Sriw World of Coding·17h ago50 Vendors - One Deadline - Zero Room for Error: Designing an SFTP-to-Synapse Ingestion Pipeline…If your CTO asked you right now, “what happens if Vendor #37’s file doesn’t show up by 5:30 AM?” — would your data platform even notice…
Sarthak Singhal·6d agoFrom Millions to Billions: Designing Systems for Large-Scale Data IngestionIntroduction
Sundar Ram Subramanian·Nov 3Data Ingestion in a RAG systemThis is the second article in the series of articles on Traditional RAG. From this article we will dive deep in to components of the…
Shridhar Pawar·Oct 29RAG Data Ingestion on Azure: From a PDF to a Searchable IndexMost RAG tutorials spend their time on the query side. Most RAG failures in production come from the ingestion side. This is a complete…
InNeo4j Developer BlogbyMaxime GUERY·Oct 23Implementation of the Neo4j Import Without Deadlock and Lock ContentionExplanation and sharing of a generic implementation of the approach enabling lock-free data import.
Trisha Gaur·Oct 21Snowflake Data Engineering Still Needs an Activation Layer on TopSnowflake data engineering delivers fast, elastic storage and compute, yet roughly two-thirds of the compute spend in a warehouse’s second…
Jacobmoracha·Oct 20Where Does Data Come From?The first stage of the data engineering lifecycle, explained for people who build pipelinesA response icon1A response icon1
Sriw World of Coding·Oct 16Azure Data Engineer Interview Question : What are Data Formats supported in Azure Data Factory?Quick question: if someone dropped a random file into your Blob Storage right now — CSV, JSON, Parquet, Avro, XML — would you know…
Prince Chauhan·Oct 16Deploying docling-pipelines on OpenShift: Containers, Routes, and ObservabilityA complete walkthrough — from the UBI9-based multi-stage build to OpenShift Deployments, Routes, ConfigMaps, and distributed tracing with…A response icon1A response icon1
Saikat Ganguly·Oct 15Microsoft Fabric Warehouse: How Data Is Stored, Managed, and Made Ready for AnalyticsAn engineer’s practical guide to data storage, ingestion, validation, and production-ready data management
