Stackery
FEBRUARY 21, 2019
Lambda@Edge is a compute service that allows you to write JavaScript code that executes in any of the 150+ AWS edge locations making up the Amazon CloudFront content delivery network (CDN) service. Lambda@Edge has some design limitations: Node.JS Lambda@Edge has some design limitations: Node.JS Lambda@Edge.
Apiumhub
MARCH 12, 2024
Rather than relying solely on centralized data centers, edge computing distributes computational processes closer to the source of data generation. Implementation: Deploying functions using AWS Lambda, triggered by events, eliminating the need for managing servers and allowing developers to focus on writing code.
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Xebia
JANUARY 19, 2023
A data engineer should: Know how to communicate with non-technical and technical people. Understand the cultural foundations of Agile, DevOps, and DataOps. DataOps aims to improve the release and quality of data products. The central thesis is to stream process all the data. Lastly, do not forget to back up your data.
Hypergrid
NOVEMBER 15, 2018
Support for AWS Lambda. AWS Lambda is the leading Serverless or Function-as-a-service (FaaS) service, and it gives amazing productivity and agility to developers without having to provision or monitoring any underlying infrastructure. HyperCloud 6.0 HyperCloud 6.0 Compliance Analysis and Remediation.
Xebia
MAY 21, 2024
This may include breaking monolithic applications into microservices, containerizing applications using Docker and Kubernetes, or adopting serverless computing with AWS Lambda. Companies are leveraging cloud-based data services such as managed databases, data lakes, and analytics platforms to gain insights from their data more effectively.
LaunchDarkly
JUNE 5, 2019
Early in 2016, TrueCar decided to move internet operations off premises from its data centers to the AWS cloud. Rollouts were slow, sometimes manual, and required DevOPs involvement. The solution that we devised emerged after the Amazon Web Services (AWS) launched Lambda@Edge in mid-2017.
Cloudera
JULY 26, 2021
The alleviation of infrastructure and computational constraints associated with solely on-premises data platforms; Data Products can now use different deployment models (e.g., A true data platform should enable adapting the product to new environments without material refactoring effort. Deep Java Learning, Apache Spark 3.x,
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