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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. Understanding the essential components of data pipelines is crucial for designing efficient and effective data architectures.

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Hands-On Introduction to Delta Lake with (py)Spark

Towards Data Science

Concepts, theory, and functionalities of this modern data storage framework Photo by Nick Fewings on Unsplash Introduction I think it’s now perfectly clear to everybody the value data can have. To use a hyped example, models like ChatGPT could only be built on a huge mountain of data, produced and collected over years.

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Data Mesh Architecture: Revolutionizing Event Streaming with Striim

Striim

Data consistency is ensured through uniform definitions and governance requirements across the organization, and a comprehensive communication layer allows other teams to discover the data they need. Marketing teams should have easy access to the analytical data they need for campaigns.

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What is Data Engineering? Skills, Tools, and Certifications

Cloud Academy

Part of the Data Engineer’s role is to figure out how to best present huge amounts of different data sets in a way that an analyst, scientist, or product manager can analyze. What does a data engineer do? A data engineer is an engineer who creates solutions from raw data.

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The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData: Data Engineering

It enables advanced analytics, makes debugging your marketing automations easier, provides natural audit trails for compliance, and allows for flexible, evolving customer data models. So next time you’re designing your customer data architecture in your CDP, don’t just think about the current state of your customers.

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

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Snowflake Observability and 4 Reasons Data Teams Should Invest In It

Monte Carlo

You feel like the world is your oyster and the possibilities for how your data team can add value to the business is virtually infinite. Data observability solutions capability to automate lineage can help in this regard. That’s the beauty of Monte Carlo because it allows us to see who is using data and where it is being consumed.

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