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AWS serverless services: Exploring your options

Dynatrace

This means you no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems. Speed is next; serverless solutions are quick to spin up or down as needed, and there are no delays due to limited storage or resource access. AWS offers four serverless offerings for storage.

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Cloudburst: stateful functions-as-a-service

The Morning Paper

Last week we looked at a function shipping solution to the problem; Cloudburst uses the more common data shipping to bring data to caches next to function runtimes (though you could also make a case that the scheduling algorithm placing function execution in locations where the data is cached a flavour of function-shipping too).

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AWS EKS Monitoring as a Self-Service with Dynatrace

Dynatrace

PostgreSQL & Elastic for data storage. REDIS for caching. With the existing notification integrations for tools such as Slack, xMatters, ServiceNow, Lambda, JIRA, you can also pro-actively notify people in case there’s a problem: Dynatrace auto detected a problem with 3 kube proxies. NGINX as an API Gateway.

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A one size fits all database doesn't fit anyone

All Things Distributed

This consistent performance is a big part of why the Snapchat Stories feature , which includes Snapchat's largest storage write workload, moved to DynamoDB. Zynga also uses ElastiCache (Memcached and Redis) in place of their self-managed equivalents for in-memory caching. Queries that used to take 30 seconds now take one second.

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Accelerating Data: Faster and More Scalable ElastiCache for Redis

All Things Distributed

Since then we’ve introduced Amazon Kinesis for real-time streaming data, AWS Lambda for serverless processing, Apache Spark analytics on EMR, and Amazon QuickSight for high performance Business Intelligence. Team Internet AG is an ad tech company with a focus on domain monetization and real-time bidding.

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Choosing a cloud DBMS: architectures and tradeoffs

The Morning Paper

Which I’m quite happy to see as my most recent data pipeline is based around Lambda, S3, and Athena, and it’s been working great for my use case. For query executors that can be frequently started and stopped the authors explore performance with cold and warm caches (where applicable), and also the horizontal and vertical scaling performance.

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Fast key-value stores: an idea whose time has come and gone

The Morning Paper

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. Oh, you mean a cache? Yes, a bit like those 2nd-level caches we were talking about earlier, e.g. Ehcache from 2003 onwards.

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