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They Handle 500B Events Daily. Here’s Their Data Engineering Architecture.

Monte Carlo

A data engineering architecture is the structural framework that determines how data flows through an organization – from collection and storage to processing and analysis. Before building your own data architecture from scratch though, why not steal – er, learn from – what industry leaders have already figured out?

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Simplifying Data Architecture and Security to Accelerate Value

Snowflake

What if you could streamline your efforts while still building an architecture that best fits your business and technology needs? At BUILD 2024, we announced several enhancements and innovations designed to help you build and manage your data architecture on your terms. Here’s a closer look.

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Modern Data Architecture: Data Mesh and Data Fabric 101

Precisely

Data mesh and data fabric are two modern data architectures that serve to enable better data flow, faster decision-making, and more agile operations. Both architectures share the goal of making data more actionable and accessible for users within an organization.

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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. The Medallion architecture is a design pattern that helps data teams organize data processing and storage into three distinct layers, often called Bronze, Silver, and Gold.

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Modern Data Architecture for Embedded Analytics

Every data-driven project calls for a review of your data architecture—and that includes embedded analytics. Before you add new dashboards and reports to your application, you need to evaluate your data architecture with analytics in mind. 9 questions to ask yourself when planning your ideal architecture.

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Addressing The Challenges Of Component Integration In Data Platform Architectures

Data Engineering Podcast

In this episode Tobias Macey shares his thoughts on the challenges that he is facing as he prepares to build the next set of architectural layers for his data platform to enable a larger audience to start accessing the data being managed by his team. With Materialize, you can! Want to see Starburst in action? Want to see Starburst in action?

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Our Approach to Architecture by James Heward

Scott Logic

In this article, we are publishing Scott Logic’s approach to architecture, and how we avoid common pitfalls. Whilst there are myriad reasons for these failures, in many cases they can be attributed to poorly implemented architecture. We believe architecture has a two-fold purpose.

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Embedding BI: Architectural Considerations and Technical Requirements

Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures. Holding onto old BI technology while everything else moves forward is holding back organizations.

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Best Practices for Deploying & Scaling Embedded Analytics

Read more about how to simplify the deployment and scalability of your embedded analytics, along with important considerations for your: Environment Architecture: An embedded analytics architecture is very similar to a typical web architecture. Deployment: Benefits and drawbacks of hosting on premises or in the cloud.