Data & Analytics

RabbitMQ Trigger is an n8n node that starts a workflow whenever a new message arrives in a RabbitMQ queue. RabbitMQ is an open-source message broker used by development teams to decouple services, manage background job queues, and handle asynchronous processing. If your engineering team already uses RabbitMQ to pass messages between microservices, this trigger lets n8n listen to those queues and execute automation workflows in response. The core problem this solves is getting business logic and operational workflows connected to your message queue infrastructure. Developers build RabbitMQ queues for technical reasons — handling order processing, managing email queues, distributing background tasks — but the business side needs visibility and action. The RabbitMQ Trigger bridges that gap by letting n8n consume messages from any queue and route them into CRM updates, notification systems, reporting dashboards, or any other business tool. At Osher Digital, we use the RabbitMQ Trigger when clients have existing message queue infrastructure and need to connect it to business workflows without writing custom code. This fits into our system integration work, where we connect developer-facing infrastructure to business-facing tools. Common use cases include processing order events from an e-commerce backend, handling webhook retries through a dead-letter queue, and orchestrating multi-step data processing pipelines.

Redis is an open-source, in-memory data store that functions as a database, cache, message broker, and streaming engine. Unlike traditional disk-based databases, Redis holds data in RAM, which means read and write operations happen in microseconds rather than milliseconds. It supports data structures including strings, hashes, lists, sets, sorted sets, and streams — making it far more flexible than a simple key-value cache. The most common problem Redis solves is speed. When your application queries a relational database for the same data repeatedly, response times degrade as load increases. Redis sits between your application and your database, serving frequently accessed data from memory. Session stores, leaderboards, rate limiters, real-time analytics counters, and pub/sub messaging channels all run well on Redis because they need sub-millisecond response times. For automation workflows built on n8n, Redis is useful as a shared state store between workflow executions. You can cache API responses, deduplicate incoming webhook data, or manage queue-based processing where multiple workflows need to coordinate. Redis Streams can also act as a lightweight message broker for event-driven architectures. At Osher, we connect Redis into broader system integration projects where performance matters — particularly for real-time data pipelines and AI agent architectures that need fast access to context data between inference calls.

The GraphQL node in n8n lets you send queries and mutations to any GraphQL API directly from your automation workflows. Instead of making multiple REST API calls to assemble the data you need, a single GraphQL query can request exactly the fields you want from multiple related resources in one request. GraphQL APIs are used by platforms like Shopify, GitHub, Contentful, Strapi, Hasura, and many modern SaaS products. If you are integrating with any of these services, the GraphQL node gives you precise control over what data you fetch. You write a GraphQL query, set your variables, and the node returns structured JSON with only the fields you asked for. The node supports queries (read operations), mutations (write operations), and custom headers for authentication. You can pass Bearer tokens, API keys, or custom auth headers. Variables can be set dynamically from upstream nodes, so your GraphQL queries can be parameterised based on data flowing through the workflow. At Osher, we use the GraphQL node for Shopify integrations, headless CMS connections, and any API that offers a GraphQL endpoint alongside or instead of REST. Our system integration team picks GraphQL over REST when the data requirements are complex and we need to reduce the number of API calls. If you are connecting to GraphQL-based services, our custom development team can build the queries and wire them into your workflows.

Rename Keys is a utility node in n8n that changes the property names (keys) of JSON objects as they pass through a workflow. When you connect two systems that use different field names for the same data, Rename Keys sits between them and translates one naming convention to another. This is a common problem in integration work. Your CRM might call a field “company_name” while your accounting software expects “organisation”. Your API returns “firstName” but your database column is “first_name”. Without a translation step, data either fails to map or ends up in the wrong fields. Rename Keys solves this without writing any code. The node lets you define one or more key renaming rules. You specify the current key name and what it should become. It can handle nested JSON properties and rename multiple keys in a single step. You can also use regex patterns for more advanced renaming across keys that share a common pattern. At Osher, we use Rename Keys constantly in our system integration projects. Whenever we connect two platforms that structure their data differently, this node handles the field mapping without adding code nodes to the workflow. If your n8n workflows are breaking because of mismatched field names between systems, this is usually the fix.

The Loop Over Items node (also called Split in Batches) in n8n processes large datasets in smaller groups rather than all at once. It takes an array of items, splits them into batches of a configurable size, and processes each batch through a defined set of nodes before moving on to the next batch. This node solves a fundamental problem in workflow automation: most APIs, databases, and external services cannot handle thousands of records in a single request. They have rate limits, payload size restrictions, or timeout constraints. Without batch processing, a workflow that needs to update 5,000 CRM records would either hit API limits or time out entirely. The node works by splitting your data into groups (e.g., 50 items per batch), sending each group through downstream nodes, and then looping back for the next group until all items are processed. You can combine it with Wait nodes to add delays between batches, which is essential for respecting rate limits on services like Google, HubSpot, or Xero. At Osher, we use this node in virtually every data processing workflow that handles more than a handful of records. Our BOM weather data pipeline project relied on batch processing to handle large volumes of meteorological data without overwhelming downstream APIs. Our n8n consulting team sizes batches based on the specific API limits of each integration involved.

The Webhook node turns an n8n workflow into an API endpoint. It creates a URL that, when called by an external system, triggers the workflow and passes the incoming request data (headers, query parameters, body) to the next node in the chain. This is how you build event-driven automations that respond to real-time events from other systems. Webhooks solve the polling problem. Instead of having your workflow check another system every few minutes for new data (which wastes resources and introduces delays), the external system notifies your workflow the moment something happens. A CRM sends a webhook when a deal closes. A payment processor sends a webhook when a charge succeeds. A form tool sends a webhook when someone submits a response. Your n8n workflow starts processing immediately. The Webhook node supports GET and POST requests, can accept JSON, form data, and binary payloads, handles custom headers for authentication verification, and can return custom responses to the calling system. You can also configure it for testing (temporary URL) or production (persistent URL that survives workflow restarts). At Osher, webhook-triggered workflows are our most common build pattern. Our property inspection automation uses webhooks to trigger report generation when field inspectors submit data. We build webhook-based system integrations and business automations for clients who need their systems to react to events in real time rather than on a schedule. If you need your automation to respond instantly when something happens in another system, talk to our n8n team about building webhook-triggered workflows.

The HTTP Request node is one of the most used nodes in n8n. It lets a workflow send HTTP requests (GET, POST, PUT, PATCH, DELETE) to any web API or URL, making it the universal connector for services that don’t have a dedicated n8n integration node. If a system has a REST API, the HTTP Request node can talk to it. This node handles authentication (API keys, OAuth2, bearer tokens, basic auth), custom headers, query parameters, request bodies (JSON, form data, binary), pagination, response parsing, and error handling. It can upload and download files, follow redirects, and process responses as JSON, text, or binary data. At Osher, the HTTP Request node appears in nearly every n8n workflow we build. It connects to client-specific APIs, third-party SaaS platforms, internal microservices, and AI model endpoints. We used it extensively in our BOM weather data pipeline project, where the workflow pulled weather data from external APIs and pushed processed results into the client’s systems. It’s also central to every system integration project where the target system doesn’t have a native n8n node. If your automation project needs to connect to an API that n8n doesn’t have a built-in node for, the HTTP Request node fills that gap. Talk to our n8n team about building API integrations into your workflows.

Instabug is a mobile app monitoring platform that provides bug reporting, crash analytics, performance monitoring, and in-app user feedback collection for iOS and Android applications. When users encounter issues, they can shake their device (or use a custom trigger) to submit a bug report that automatically includes a screenshot, device logs, network requests, and environment details, giving developers the context they need to reproduce and fix problems. The problem Instabug solves is the gap between user-reported issues and the technical detail developers need. When a user says “the app crashed,” that is not enough to debug. Instabug automatically captures the crash stack trace, the sequence of user actions leading up to it, network request history, device model, OS version, and memory state. This turns vague bug reports into actionable debugging data. Osher integrates Instabug into mobile application projects as part of our custom development services. Beyond the SDK setup, we connect Instabug’s reporting data to your project management and support tools so that crash reports and user feedback automatically create tickets in Jira, Linear, or your preferred tracker. Our integration team builds these pipelines so your development and support teams work from the same data without manual transfer.

Cronly is a monitoring and alerting service built specifically for n8n workflow automation. It watches your n8n workflows for failures, missed executions, and performance issues, then sends notifications through Slack, Discord, email, or webhooks when something goes wrong. If you run n8n workflows that your business depends on, Cronly tells you when they break before your customers notice. The problem Cronly solves is workflow reliability visibility. n8n is powerful for building automation, but it does not provide robust built-in monitoring for production workloads. If a scheduled workflow fails at 2am, you might not find out until someone reports missing data the next morning. Cronly watches for exactly this: failed executions, workflows that should have run but did not, and execution times that exceed your defined thresholds. At Osher, Cronly is a standard part of our n8n consulting engagements. When we build and deploy n8n workflows for clients, we set up Cronly monitoring so that both our team and yours know immediately when something needs attention. This ties into our broader process automation services where workflow reliability directly impacts business operations. We have written about n8n hosting and operations in our self-hosting guide.

One AI is a language AI API platform that gives developers access to pre-built natural language processing (NLP) capabilities through a single unified API. Instead of training custom models, you call One AI’s endpoints to perform tasks like text summarisation, sentiment analysis, keyword extraction, topic detection, and entity recognition on any text or audio input. The problem One AI solves is that building NLP pipelines from scratch is slow and expensive. Most businesses need the same core language capabilities (summarise this document, extract these entities, classify this text), but training and hosting models requires ML engineering resources that many teams lack. One AI packages these capabilities as composable API “skills” that can be chained together in a single request. Osher connects One AI into automated data processing pipelines where unstructured text needs to be parsed, classified, or summarised at scale. For example, we have built workflows that use One AI to extract key information from incoming support emails, summarise meeting transcripts, or classify documents by topic before routing them to the right team. Our AI agent development practice uses tools like One AI as components within larger intelligent systems.

ConfigCat is a feature flag and remote configuration management service that lets development teams toggle features on and off, run A/B tests, and push configuration changes to applications without deploying new code. It provides SDKs for every major platform (JavaScript, Python, .NET, Java, Go, iOS, Android, and more) and a dashboard where non-technical team members can manage feature flags independently. The problem ConfigCat solves is deployment risk. Releasing new features as all-or-nothing code deployments is risky. If something breaks, you roll back the entire release. Feature flags let you wrap new code in toggles that can be switched on for a small percentage of users first, then gradually rolled out, or instantly killed if problems appear. ConfigCat makes this possible without building your own flag infrastructure. Osher integrates ConfigCat into custom application development projects where controlled feature rollouts matter. We also use feature flags within AI agent deployments to test different model configurations or prompt variations against live traffic before committing to changes. ConfigCat’s targeting rules allow us to segment users by attribute, making it useful for gradual rollouts across regions or customer tiers.

Kadoa is an AI-powered web scraping platform that extracts structured data from websites without requiring you to write CSS selectors, XPath queries, or custom parsing code. You point Kadoa at a webpage, and its AI identifies the data structure (product listings, job postings, contact details, pricing tables) and extracts it into clean JSON or CSV format. Traditional web scraping breaks every time a website changes its layout. Kadoa reduces this maintenance burden because its AI adapts to structural changes rather than relying on hardcoded element selectors. This makes it particularly useful for ongoing data collection from sites that update their design frequently. At Osher, we use Kadoa as a data source within broader automated data processing pipelines. A typical project connects Kadoa to an n8n workflow that schedules extraction, processes the returned data, and loads it into a database or CRM for analysis. We have built competitive intelligence, market research, and lead enrichment systems using Kadoa as the extraction layer. For an example of how we build automated data pipelines, see our BOM weather data pipeline case study. Kadoa is a good fit for teams that need structured data from the web but do not have developers available to build and maintain traditional scraping scripts.

ZenRows is a web scraping API built to extract data from websites that actively block automated access. It handles anti-bot protections, CAPTCHAs, JavaScript rendering, and IP rotation behind a single API call, so your team gets clean structured data without building and maintaining scraping infrastructure. Most businesses hit the same wall: they need competitor pricing, product catalogues, or market data from websites that detect and block scrapers within minutes. ZenRows solves this by routing requests through residential proxies, rendering JavaScript-heavy pages in headless browsers, and rotating fingerprints automatically. You send a URL, you get back HTML or parsed JSON. At Osher, we connect ZenRows into broader automated data processing pipelines using n8n. A typical setup pulls data from target sites via ZenRows, cleans and transforms it through an n8n workflow, then loads it into a database or dashboard for analysis. We have built similar pipelines for insurance and property clients who needed external data feeds running reliably without manual intervention. See our BOM weather data pipeline case study for a real example of automated data extraction at scale. ZenRows is particularly useful for teams in e-commerce, market research, and competitive intelligence who need ongoing access to web data but lack the engineering resources to maintain proxy infrastructure and bypass logic themselves.