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A Guide to Data Pipelines (And How to Design One From Scratch)

Striim

Data pipelines are the backbone of your business’s data architecture. Implementing a robust and scalable pipeline ensures you can effectively manage, analyze, and organize your growing data. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

AI data engineers are data engineers that are responsible for developing and managing data pipelines that support AI and GenAI data products. Essential Skills for AI Data Engineers Expertise in Data Pipelines and ETL Processes A foundational skill for data engineers?

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Data Pipeline Observability: A Model For Data Engineers

Databand.ai

Data Pipeline Observability: A Model For Data Engineers Eitan Chazbani June 29, 2023 Data pipeline observability is your ability to monitor and understand the state of a data pipeline at any time. We believe the world’s data pipelines need better data observability.

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Data Pipeline Architecture Explained: 6 Diagrams and Best Practices

Monte Carlo

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture?

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On-Prem vs. The Cloud: Key Considerations 

phData: Data Engineering

Prior to making a decision, an organization must consider the Total Cost of Ownership (TCO) for each potential data warehousing solution. On the other hand, cloud data warehouses can scale seamlessly. Vertical scaling refers to the increase in capability of existing computational resources, including CPU, RAM, or storage capacity.

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DataOps Architecture: 5 Key Components and How to Get Started

Databand.ai

It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. These systems typically consist of siloed data storage and processing environments, with manual processes and limited collaboration between teams.

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Data Scientist vs Data Engineer: Differences and Why You Need Both

AltexSoft

They’re integral specialists in data science projects and cooperate with data scientists by backing up their algorithms with solid data pipelines. Juxtaposing data scientist vs engineer tasks. One data scientist usually needs two or three data engineers. An overview of data engineer skills.