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Choosing a cloud DBMS: architectures and tradeoffs

The Morning Paper

Choosing a cloud DBMS: architectures and tradeoffs Tan et al., As it is infeasible to test every OLAP system runnable on AWS, we chose widely-used systems that represented a variety of architectures and cost models. VLDB’19. The design space. Key findings.

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AWS EKS Monitoring as a Self-Service with Dynatrace

Dynatrace

PostgreSQL & Elastic for data storage. REDIS for caching. MaaSS for Cloud Architects: Deployment and Architecture Validations. Validate correct architecture, configuration and deployment by looking at Service Flow! Their technology stack looks like this: Spring Boot-based Microservices. NGINX as an API Gateway.

AWS 127
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AWS serverless services: Exploring your options

Dynatrace

To get a better understanding of AWS serverless, we’ll first explore the basics of serverless architectures, review AWS serverless offerings, and explore common use cases. Serverless architecture: A primer. Serverless architecture shifts application hosting functions away from local servers onto those managed by providers.

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In-Stream Big Data Processing

Highly Scalable

The pipelines can be stateful and the engine’s middleware should provide a persistent storage to enable state checkpointing. In many cases join is performed on a finite time window or other type of buffer e.g. LFU cache that contains most frequent tuples in the stream. Marz, “Big Data Lambda Architecture”. Jacobsen and R.

Big Data 154
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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).

Lambda 98
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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.

Cache 79
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Content Management Systems of the Future: Headless, JAMstack, ADN and Functions at the Edge

Abhishek Tiwari

When it comes to innovation, most of CMS solutions are constrained by their legacy architecture (read strong coupling between content management and content presentation) which makes it difficult to serve content to new types of emerging channels such as apps and devices. Eventually, we decided to move them to Jekyll.

Systems 63