Airbyte’s Post

Curious about how we scale Airbyte Cloud infrastructure? As part of supporting multiple geographic regions, we adopted a control-plane/data-plane architecture. A control-plane orchestrates data movement workloads across multiple data-planes. Technically speaking, each plane is a Kubernetes cluster. Jimmy M. shared our solution to load balancing Airbyte workloads: inversion of control. We enqueue workloads in a single job queue and let the data-planes compete for jobs to process if they have capacity to do so. This has the benefit of treating capacity as a problem that is local to a cluster, removes the complexity of planning ahead for available resources, and keeps operations simple. #kubernetes #dataengineering #etl

Wow! We're officially 25,000 strong on LinkedIn! A massive thank you to each and every one of you for following along, engaging with our content, and being part of this. Your support truly means the world to us. #25kStrong #ThankYou #LinkedInCommunity

Discover why investing in closed-source ELT can lead to accumulating technical debt and explore open-source alternatives to future-proof your data infrastructure. #OpenSource #ELT #DataIntegration

Check out our latest video on how you can use Declarative YAML Sources within PyAirbyte! https://lnkd.in/g66XfZTK

Navigating change management in dimensional data models can be complex. This insightful article explores practical approaches and strategies to ensure smooth transitions and minimize disruptions in your data pipeline. Learn how to deploy model changes safely and effectively, ensuring consistency and transparency for your data consumers. #DataModels #DataIntegration #DataPipeline

Data movement needs to be scalable, extensible, and reliable. As 6,000 companies move data daily from our 300 data sources, we have also invested heavily in making Airbyte observable through rich metadata available via an API. Today we’re happy to announce that Metaplane, the leading data observability platform used by our own data team, launched an Airbyte integration on top of that API. Data teams can use Airbyte and Metaplane to proactively detect issues like long-running syncs, late-arriving data, and syncs that moved an incorrect amount of data from source to destination. These monitors use machine learning trained on historical metadata, so no configuration is needed. You can also see which Airbyte streams load tables in your warehouse, so you can trace end-to-end lineage from source to destination. Read more here: https://lnkd.in/geAtE7Tw #dataengineering #dataquality #dataobservability #dataintegration

New feature alert 🚨 You now have the ability to undo and/or redo your changes within the Connector Builder! This also comes with the support for keyboard shortcuts to make interacting with the Connector Builder more intuitive.

Dive into this insightful article on analyzing query plans for efficient table joins in PostgreSQL. Whether you’re a developer, data analyst, or database administrator, mastering query optimization can elevate your database skills. #PostgreSQL #DatabaseOptimization #QueryPlans #DataAnalysis #DatabaseManagement

Unlock the power of Amazon product review analysis with Apify and Snowflake Cortex! This guide walks you through scraping reviews, loading data into Snowflake, and summarizing insights. #DataIntegration #Snowflake