Remove Data Architecture Remove Data Integration Remove Data Schemas
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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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Data Mesh Architecture: Revolutionizing Event Streaming with Striim

Striim

This allows for two-way integration so that information can flow from one system to another in real-time. Striim is a cloud-native Data Mesh platform that offers features such as automated data mapping, real-time data integration, streaming analytics, and more.

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The JaffleGaggle Story: Data Modeling for a Customer 360 View

dbt Developer Hub

To do so, we’ll focus on three steps: Performing the email domain extraction from the email Flagging personal emails Creating a column for corporate emails After we complete these steps, we’ll also cover a "human in the loop" step to ensure data integrity at the modelling stage.

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17 Super Valuable Automated Data Lineage Use Cases With Examples

Monte Carlo

Squatch VP of Data, IT & Security, Nick Johnson. Data integration and modeling In previous eras, data models like Data Vault were used to manually create full visibility into data lineage. Data System Modernization And Team Reorganization The only constant in data engineering is change.

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100+ Big Data Interview Questions and Answers 2023

ProjectPro

It also discusses several kinds of data. Schemas are available in various shapes and sizes, and the star schema and the snowflake schema are two of the most common. Entities in a star schema are depicted as stars, whereas those in a snowflake schema are depicted as snowflakes.

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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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