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Data Engineering

Data Engineering knowledge cards on KnowBite — curated from specialist publications, updated throughout the day.

  1. Quick Start: C# and MongoDB - Starting and Setup

    In the first of the C# Quick Start series, I'll show how to set up connections between C# and MongoDB. In future parts we'll move on and work through: Database Create, Read, Update, and Delete (CRUD) operations Map MongoDB to C# objects Look at MongoDB aggregation How to use LINQ with MongoDB As you already know, C# is a general-purpose language and MongoDB is a general-purpose data platform. Together, C# and…

    MongoDB Blog · 2026-11-03T20:09:41Z

  2. Migrate Google BigQuery to an open lakehouse on AWS with Apache Iceberg

    Learn how to migrate Google BigQuery workloads to an open lakehouse on AWS built on Apache Iceberg and Amazon S3. This post explains why an open lakehouse future-proofs your data, presents the target architecture on AWS, and walks through a step-by-step migration: assess, design, convert, migrate, cut over, and optimize.

    AWS Big Data Blog · 2026-11-02T17:21:17Z

  3. Introducing Atlas App Connections: Secure, One-Click Access for AI Tools

    For builders using AI coding tools, speed is everything. Integrations that require generating a credential, filing an access request, or waiting on a custom build can stop momentum (and productivity) in its tracks. Until now, connecting an external application like an AI coding assistant to a MongoDB Atlas account introduced some of that friction. The only way to do so was by sharing long-lived service account…

    MongoDB Blog · 2026-11-03T20:08:21Z

  4. Building Next-gen AI agents: The MongoDB Atlas-Microsoft Foundry Integration

    Generative AI is rapidly evolving from experimenting with models to relying on intelligent, autonomous multi-agent workflows that can reason, act, and adapt in real time. Together, Microsoft and MongoDB are defining the future of AI by providing companies everywhere a robust, secure, scalable foundation for building innovative, next-generation AI agents. Microsoft Azure Foundry provides a unified, enterprise-grade…

    MongoDB Blog · 2026-11-03T20:02:58Z

  5. Automated, Secure, and Scalable: Reference Implementation for Atlas on Azure

    Today’s cloud-driven world is characterized by the requirement to scale—and to do so massively and faster than ever. But standing up secure and compliant environments on the cloud is complex and requires huge efforts, as teams have to figure out networking, policies, identity, and governance requirements across numerous projects. At MongoDB, we’re focused on empowering enterprise customers to build faster, smarter…

    MongoDB Blog · 2026-11-03T20:02:16Z

  6. Introducing time series collections for the next generation of Amazon OpenSearch Serverless

    The next generation of Amazon OpenSearch Service serverless now supports time series collections. Define the boundaries for cost, performance, security, access, and data retention using simple controls for data tiering, time field selection, and data retention, then turn raw events into insights with the next-generation OpenSearch UI.

    AWS Big Data Blog · 2026-10-27T21:52:04Z

  7. Fighting Tool Sprawl: The Case for AI Tool Registries

    As enterprise AI agent adoption scales, the absence of centralized, organization-level tool infrastructure is producing compounding costs. When adoption is built around optimizing for deployment speed, enterprises expose themselves to a combination of risks: duplicated engineering effort, security exposure, and operational opacity. Every enterprise needs its own shared tool registry, one that reflects its specific…

    MongoDB Blog · 2026-11-03T20:01:13Z

  8. From Problem to Fix in Minutes: Automating Search Relevance Tuning with the Amazon OpenSearch Service Relevance Agent

    The Amazon OpenSearch Service Search Relevance Agent turns a low NDCG score into a better search query in minutes. Describe the problem through the Ask AI chat, and the agent analyzes your User Behavior Insights data, generates an improved query, and validates it with an offline Search Relevance Workbench experiment, all without leaving the UI.

    AWS Big Data Blog · 2026-10-27T21:31:20Z

  9. Financial Crime Mitigation with MongoDB / Part IV: Agentic AI for Case Management

    Welcome back to the last part of our series on Building a Financial Crime Mitigation Platform. We have shown how MongoDB ideally supports the technical capabilities of modern digital financial operations as a unified data platform. In case you missed it, be sure to check out the series overview . In today’s world, fraud management systems need to react in the moment. Immediacy has become a must-have characteristic…

    MongoDB Blog · 2026-11-03T20:00:16Z

  10. Layer MongoDB Atlas Resource Policies for Defense-in-Depth

    Editor’s note: This post is the second in a five-part series exploring how MongoDB Atlas Resource Policies help you strengthen database security and enforce consistent guardrails across your organization. Read the first post on IP access list management here . Future posts will cover topics like blocking wildcard IP access and restricting access by cloud provider or region. Imagine protecting your home with only a…

    MongoDB Blog · 2026-11-03T19:56:14Z

  11. Secure MongoDB Atlas With Automated IP Access List Policies

    Editor’s note: This post is the first in a five-part series exploring how MongoDB Atlas Resource Policies help you strengthen database security and enforce consistent guardrails across your organization. Future posts will cover topics like defense-in-depth, blocking wildcard IP access, and restricting access by cloud provider or region. Picture this: Your development team spins up a new MongoDB cluster for a…

    MongoDB Blog · 2026-11-03T19:55:16Z

  12. LEAF: Distillation of State‑of‑the‑Art Text Embedding Models

    At MongoDB Research, we’re exploring how to make cutting‑edge AI models smaller, faster, and more flexible. In this blog post, we present LEAF ( Lightweight Embedding Alignment Framework ), a new knowledge distillation framework for text embedding models. We’re also releasing two best‑in‑class embedding models trained with LEAF: mdbr-leaf-ir —23M parameter model optimized for retrieval‑focused tasks like semantic…

    MongoDB Blog · 2026-11-03T19:54:38Z

  13. Trace security threats in multi-agent pipelines with Amazon OpenSearch Service

    Learn how to trace two invisible threats in a multi-agent AI pipeline, prompt injection propagation and cross-tenant data exfiltration, using Amazon OpenSearch Service and Amazon Bedrock. Instrument every agent hop with embeddings, provenance, and PII detection, reconstruct the attack path, and add guardrails that stop these attacks before they reach production.

    AWS Big Data Blog · 2026-10-19T16:10:31Z

  14. Financial Crime Mitigation with MongoDB / Series Overview

    Where money moves, crime evolves. As banking becomes fully digital—with instant payments, open finance APIs, and mobile-first onboarding—it has opened new roads for cyberattacks, forcing financial institutions to respond in real-time. With just seconds to detect, prevent, and react to suspicious activity—without disrupting the customer experience—banks face an immense challenge that only modern, AI-powered solutions…

    MongoDB Blog · 2026-11-03T19:53:52Z

  15. Unlocking AI Search: Introducing Automated Embedding in MongoDB Vector Search

    We are excited to announce the public preview of Automated Embedding in MongoDB Vector Search (available in Public Preview in MongoDB Community Edition ), a groundbreaking feature designed to make building sophisticated, AI-powered applications easier than ever. Since 2023, MongoDB has offered integrated vector search alongside our operational database, and last year we announced the acquisition of Voyage AI…

    MongoDB Blog · 2026-11-03T17:17:18Z

  16. Resolving concurrent write conflicts for Apache Iceberg tables in Amazon Redshift

    Amazon Redshift now automatically resolves many concurrent writes to Apache Iceberg tables that don't actually overlap, committing changes that previously failed and had to retry. Learn why these writes conflict, how the engine rebases non-overlapping ones across operation, partition, and file checks, and the throughput gains it delivers.

    AWS Big Data Blog · 2026-10-13T20:16:48Z

  17. Bring Live MongoDB Atlas Data Into Microsoft 365 Copilot

    The federated Copilot connector for MongoDB Atlas gives organizations a governed way to bring live operational data into the flow of work across Copilot experiences. To operate efficiently, businesses require timely information about the systems that underpin their organizations. For example, they need to be able to answer questions like: Which incidents are open? Which orders are delayed? What fields and data types…

    MongoDB Blog · 2026-11-03T14:00:00Z

  18. Bring data quality to your Amazon EMR workloads with Amazon EMR Data Quality built on AWS Glue Data Quality

    AWS Glue Data Quality is now available natively on Amazon EMR, bundled in AWS runtime for Apache Spark (emr-spark-8.1.0). Write the same DQDL rulesets you use in AWS Glue directly in your Amazon EMR Spark jobs. This post walks through two patterns: lightweight inline validation and an Apache Iceberg Write-Audit-Publish workflow with managed rulesets.

    AWS Big Data Blog · 2026-10-12T23:40:26Z

  19. Introducing MongoDB Atlas Sharding Advisor: Know When and How to Scale Sharded Clusters

    As applications grow, database teams need to make increasingly complex decisions about how to distribute data, manage workloads, and scale their infrastructure. Sharding enables MongoDB to scale horizontally, but managing a sharded cluster can introduce its own challenges. Understanding cluster health, diagnosing uneven load, and choosing an appropriate shard key can require piecing together metrics across multiple…

    MongoDB Blog · 2026-11-02T15:13:14Z

  20. Building an Agentic Warehouse Management System—Part 2: Java and Spring AI

    If you arrived here without reading the first part, I recommend starting with Agentic WMS — Part 1: Where AI Agents Add Value . There, we introduced the WMS scenario, explored the replenishment problem, and discussed where an AI agent can add value without replacing deterministic application logic. In this second part, we will move from the design to the first steps of the implementation. We will look at how the…

    MongoDB Blog · 2026-10-29T17:33:15Z

  21. Monitoring MWAA-orchestrated ETL pipelines with Amazon OpenSearch Service

    Troubleshooting a multi-service, MWAA-orchestrated ETL pipeline often means hunting across scattered Amazon CloudWatch log groups. This post shows how to centralize your ETL logs in Amazon OpenSearch Service and query them in plain language through an MCP server on Amazon Bedrock AgentCore, so you find root causes faster.

    AWS Big Data Blog · 2026-10-06T16:17:46Z

  22. Agentic Smart Payment Routing with MongoDB / Part I: The Multi-Modal Blueprint

    Transaction routing is a delicate balancing act between authorization rates, execution cost, and latency. A declined transaction can mean a lost sale, making payment optimization one of the most direct levers for top-line revenue growth. Yet payment operations teams remain trapped in manual operational cycles: Pulling batch CSV reports, analyzing spreadsheets, and manually updating rule tables in legacy gateways…

    MongoDB Blog · 2026-10-29T16:38:20Z

  23. Cost-effective ETL with DuckDB and Amazon S3 Tables on AWS Glue

    Learn how to pair DuckDB with AWS Glue 6.0 to run SQL-centric ETL on a single worker, reading Parquet from Amazon S3 and writing Apache Iceberg tables to Amazon S3 Tables. This post walks through a complete, runnable example and compares measured cost and runtime against an equivalent Apache Spark job on the same Glue runtime.

    AWS Big Data Blog · 2026-10-06T16:13:02Z

  24. Scanning 7.25 Million Public Repositories on China's Largest Git Platform for Secrets

    This guest post by independent security researcher Ben Zimmermann was developed in collaboration with the Kingfisher team at MongoDB. Ben specializes in large-scale credential exposure research across public source-hosting platforms. TL;DR: I scanned 7,256,531 public repositories on Gitee (China's largest indigenous Git hosting platform), using both TruffleHog and Kingfisher , and found 26,400 unique verified-live…

    MongoDB Blog · 2026-10-28T16:17:24Z

  25. Query across accounts and table formats with multi-catalog in Amazon EMR

    Amazon EMR 8.1.0 adds multi-catalog support through the RedirectingSessionCatalog, so you can query Iceberg, Delta Lake, Hudi, and Hive tables through a single catalog and join data across AWS accounts without copying it. This post shows how to put these capabilities into practice on Amazon EMR Serverless.

    AWS Big Data Blog · 2026-10-06T16:05:27Z

  26. DataGrip: a great database IDE to work with MongoDB Atlas

    JetBrains released a new version of DataGrip that includes support for MongoDB and ships with the MongoDB Shell out of the box. A few months ago, JetBrains released the first version of DataGrip that supports MongoDB. DataGrip is a professional DataBase IDE and is a great tool for advanced data exploration and analytical queries for your data in MongoDB and Atlas. If you use any of the other JetBrains commercial…

    MongoDB Blog · 2026-10-28T01:07:06Z

  27. Materialize once, query anywhere: Introducing Iceberg materialized views in Amazon Redshift

    Amazon Redshift now supports Iceberg materialized views. Compute an aggregation once in Amazon Redshift and store the result as a standard Apache Iceberg table in Amazon S3, queryable by Amazon Athena, Apache Spark, Amazon SageMaker, and AWS Glue. This post covers use cases, incremental refresh, and a step-by-step getting-started guide.

    AWS Big Data Blog · 2026-10-05T21:41:49Z

  28. MongoDB.local London 2024: Better Applications, Faster

    Since we kicked off MongoDB’s series of 2024 events in April, we’ve connected with thousands of customers, partners, and community members in cities around the world—from Mexico City to Mumbai. Yesterday marked the nineteenth stop of the 2024 MongoDB.local tour, and we had a blast welcoming folks across industries to MongoDB.local London, where we discussed the latest technology trends, celebrated customer…

    MongoDB Blog · 2026-10-28T01:04:18Z

  29. Run an automated operational review with the Amazon Redshift MCP server

    Amazon Redshift automates much of its own tuning, but periodic operational reviews still pay off. Learn how the review_cluster tool in the open-source Amazon Redshift MCP server runs a full cluster diagnostic from one natural-language request, evaluating 12 diagnostic areas and returning prioritized, documentation-linked recommendations in minutes.

    AWS Big Data Blog · 2026-10-05T15:44:45Z

  30. Getting Started With MongoDB and AWS CodeWhisperer

    Amazon CodeWhisperer is trained on billions of lines of code and can generate code suggestions — ranging from snippets to full functions — in real-time, based on your comments and existing code. AI code assistants have revolutionized developers’ coding experience, but what sets Amazon CodeWhisperer apart is that MongoDB has collaborated with the AWS Data Science team, enhancing its capabilities! At MongoDB, we are…

    MongoDB Blog · 2026-10-28T00:58:19Z

  31. Optimize consumer rebalancing on Amazon MSK with next generation protocol

    The KIP-848 consumer protocol in Apache Kafka 4.0 redesigns consumer group rebalancing to eliminate stop-the-world pauses. This post explains how the consumer protocol works on Amazon MSK, how to enable it on MSK Standard and Express brokers, and how to diagnose and resolve slow rebalancing issues.

    AWS Big Data Blog · 2026-10-01T16:43:54Z

  32. 10 Years of MongoDB Atlas: Built for What’s Next

    Nearly a decade ago, I joined MongoDB as a Senior Product Manager to help build the company’s new cloud product, MongoDB Atlas. Our customers had been telling us they wanted to bring MongoDB’s familiar developer experience to the cloud, with the reliability and confidence teams needed to run in production. Atlas was our answer. Today, we’re celebrating 10 years of MongoDB Atlas, the generational data platform for AI…

    MongoDB Blog · 2026-06-25T17:28:40Z

  33. Building an LLM-powered DAG failure analysis plugin for Amazon MWAA

    Debugging Apache Airflow DAG failures across services like AWS Glue, Amazon EMR, and Amazon Athena is slow and manual. In this post, we show you how to build a custom Airflow plugin that integrates with Amazon Bedrock to automatically analyze DAG task failures and deliver on-demand root cause analysis on Amazon MWAA.

    AWS Big Data Blog · 2026-09-29T16:17:09Z