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Axel Thevenot

Axel Thevenot

Open source LinkedIn

France

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About

Axel is a seasoned cloud solutions architect with extensive experience in designing and implementing scalable cloud infrastructures. He specializes in Google Cloud Platform and has a strong background in DevOps practices. Axel is passionate about leveraging cloud technologies to drive business transformation and efficiency. He is committed to continuous learning and staying updated with the latest industry trends.

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BigQuery Introduces Code-Free Workflow Tool for Streamlined Data Management Trending ️‍🔥

BigQuery Introduces Code-Free Workflow Tool for Streamlined Data Management

3 months ago

BigQuery

Google has unveiled BigQuery Workflows, a new code-free orchestration tool for BigQuery, now in preview. It simplifies data pipeline management with a visual interface, built-in scheduling, centralized monitoring, flexibility, and cost-effectiveness. Users can create sequences of code assets like notebooks and SQL queries. Limitations include creating new assets within the workflow, no access grants for specific workflows, and availability only in Google Cloud.

Automate Your Marketing Audits - Say Goodbye to Manual Checklists

Automate Your Marketing Audits - Say Goodbye to Manual Checklists

Featured

Marketing Auditor is a comprehensive, automated tool designed to revolutionize the process of auditing digital marketing campaigns. It supports platforms like Google Ads and Google Analytics, performing 200+ automated checks to uncover growth opportunities and deliver actionable insights. Users can generate professional, white-labeled reports customized to their branding and preferred formats like Google Slides and PowerPoint.

BigQuery Increases Maximum Partitions Per Table from 4000 to 10000

BigQuery Increases Maximum Partitions Per Table from 4000 to 10000

7 months ago

BigQuery

Now, the maximum number of partitions by table in BigQuery has increased from 4000 to 10000. Partitioned tables are divided into segments called partitions. Specifying a partition column allows you to run queries over those partitions to scan only the relevant data, improving performance and reducing costs. BigQuery supports three types of partitions: Integer Range Partitioning, Time-Unit Column Partitioning, and Ingestion Time Partitioning.