AWS Data Engineering Online Training | Data Engineer Training in Hyderabad

Different services and concepts of AWS data engineering

AWS Data Engineering and Data Analytics services offer a comprehensive suite of tools for efficiently managing and extracting insights from large datasets. In the realm of data engineering, services like Amazon S3 provide a robust foundation for storing vast amounts of raw data, while AWS Glue simplifies the ETL process by automating data preparation and loading tasks. Amazon Web Services (AWS) offers a variety of services and concepts that are relevant to data engineering. AWS provides scalable and flexible solutions for handling and processing large volumes of data. Here are some key AWS data engineering services and concepts

AWS Data Engineering Online Training

1. Amazon S3 (Simple Storage Service):

Concept: Amazon S3 is an object storage service that allows you to store and retrieve any amount of data at any time.

Use in Data Engineering: S3 is often used as a data lake for storing raw data before processing. It provides high durability and availability for large-scale data storage.                                                                                 AWS Data Engineering

2. Amazon Redshift:

Concept: Amazon Redshift is a fully managed, petabyte-scale data warehouse service.

Use in Data Engineering: Redshift is commonly used for analytical processing and querying of large datasets. It is optimized for high-performance analysis.

3. AWS Glue:

Concept: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for you to prepare and load your data for analysis.

Use in Data Engineering: Glue can automate the ETL process, making it easier to transform and prepare data for analysis. It supports various data sources and destinations.  

4. Amazon EMR (Elastic Map Reduce):

Concept: Amazon EMR is a cloud-based big data platform that allows processing of large data sets using popular frameworks such as Apache Spark and Apache Hardtop.           

Use in Data Engineering: EMR is used for distributed processing of large datasets. It is suitable for a wide range of data processing tasks, including data transformation and analysis.             Data Engineer Training in Hyderabad

5. AWS Data Pipeline:

Concept: AWS Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premises data sources.

Use in Data Engineering: Data Pipeline helps you create, schedule, and manage data workflows, making it easier to move and process data across different services.

6. Amazon Kinesis:

Concept: Amazon Kinesis is a platform for streaming data on AWS, offering capabilities for real-time data ingestion and processing.

Use in Data Engineering: Kinesis is suitable for scenarios where you need to ingest and process real-time streaming data. It includes services like Kinesis Streams for handling high-throughput data streams.

7. Amazon Aurora:

Concept: Amazon Aurora is a MySQL and PostgreSQL-compatible relational database engine.

Use in Data Engineering: Aurora can be used as a scalable and high-performance database for applications, including those involving data engineering tasks.

8. AWS Lambda:

Concept: AWS Lambda is a server less computing service that lets you run your code without provisioning or managing servers.

Use in Data Engineering: Lambda functions can be used to execute code in response to events, making it useful for tasks like data transformation and processing triggered by events.                Data Analyst Course in Hyderabad

9. Amazon Dynamo DB:

 Concept: Amazon Dynamo DB is a fully managed nosily database service.

Use in Data Engineering: Dynamo DB can be used to store and retrieve semi-structured data and is often used in scenarios where low-latency and high-throughput access to data is required.

These are just a few examples, and AWS provides a wide range of services to cater to various data engineering needs, from storage and processing to analytics and machine learning. The choice of services depends on the specific requirements of your data engineering workflows.

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