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Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines

Cloudera

To tackle these challenges, we’re thrilled to announce CDP Data Engineering (DE) , the only cloud-native service purpose-built for enterprise data engineering teams. Native Apache Airflow and robust APIs for orchestrating and automating job scheduling and delivering complex data pipelines anywhere.

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

Edureka

Fabric is meant for organizations looking for a single pane of glass across their data estate with seamless integration and a low learning curve for Microsoft users. Snowflake is a cloud-native platform for data warehouses that prioritizes collaboration, scalability, and performance. Office 365, Power BI, Azure).

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Use Consistent And Up To Date Customer Profiles To Power Your Business With Segment Unify

Data Engineering Podcast

However, that's also something we're re-thinking with our warehouse-centric strategy. How does reverse ETL factor into the enrichment process for profile data? Rudderstack]([link] RudderStack provides all your customer data pipelines in one platform. Let us know if you have opinions there!

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Toward a Data Mesh (part 2) : Architecture & Technologies

François Nguyen

TL;DR After setting up and organizing the teams, we are describing 4 topics to make data mesh a reality. With this 3rd platform generation, you have more real time data analytics and a cost reduction because it is easier to manage this infrastructure in the cloud thanks to managed services.

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Data Pipelines in the Healthcare Industry

DareData

One paper suggests that there is a need for a re-orientation of the healthcare industry to be more "patient-centric". Furthermore, clean and accessible data, along with data driven automations, can assist medical professionals in taking this patient-centric approach by freeing them from some time-consuming processes.

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Data Pipeline vs. ETL: Which Delivers More Value?

Ascend.io

In the modern world of data engineering, two concepts often find themselves in a semantic tug-of-war: data pipeline and ETL. Fast forward to the present day, and we now have data pipelines. Data Ingestion Data ingestion is the first step of both ETL and data pipelines.

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Bringing Automation To Data Labeling For Machine Learning With Watchful

Data Engineering Podcast

In this episode founder Shayan Mohanty explains how he and his team are bringing software best practices and automation to the world of machine learning data preparation and how it allows data engineers to be involved in the process. Data stacks are becoming more and more complex. That’s where our friends at Ascend.io