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We’ve Been Using FITT Data Architecture For Many Years, And Honestly, We Can Never Go Back

DataKitchen

TL;DR: Functional, Idempotent, Tested, Two-stage (FITT) data architecture has saved our sanity—no more 3 AM pipeline debugging sessions. The cloud has made it incredibly affordable to have copies of systems, tools, pipelines, and even data. Pipeline broke due to a schema change? Re-run the pipeline with debugging enabled.

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Airflow vs Dagster: Comparing Two Data Orchestration Solutions

ProjectPro

billion by 2032, highlighting the critical need for efficient data pipeline management. While Airflow has long been a staple in the data engineering ecosystem, Dagster is emerging as a strong alternative, offering a fresh perspective on orchestration with enhanced functionality for data-aware pipelines. billion in 2024 to $924.39

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The Race For Data Quality in a Medallion Architecture

DataKitchen

We have also seen a fourth layer, the Platinum layer , in companies’ proposals that extend the Data pipeline to OneLake and Microsoft Fabric. The need to copy data across layers, manage different schemas, and address data latency issues can complicate data pipelines. However, this architecture is not without its challenges.

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Microsoft Fabric vs. Snowflake: Key Differences You Need to Know

Edureka

Ideal for: Business-centric workflows involving fabric Snowflake = environments with a lot of developers and data engineers 2. Ideal for: Fabric: Microsoft-centric organizations Snowflake: Multi-cloud flexibility seekers 3. Cloud support Microsoft Fabric: Works only on Microsoft Azure.

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Unlocking Data Team Success: Are You Process-Centric or Data-Centric?

DataKitchen

Unlocking Data Team Success: Are You Process-Centric or Data-Centric? We’ve identified two distinct types of data teams: process-centric and data-centric. We’ve identified two distinct types of data teams: process-centric and data-centric. They work in and on these pipelines.

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Data Engineering Weekly #196

Data Engineering Weekly

impactdatasummit.com Thumbtack: What we learned building an ML infrastructure team at Thumbtack Thumbtack shares valuable insights from building its ML infrastructure team. The blog emphasizes the importance of starting with a clear client focus to avoid over-engineering and ensure user-centric development.

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How to Build an AI Agent with Pydantic AI: A Beginner's Guide

ProjectPro

Have you ever considered the challenges data professionals face when building complex AI applications and managing large-scale data interactions? These obstacles usually slow development, increase the likelihood of errors and make it challenging to build robust, production-grade AI applications that adapt to evolving business requirements.