Storage Best Practices for Data and Analytics Applications: AWS Whitepaper
In this white paper, you'll get an in-depth look at best practices and AWS storage solutions to help you level up your data storage and analytics capabilities. Feel free to reach out to us today if you'd like to learn more.
A data lake is an architectural approach that allows organizations to store all types of data in a centralized repository. This enables categorization, cataloging, securing, and analyzing data by various users and tools. Unlike traditional data storage solutions that often create silos, a data lake supports structured, semi-structured, and unstructured data, allowing for more comprehensive and efficient analytics.
Why use Amazon S3 for data lakes?
Amazon S3 provides an optimal foundation for data lakes due to its virtually unlimited scalability and high durability, designed to offer 99.999999999% durability. It allows organizations to store data in its native format, decouples storage from compute, and integrates seamlessly with various AWS services for data ingestion, processing, and security.
How can data be ingested into a data lake?
Data can be ingested into a data lake using several methods, including real-time streaming with Amazon Kinesis Data Firehose, which automatically scales to match data volume and can transform data before storage. Other methods include AWS Glue for ETL processes and AWS DataSync for transferring data from on-premises storage.
Storage Best Practices for Data and Analytics Applications: AWS Whitepaper
published by Enterprise Technology International
ETI is your trusted partner in navigating the complexities of digital transformation. Our cutting-edge solutions spanning AI, Cloud, Mobility, IoT, Security, Hybrid Work, and Technology as a Service (TaaS), help businesses accelerate innovation while ensuring scalability and security.
Sign in with LinkedIn