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5 Tips for Building Scalable Data Pipelines

KDnuggets

Building data pipelines is a very important skill that you should learn as a data engineer. A data pipeline is just a series of procedures that transport data from one location to another, frequently changing it along the way.

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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Continuous Integration and Continuous Delivery (CI/CD) for Data Pipelines: It is a Game-Changer with AnalyticsCreator! The need for efficient and reliable data pipelines is paramount in data science and data engineering. They transform data into a consistent format for users to consume.

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What Data Engineers Really Do?

Analytics Vidhya

In a data-driven world, behind-the-scenes heroes like data engineers play a crucial role in ensuring smooth data flow. A data engineer investigates the issue, identifies a glitch in the e-commerce platform’s data funnel, and swiftly implements seamless data pipelines.

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How to Implement a Data Pipeline Using Amazon Web Services?

Analytics Vidhya

Introduction The demand for data to feed machine learning models, data science research, and time-sensitive insights is higher than ever thus, processing the data becomes complex. To make these processes efficient, data pipelines are necessary. appeared first on Analytics Vidhya.

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Building Data Pipeline with Prefect

KDnuggets

Learn how to build and deploy an end-to-end data pipeline using Prefect with a few lines of code.

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Data Engineering for Streaming Data on GCP

Analytics Vidhya

Real-time dashboards such as GCP provide strong data visualization and actionable information for decision-makers. Nevertheless, setting up a streaming data pipeline to power such dashboards may […] The post Data Engineering for Streaming Data on GCP appeared first on Analytics Vidhya.

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Introducing Databricks LakeFlow: A unified, intelligent solution for data engineering

databricks

Today, we are excited to announce Databricks LakeFlow, a new solution that contains everything you need to build and operate production data pipelines.