Remove Aggregated Data Remove Architecture Remove Data Collection
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Data Engineering Weekly #210

Data Engineering Weekly

Netflix writes an excellent article describing its approach to cloud efficiency, starting with data collection to questioning the business process. link] Adevinta: From Lakehouse architecture to data mesh One of DEW’s 2025 predictions is that we will see increased adoption of the data Mesh principles.

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Startup Spotlight: Leap Metrics Champions Data-Driven Healthcare 

Snowflake

Healthcare data can and should serve as a holistic, actionable tool that empowers caregivers to make informed decisions in real time. We founded Leap Metrics and built Sevida to serve patients and healers by providing an analytics-first approach to data collection and care management solutions. That’s where Snowflake comes in.

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Faster Features, Happier Customers: Introducing The Platform That Transformed Our Grocery App

Picnic Engineering

As part of this change, we adopted a more modular app architecture (inspired by Uber’s Riblets ) in order to reduce the amount of sweeping changes. We had cut the lead time for most features almost in half by reducing the amount of code to write and unifying our architecture.

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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. But it isn’t just aggregating data for models. Data needs to be prepared and analyzed.

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Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

While all these solutions help data scientists, data engineers and production engineers to work better together, there are underlying challenges within the hidden debts: Data collection (i.e., The serving and monitoring infrastructure need to fit into your overall enterprise architecture and tool stack.

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What is a Data Pipeline (and 7 Must-Have Features of Modern Data Pipelines)

Striim

These steps guarantee that data is accurate, reliable, and meaningful by the time it reaches its destination, making it possible for teams to generate insights and make data-driven decisions. This architecture can vary based on the needs of the organization and the type of data being processed.

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Picnic’s migration to Datadog

Picnic Engineering

To streamline trace collection to a single point, we made the decision not to employ the OTEL collector, and instead use the Datadog agent as our collector. The final solution architecture: Observability as a Code: Observability as Code is a critical part of our approach. Written by Pavel Storozhenko and Harkeet Bajaj.

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