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Architecture Modernization Enabling Teams (AMET)

Strategic Tech

This article was co-authored by Eduardo da Silva and Nick Tune based on our individual and collective experiences. FThis article describes a pattern we have observed and applied in multi-team-scope architecture modernization initiatives, the Architecture Modernization Enabling Team (AMET).

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Ensure safe and secure releases at scale by providing Golden Paths

Dynatrace

Golden Paths for rapid product development Modern software development aims to streamline development and delivery processes to ensure fast releases to the market without violating quality and security standards. The new functionality has already been released and is available for your use.

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Full visibility into your serverless applications with AI-powered Azure Functions monitoring (GA)

Dynatrace

As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Dynatrace is thrilled to announce the General Availability of support for both the 2.x It allows for the breaking up of heavy monolithic architectures into multiple serverless “functions.”

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Full visibility into your serverless applications with AI-powered Azure Functions monitoring (GA)

Dynatrace

As companies strive to innovate and deliver faster, modern software architecture is evolving at near the speed of light. Dynatrace is thrilled to announce the General Availability of support for both the 2.x It allows for the breaking up of heavy monolithic architectures into multiple serverless “functions.”

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Data Consistency in Apache Cassandra — Part 2

Software Architecture

Source: [link] Cassandra has tunable consistency which means that not only on the database level, you can tune the immediate and eventual consistency of your data per query/operation by setting the read CL (consistency level) and write CL. You’d never want to set write CL = ALL, read CL = ALL. Comments and thoughts welcome.

Tuning 100
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Data Consistency in Apache Cassandra — Part 1

Software Architecture

You can tune the write consistency for performance (by setting the write CL as ONE) or immediate consistency for critical piece of data (by setting the write CL as ALL) Following is how it works: A client sends a write request to the coordinator. For all these benefits, the price you pay is lower consistency. Comments and thoughts welcome.

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5 tips for architecting fast data applications

O'Reilly Software

Considerations for setting the architectural foundations for a fast data platform. Google was among the pioneers that created “web scale” architectures to analyze the massive data sets that resulted from “crawling” the web that gave birth to Apache Hadoop, MapReduce, and NoSQL databases. Back in the days of Web 1.0, At least once?