into data pipelines using services like Amazon SageMaker or AWS Glue for advanced data analysis and predictions.

Data Versioning and Lineage:
Tracking data versioning and lineage information to understand the origin and impact of data changes using tools like AWS Glue DataBrew or third-party solutions.

Data Security:
Implementing data security measures such as encryption, access controls, and data classification using AWS services like AWS Key Management Service (KMS) or AWS Identity and Access Management (IAM).

Data Migration:
Planning and executing data migration strategies to move data from on-premises environments to AWS using services like AWS Database Migration Service (DMS).

Serverless Data Processing:
Leveraging AWS Lambda, AWS Glue, and other serverless services for data processing tasks without the need for infrastructure management.

Data Archiving and Backup:
Implementing data archiving and backup strategies using services like Amazon Glacier, AWS Backup, or other AWS storage solutions.

AWS – Data Engineering

About Course

What I will learn?

Course Curriculum

Data Storage Understanding and utilizing Amazon storage services like Amazon S3, Amazon Redshift, and Amazon DynamoDB for efficient data storage.

Amazon S3 (Simple Storage Service)

Amazon EBS (Elastic Block Store)

Amazon EFS (Elastic File System)

Databases

Amazon RDS (Relational Database Service)

Amazon DynamoDB

Amazon Aurora:

Amazon Redshift:

Data Ingestion Strategies and tools for collecting and importing data from various sources into the Amazon ecosystem.

Data Transformation Techniques and tools for transforming raw data into a format suitable for analysis and processing, such as using AWS Glue, Apache Spark, or AWS Data Pipeline.

Data Catalogs Building and managing data catalogs using tools like AWS Glue Data Catalog to provide a centralized view of data assets.

Data Modeling Designing and implementing data models to support the analytical and reporting needs of the organization, collaborating with data architects and analysts

Data Warehousing Building and optimizing data warehouses using Amazon Redshift, including schema design, data distribution, and query performance tuning.

Streaming Data Processing: Handling and processing real-time streaming data using frameworks like Apache Kafka, Amazon Kinesis, or AWS Lambda.

Big Data Technologies Leveraging Amazon EMR (Elastic MapReduce), Amazon Athena, and AWS Glue to process and analyze large volumes of data.

Data Pipeline Orchestration Designing and managing data pipelines using AWS Data Pipeline or AWS Step Functions for efficient data flow across various stages.

Monitoring and Performance Optimization: Monitoring data pipelines, storage, and processing systems, and optimizing performance using monitoring tools and techniques.

Machine Learning Integration: Integrating machine learning capabilities into data pipelines using services like Amazon SageMaker or AWS Glue for advanced data analysis and predictions.

Data Versioning and Lineage: Tracking data versioning and lineage information to understand the origin and impact of data changes using tools like AWS Glue DataBrew or third-party solutions.

Data Security: Implementing data security measures such as encryption, access controls, and data classification using AWS services like AWS Key Management Service (KMS) or AWS Identity and Access Management (IAM).

Data Migration: Planning and executing data migration strategies to move data from on-premises environments to AWS using services like AWS Database Migration Service (DMS).

Serverless Data Processing: Leveraging AWS Lambda, AWS Glue, and other serverless services for data processing tasks without the need for infrastructure management.

Data Archiving and Backup: Implementing data archiving and backup strategies using services like Amazon Glacier, AWS Backup, or other AWS storage solutions.

Student Ratings & Reviews

Data Storage
Understanding and utilizing Amazon storage services like Amazon S3, Amazon Redshift, and Amazon DynamoDB for efficient data storage.

Data Ingestion
Strategies and tools for collecting and importing data from various sources into the Amazon ecosystem.

Data Transformation
Techniques and tools for transforming raw data into a format suitable for analysis and processing, such as using AWS Glue, Apache Spark, or AWS Data Pipeline.

Data Catalogs
Building and managing data catalogs using tools like AWS Glue Data Catalog to provide a centralized view of data assets.

Data Modeling
Designing and implementing data models to support the analytical and reporting needs of the organization, collaborating with data architects and analysts

Data Warehousing
Building and optimizing data warehouses using Amazon Redshift, including schema design, data distribution, and query performance tuning.

Streaming Data Processing:
Handling and processing real-time streaming data using frameworks like Apache Kafka, Amazon Kinesis, or AWS Lambda.

Big Data Technologies
Leveraging Amazon EMR (Elastic MapReduce), Amazon Athena, and AWS Glue to process and analyze large volumes of data.

Data Pipeline Orchestration
Designing and managing data pipelines using AWS Data Pipeline or AWS Step Functions for efficient data flow across various stages.

Monitoring and Performance Optimization:
Monitoring data pipelines, storage, and processing systems, and optimizing performance using monitoring tools and techniques.

Machine Learning Integration:
Integrating machine learning capabilities into data pipelines using services like Amazon SageMaker or AWS Glue for advanced data analysis and predictions.

Data Versioning and Lineage:
Tracking data versioning and lineage information to understand the origin and impact of data changes using tools like AWS Glue DataBrew or third-party solutions.

Data Security:
Implementing data security measures such as encryption, access controls, and data classification using AWS services like AWS Key Management Service (KMS) or AWS Identity and Access Management (IAM).

Data Migration:
Planning and executing data migration strategies to move data from on-premises environments to AWS using services like AWS Database Migration Service (DMS).

Serverless Data Processing:
Leveraging AWS Lambda, AWS Glue, and other serverless services for data processing tasks without the need for infrastructure management.

Data Archiving and Backup:
Implementing data archiving and backup strategies using services like Amazon Glacier, AWS Backup, or other AWS storage solutions.