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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

By Josep Ferrer , KDnuggets AI Content Specialist on July 15, 2025 in Data Science Image by Author Delivering the right data at the right time is a primary need for any organization in the data-driven society. But lets be honest: creating a reliable, scalable, and maintainable data pipeline is not an easy task.

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Data pipelines

Dataconomy

Data pipelines are essential in our increasingly data-driven world, enabling organizations to automate the flow of information from diverse sources to analytical platforms. What are data pipelines? Purpose of a data pipeline Data pipelines serve various essential functions within an organization.

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What’s New with Azure Databricks: Unified Governance, Open Formats, and AI-Native Workloads

databricks

Events Data + AI Summit Data + AI World Tour Data Intelligence Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more Databricks Mosaic Research Blog Discover the latest in our Gen AI research Data Brew Podcast Let’s talk data!

Azure 260
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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

By Bala Priya C , KDnuggets Contributing Editor & Technical Content Specialist on June 19, 2025 in Programming Image by Author | Ideogram Youre architecting a new data pipeline or starting an analytics project, and you’re probably considering whether to use Python or Go. We compare Go and Python to help you make an informed decision.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

Flipboard

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Choose Delete stack.

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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

Flipboard

Document Everything : Keep clear and versioned documentation of how each feature is created, transformed, and validated. Use Automation : Use tools like feature stores, pipelines, and automated feature selection to maintain consistency and reduce manual errors.

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Data integration

Dataconomy

Feeding data for analytics Integrated data is essential for populating data warehouses, data lakes, and lakehouses, ensuring that analysts have access to complete datasets for their work. Best practices for data integration Implementing best practices ensures successful data integration outcomes.