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The Roots of Today's Modern Backend Engineering Practices

The Pragmatic Engineer

It’s fascinating how what is considered “modern” for backend practices keep evolving over time; back in the 2000s, virtualizing your servers was the cutting-edge thing to do; while around 2010 if you onboarded to the cloud, you were well ahead of the pack. Joshua has remained technical while working as an executive.

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Netflix Cloud Packaging in the Terabyte Era

Netflix Tech

As an example, cloud-based post-production editing and collaboration pipelines demand a complex set of functionalities, including the generation and hosting of high quality proxy content. It is worth pointing out that cloud processing is always subject to variable network conditions.

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Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

Netflix Tech

By Torio Risianto, Bhargavi Reddy, Tanvi Sahni, Andrew Park Continue reading on Netflix TechBlog ».

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Streaming Big Data Files from Cloud Storage

Towards Data Science

In this post we consider the case in which our data application requires access to one or more large files that reside in cloud object storage. This continues a series of posts on the topic of efficient ingestion of data from the cloud (e.g., Multi-part downloading is critical for pulling large files from the cloud in a timely fashion.

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A Definitive Guide to Using BigQuery Efficiently

Towards Data Science

Like a dragon guarding its treasure, each byte stored and each query executed demands its share of gold coins. Join as we journey through the depths of cost optimization, where every byte is a precious coin. It is also possible to set a maximum for the bytes billed for your query. Photo by Konstantin Evdokimov on Unsplash ?

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Staying in the Zone: How DoorDash used a service mesh to manage  data transfer, reducing hops and cloud spend

DoorDash Engineering

This led us to use a number of observability tools, including VPC flow logs , ebpf agent metrics , and Envoy networking bytes metrics to rectify the situation. Lessons learned Some of the key discoveries made during our journey include: Cloud service provider data transfer pricing is more complex than it initially seems.

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The Stream Processing Model Behind Google Cloud Dataflow

Towards Data Science

Google Cloud Dataflow is a unified processing service from Google Cloud; you can think it’s the destination execution engine for the Apache Beam pipeline. Triggering based on data-arriving characteristics such as counts, bytes, data punctuations, pattern matching, etc. Triggering at completion estimates such as watermarks.