Sync cloud resources 6x faster with CloudQuery incremental sync ⚡ Syncing data frequently is essential for real-time analytics and data pipelines. However, transferring large datasets between providers is resource-intensive and time-consuming, especially when syncing frequently. CloudQuery's incremental sync tracks what's already synced and fetches only the changes. How incremental sync works: • Stores last sync timestamp in a state table • Queries the source for records modified after that timestamp • Updates only changed data in the destination database In the example below, after the initial full sync of 33 seconds, incremental runs complete in just 5 seconds. Plus, CloudQuery is open source! Install it with "pip install cloudquery". 🚀 Full article: bit.ly/46QIWFq ☕️ Run this code: bit.ly/4nOsxHr #DataEngineering #Python #Collaboration

Build production RAG pipelines with 18 lines of YAML 🚀 RAG applications need data from various sources moved into vector stores. Manual API integration means writing boilerplate for rate limiting, pagination, and error handling instead of building AI. @cloudqueryio handles the entire data-to-embeddings pipeline with declarative YAML config and native pgvector support. Key benefits: • Pre-built connectors for AWS, GCP, Azure, and 100+ platforms • Sync state persistence with incremental processing and automatic schema evolution • Built-in PII removal, column obfuscation, and data cleaning for compliance • Native pgvector support: text splitting, embeddings, semantic indexing for RAG Plus, CloudQuery is open source! Install it with "pip install cloudquery". ☕️ Link to the repository: github.com/cloudquery/cloudq… 🚀 Full article (Production RAG with CloudQuery and Postgres): bit.ly/3VJelDi #DataEngineering #ELT #Colaboration #DataPipelines