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Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming…

IBM Data Science in Practice

Graceful External Termination: Handling Pod Deletions in Kubernetes Data Ingestion and Streaming Jobs When running big-data pipelines in Kubernetes, especially streaming jobs, its easy to overlook how these jobs deal with termination. If not handled correctly, this can lead to locks, data issues, and a negative user experience.

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Data Integrity for AI: What’s Old is New Again

Precisely

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. But those end users werent always clear on which data they should use for which reports, as the data definitions were often unclear or conflicting.

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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

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How Gardenia Technologies helps customers create ESG disclosure reports 75% faster using agentic generative AI on Amazon Bedrock

AWS Machine Learning Blog

This ReportSpec definition is inserted into the task prompt. Christian Dunn is a Software Engineer based in London building ETL pipelines, web-apps, and other business solutions at Gardenia Technologies. She helps AWS customers to bring their big ideas to life and accelerate the adoption of emerging technologies.

AWS
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Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

Flipboard

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. Users can write data to managed RMS tables using Iceberg APIs, Amazon Redshift, or Zero-ETL ingestion from supported data sources.

SQL
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Ask HN: Who wants to be hired? (July 2025)

Hacker News

I'm JD, a Software Engineer with experience touching many parts of the stack (frontend, backend, databases, data & ETL pipelines, you name it). With over 3 years of working with ETL pipelines and REST API integrations and development, I understand how to develop and maintain robust and scalable data systems.

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Database replication

Dataconomy

Database replication involves creating copies of data across different servers or databases, which ensures that all users and applications have access to the same data at all times. This practice is vital in distributed database systems and enables organizations to manage data more effectively.