Remove tag microservices
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Data News — Week 24.02

Christophe Blefari

dbt meta tag — A list of the companies habing product features depending on the meta tag. Netflix video processing rebuilt with microservices. Unit testing dbt models — Using a dbt-unit-testing package Matthieu showcases how you can easily test your models. It shows how deeply dbt change the data world.

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How DoorDash Migrated from StatsD to Prometheus

DoorDash Engineering

Challenges Faced With StatsD StatsD was a great asset for our early observability needs, but we began encountering constraints such as losing metrics during surge events, difficulties with naming/standardized tags, and a lack of reporting tools. We’ll briefly introduce StatD’s history before diving into those specific issues.

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Building Netflix’s Distributed Tracing Infrastructure

Netflix Tech

Distributed Tracing: the missing context in troubleshooting services at scale Prior to Edgar, our engineers had to sift through a mountain of metadata and logs pulled from various Netflix microservices in order to understand a specific streaming failure experienced by any of our members. Trace Instrumentation: how will it impact our service?

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Scalable Annotation Service?—?Marken

Netflix Tech

Our team, Asset Management Platform, decided to create a generic service called Marken which allows any microservice at Netflix to annotate their entity. Annotations Sometimes people describe annotations as tags but that is a limited definition. An annotation must be associated with some entity which belongs to some microservice.

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Building a Control Plane for Lyft’s Shared Development Environment

Lyft Engineering

in microservice architectures. Lyft runs hundreds of microservices to power the company’s offerings. Our team, the Developer Infrastructure team, aims to build the best tools to enable microservice owners (our “customers”) to reliably and quickly test changes in a local and/or end-to-end environment.

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A Complete Guide to Scale Your Data Pipelines and Data Products with Contract Testing and Dbt

Towards Data Science

To navigate this transition, we can learn from successful implementations of decentralization and distributed architectures like microservices in the operational world. That was the case in the operational world when implementing microservices at scale. Notice also how we have tagged the tests as “contract-test-source”.

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ML Training and Deployment Pipeline Using Databricks

Ripple Engineering

Due to security and compliance requirements we need to deploy our models in our own cluster, packaged within a microservice, on AWS. ML flow tracking and ML flow APIs help with updating model stages and tags across various platforms. In the future, we plan to explore serverless real-time inference.