article thumbnail

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.

article thumbnail

Staying in the Zone: How DoorDash used a service mesh to manageĀ  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.

Bytes 84
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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 ?

Bytes 73
article thumbnail

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.

Cloud 95
article thumbnail

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 itā€™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.

article thumbnail

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.

article thumbnail

Processing medical images at scale on the cloud

Tweag

Thankfully, cloud-based infrastructure is now an established solution which can help do this in a cost-effective way. As a simple solution, files can be stored on cloud storage services, such as Azure Blob Storage or AWS S3, which can scale more easily than on-premises infrastructure. But as it turns out, we canā€™t use it.

Medical 60