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AWS (Amazon Web Services) offers a variety of services and tools for data engineering. These services are designed to help with the collection, storage, processing, and analysis of large volumes of data. Here are some key AWS services commonly used in data engineering:
Amazon S3 (Simple Storage Service): S3 is an object storage service that provides scalable storage for data in the form of objects. It is often used as a data lake or data storage solution for data engineering workloads.
Amazon EMR (Elastic MapReduce): EMR is a managed big data platform that allows you to process and analyze large amounts of data using popular frameworks like Apache Spark, Apache Hadoop, and Presto. It provides an easy way to spin up and manage clusters for distributed data processing.
AWS Glue: Glue is a fully managed extract, transform, and load (ETL) service. It helps automate the process of preparing and loading data for analytics. Glue offers capabilities for data cataloging, data transformation, and job scheduling.
AWS Lambda: Lambda is a serverless computing service that allows you to run code without provisioning or managing servers. It can be used for event-driven data processing or to perform small data transformations or enrichment tasks.
Amazon Redshift: Redshift is a fully managed data warehousing service that enables you to analyze large datasets using SQL queries. It is optimized for online analytical processing (OLAP) and can handle petabytes of data.
Amazon Kinesis: Kinesis is a real-time data streaming service. It allows you to collect, process, and analyze streaming data at scale. Kinesis offers various components, such as Kinesis Data Streams for real-time data ingestion, Kinesis Data Firehose for data delivery to destinations like S3 and Redshift, and Kinesis Data Analytics for real-time data processing.
AWS Data Pipeline: Data Pipeline is a web service for orchestrating and automating the movement and transformation of data across different AWS services and on-premises data sources.
Amazon Athena: Athena is an interactive query service that allows you to analyze data directly from S3 using standard SQL queries. It is a serverless service, so you don't need to manage any infrastructure.
AWS Glue DataBrew: DataBrew is a visual data preparation service that makes it easy for data engineers to clean and transform data for analytics and machine learning. It provides a no-code interface for data profiling, data cleansing, and data transformation tasks.
These are just a few examples of the AWS services available for data engineering. Depending on your specific requirements, you may also use other services like AWS Batch, AWS Step Functions, Amazon DynamoDB, and others to build robust data engineering pipelines on AWS.
Introduction to Data Engineering on AWS:
Data Storage and Data Lakes:
Data Processing with AWS Glue and AWS EMR:
Real-time Data Streaming with Amazon Kinesis:
Data Warehousing with Amazon Redshift:
Serverless Data Processing with AWS Lambda:
Data Orchestration with AWS Data Pipeline and AWS Step Functions:
Data Analytics with Amazon Athena:
Data Quality and Data Governance:
Monitoring, Logging, and Security:
Advanced Topics:
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AWS Data Engineering Rated 4.8 based on 5 reviews.
By: Devika Sharma, Rating:
I had a revelation after taking the AWS Data Engineering course. The programme stressed the value of data quality, security, and governance in addition to covering the technical components. The learning process was enhanced by the trainer sharing of real-world problems and solutions. I feel competent in creating salable, reliable data engineering on AWS after finishing this course. I strongly advise aspiring data engineers to take this course!
By: Rani Thakur, Rating:
With great hopes, I registered in the AWS Data Engineering course at Bestway technologies, and it did not dissapoint! A wide range of data engineering principles were addressed in the programme, and the hands-on labs were excellent for practical instruction. The instructor were helpful and competent, swiftly responding to all of our questions. I was comfortable building and putting AWS data engineering into use. Definitely a must-read for anyone interested in a career in data engineering!
By: Deepika Reddy, Rating:
I found the AWS Data Engineering to be of great value as a data professional. The information was current & compliant with best practises in the field. I gained the courage to use the knowledge in my projects right away thanks to the hands-on exercises with AWS tools. The trainer were gentle and entertaining, offering tailored advice as needed. The sessions on data streaming and analytics were my favorites. This course is the best option if you want to become an expert in data engineering.
By: Roshan, Rating:
The AWS Data Engineering Online Training in Hyderabad, India, led by Trainer Mr. Suresh, was exceptional! Mr. Suresh's expertise and teaching style were outstanding. The course content was comprehensive, and Mr. Suresh's guidance during practical exercises was invaluable. This training has significantly elevated my skills in AWS Data Engineering.
By: Abhishek, Rating:
I can't speak highly enough of the AWS Data Engineering Online Training in Hyderabad, India, with Trainer Mr. Suresh. His knowledge and passion for AWS Data Engineering shone through in every session. The course content was well-structured, and Mr. Suresh's real-world insights made a significant difference. This training has prepared me exceptionally well for AWS Data Engineering projects and certifications. Highly recommended!