Restructure Memgraph in production (#1276)

* Restructure Memgraph in production

* emphasize order

Author: katarinasupe

pages/_meta.ts

@@ -13,7 +13,6 @@ export default {
   "database-management": "Database management",
   "deployment": "Deployment",
   "clustering": "Clustering",
-  "memgraph-in-production": "Memgraph in production",
   "data-streams": "Data streams",
   "help-center": "Help center",
   "release-notes": "Release notes",

pages/deployment.mdx

@@ -3,48 +3,43 @@ title: Deployment
 description: Learn all about deployment strategies and features in Memgraph.
 ---
 
+import { Callout } from 'nextra/components'
 import {CommunityLinks} from '/components/social-card/CommunityLinks'
 
 
 # Deployment
 
-Deploy Memgraph using methods that suit your environment, whether it's containerized with Docker or a native Linux installation.
+Whether you're getting started with best practices, optimizing for specific
+workloads, or fine-tuning performance, this section brings together everything
+you need to run Memgraph effectively in production. Explore tailored [deployment
+guides](#deployment-guides), [environment-specific instructions](#environments),
+and [benchmarking strategies](#benchmarking) to ensure your graph applications
+scale reliably and efficiently.
 
-## Docker deployment
+## Deployment guides
 
-Deploy Memgraph in a [Docker environment](/deployment/docker). Learn how to
-install and run Memgraph using Docker images (`memgraph/memgraph-mage` or
-`memgraph/memgraph`). Configure Memgraph through the `memgraph.conf` file or
-Docker command flags, secure the database with user credentials and SSL, and
-ensure data persistence with Docker volumes. Backup and restore data
-efficiently, set up a replication cluster for high availability, and manage logs
-for troubleshooting and performance monitoring.
+To simplify running Memgraph in a production environment, we’ve created a series
+of guides to walk you through the process. **For the best results, follow them in
+this order**:
 
-## Native Linux deployment
+1. **Begin with the [Deployment best practices guide](/deployment/best-practices)**.
+   It outlines general best practices that apply to most production setups,
+   regardless of workload type or specific use case.
 
-Install Memgraph directly on a [Linux system](/deployment/linux). Follow steps
-to download and install Memgraph, connect using `mgconsole` or client libraries,
-and configure settings via the `memgraph.conf` file. Secure your database with
-user management and SSL encryption. Ensure data persistence by managing data
-directories, and perform backups and restores. Set up a replication cluster for
-improved data availability and monitor logs for system insights.
+2. **Then, go to the [Workloads](/deployment/workloads) section**. Each guide there
+   focuses on a specific workload or deployment scenario. At the start of each
+   guide, you'll find information on when that scenario is a good fit for your
+   needs, along with tailored recommendations.
 
-## AWS deployment
+## [Environments](/deployment/environments)
 
-Deploy Memgraph on [AWS](/deployment/aws). Learn how to launch Memgraph using
-Amazon EC2 instances and how to configure instance settings for optimal
-performance.
+Learn how to deploy Memgraph using methods that suit your environment, whether
+it’s containerized with Docker or a native Linux installation.
 
-## GCP deployment
+## [Benchmarking](/deployment/benchmarking-memgraph)
 
-Deploy Memgraph on [Google Cloud Platform (GCP)](/deployment/gcp). Follow
-instructions to set up Memgraph on Google Compute Engine and how to configure VM
-instances.
+Get hands-on with benchmarking strategies to evaluate Memgraph’s performance.
+This guide walks through stress testing, analyzing key metrics, and comparing
+real-world workloads to ensure scalability and responsiveness.
 
-## Azure deployment
-
-Deploy Memgraph on [Microsoft Azure](/deployment/azure). Discover how to deploy
-Memgraph using Azure Virtual Machines and how to configure storage and
-networking settings.
-
-<CommunityLinks/>
+<CommunityLinks />

pages/deployment/_meta.ts

@@ -1,7 +1,6 @@
 export default {
-    "docker": "Docker",
-    "linux": "Linux",
-    "aws": "AWS",
-    "gcp": "GCP",
-    "azure": "Azure"
+    "best-practices": "Best practices",
+    "workloads": "Workloads",
+    "environments": "Environments",
+    "benchmarking-memgraph": "Benchmarking",
 }

pages/memgraph-in-production/benchmarking-memgraph.mdx renamed to pages/deployment/benchmarking-memgraph.mdx

@@ -0,0 +1,50 @@
+---
+title: Environments
+description: Learn all about deployment strategies and features in Memgraph.
+---
+
+import {CommunityLinks} from '/components/social-card/CommunityLinks'
+
+
+# Environments
+
+Deploy Memgraph using methods that suit your environment, whether it's containerized with Docker or a native Linux installation.
+
+## Docker deployment
+
+Deploy Memgraph in a [Docker environment](/deployment/docker). Learn how to
+install and run Memgraph using Docker images (`memgraph/memgraph-mage` or
+`memgraph/memgraph`). Configure Memgraph through the `memgraph.conf` file or
+Docker command flags, secure the database with user credentials and SSL, and
+ensure data persistence with Docker volumes. Backup and restore data
+efficiently, set up a replication cluster for high availability, and manage logs
+for troubleshooting and performance monitoring.
+
+## Native Linux deployment
+
+Install Memgraph directly on a [Linux system](/deployment/linux). Follow steps
+to download and install Memgraph, connect using `mgconsole` or client libraries,
+and configure settings via the `memgraph.conf` file. Secure your database with
+user management and SSL encryption. Ensure data persistence by managing data
+directories, and perform backups and restores. Set up a replication cluster for
+improved data availability and monitor logs for system insights.
+
+## AWS deployment
+
+Deploy Memgraph on [AWS](/deployment/aws). Learn how to launch Memgraph using
+Amazon EC2 instances and how to configure instance settings for optimal
+performance.
+
+## GCP deployment
+
+Deploy Memgraph on [Google Cloud Platform (GCP)](/deployment/gcp). Follow
+instructions to set up Memgraph on Google Compute Engine and how to configure VM
+instances.
+
+## Azure deployment
+
+Deploy Memgraph on [Microsoft Azure](/deployment/azure). Discover how to deploy
+Memgraph using Azure Virtual Machines and how to configure storage and
+networking settings.
+
+<CommunityLinks/>

pages/memgraph-in-production/general-suggestions.mdx renamed to pages/deployment/best-practices.mdx

@@ -0,0 +1,7 @@
+export default {
+    "docker": "Docker",
+    "linux": "Linux",
+    "aws": "AWS",
+    "gcp": "GCP",
+    "azure": "Azure"
+}

pages/deployment/environments.mdx

@@ -0,0 +1,60 @@
+---
+title: Workloads
+description: Learn how to deploy Memgraph in production for your workload and consider all the advices directly from the Memgraph Team based on our multi-year experiences.
+---
+
+import { Callout } from 'nextra/components'
+import {CommunityLinks} from '/components/social-card/CommunityLinks'
+
+# Workloads
+
+When deploying Memgraph in production, it is essential to consider a set of prerequisites to ensure optimal **performance**, **scalability**
+and **resilience**. That includes decisions about hardware configurations and integration strategies. 
+
+This section provides guides that are the starting point to production-readiness with Memgraph.
+
+Before you dive into specific setups, it's important to think about:
+- **Hardware requirements** and **instance sizing**
+- **Driver configuration** 
+- **Flags**  when starting Memgraph 
+- **Data import** best practices
+- Connecting to **external sources** 
+
+These factors ensure Memgraph performs effectively in your environment, no matter the use case.
+
+## How to use these guides
+
+**Start with the [Deployment best practices](/deployment/best-practices)**. That page covers the **best practices** that apply to most
+production deployments, regardless of your workload type and are **agnostic to specific use cases**.
+Each separate guide in the Workloads section focuses on a particular type of workload or deployment scenario.
+At the beginning of each guide, you’ll learn when that **use case** is a good fit for your needs and specific tailored **recommendations**.
+
+<Callout type="warning">
+Recommendations in those guides **override** anything written in the
+general suggestions when there's a conflict, so always defer to the targeted guide when applicable.
+</Callout>
+
+## Available guides
+
+Here are the currently available guides to help you deploy Memgraph effectively:
+
+### [Memgraph in high-throughput workloads](/deployment/workloads/memgraph-in-high-throughput-workloads)
+Scale your write throughput while keeping up with fast-changing, high-velocity graph data.
+
+### [Memgraph in GraphRAG use cases](/deployment/workloads/memgraph-in-graphrag)
+Learn how to optimize Memgraph for Retrieval-Augmented Generation (RAG) systems using graph data.
+
+## 🚧 Guides in construction
+- Memgraph in transactional workloads
+- Memgraph in analytical workloads
+- Memgraph in mission critical workloads
+- Memgraph in supply chain use cases
+- Memgraph in cybersecurity use cases
+- Memgraph in fraud detection use cases
+
+<Callout type="info">
+If you'd like to help us **prioritize** this content, feel free to reach out on [Discord](https://discord.gg/memgraph)!   
+Your feedback helps us build what matters most. 
+</Callout>
+
+<CommunityLinks/>

pages/deployment/environments/_meta.ts

@@ -1,6 +1,4 @@
 export default {
-  "general-suggestions": "General suggestions",
   "memgraph-in-graphrag": "Memgraph in GraphRAG use cases",
   "memgraph-in-high-throughput-workloads": "Memgraph in high-throughput workloads",
-  "benchmarking-memgraph": "Benchmarking Memgraph",
 }

pages/deployment/aws.mdx renamed to pages/deployment/environments/aws.mdx

@@ -9,8 +9,7 @@ import { CommunityLinks } from '/components/social-card/CommunityLinks'
 # Memgraph in GraphRAG use cases
 
 <Callout type="info">
-👉 **Start here first**  
-Before diving into this guide, we recommend starting with the [**General suggestions**](/memgraph-in-production/general-suggestions) 
+Before diving into this guide, we recommend starting with the [Deployment best practices](/deployment/best-practices) 
 page. It provides **foundational, use-case-agnostic advice** for deploying Memgraph in production.
 
 This guide builds on that foundation, offering **additional recommendations tailored to specific workloads**. 
@@ -23,11 +22,11 @@ on the unique needs of your use case.
 This guide is for you if you're exploring or building **GraphRAG (Graph-Augmented Retrieval-Augmented Generation)** systems.
 You'll benefit from this content if:
 
-- 💬 You want to **query your graph using natural language** through a proxy **LLM** interface.  
-- 🔄 You need to **seamlessly extract knowledge graphs from multiple source systems**, especially when a graph structure naturally fits the data.
-- 🏢 You're building a system where **business stakeholders need fast insights** without relying on engineers to expose data through APIs.  
-- 🌙 Your engineers are **deep in the trenches at midnight**, and you'd rather they write **simple questions** instead of Cypher queries.  
-- 🧠 You have **embeddings** and need to perform **vector search** across your graph, along with structured Cypher query language expressiveness.  
+- You want to **query your graph using natural language** through a proxy **LLM** interface.  
+- You need to **seamlessly extract knowledge graphs from multiple source systems**, especially when a graph structure naturally fits the data.
+- You're building a system where **business stakeholders need fast insights** without relying on engineers to expose data through APIs.  
+- Your engineers are **deep in the trenches at midnight**, and you'd rather they write **simple questions** instead of Cypher queries.  
+- You have **embeddings** and need to perform **vector search** across your graph, along with structured Cypher query language expressiveness.  
 
 If any of these resonate with your project, this guide will walk you through the best practices and configurations to bring 
 GraphRAG to life using Memgraph.
@@ -89,10 +88,10 @@ This will give you a more accurate view of your memory needs when planning hardw
 
 Memgraph is actively working on two short-term optimizations to reduce this memory overhead:
 
-- 🧭 **Reference-based indexing**: Embeddings will be stored only in the vector index, with the property storage holding just a reference. 
+- **Reference-based indexing**: Embeddings will be stored only in the vector index, with the property storage holding just a reference. 
   Since embeddings are used exclusively for vector search, this eliminates duplication.
 
-- ⚙️ **Support for float16 in usearch**: Users will be able to store embeddings as 2-byte floats (`float16`), commonly used in neural networks. 
+- **Support for float16 in usearch**: Users will be able to store embeddings as 2-byte floats (`float16`), commonly used in neural networks. 
   This reduces memory usage significantly while maintaining accuracy within a **\<1% margin**, making it a strong tradeoff for most applications.
 
 ### Best practices
@@ -134,24 +133,24 @@ Both modes are compatible with GraphRAG workflows — choose based on your perfo
 To ensure your GraphRAG setup is secure, scalable, and suitable for multi-user environments, Memgraph offers several 
 enterprise features that are especially relevant:
 
-- 🔐 **Role-based access control**  
+- **Role-based access control**  
   GraphRAG systems often generate queries automatically via LLMs, so it’s critical to enforce strict **read-only permissions** 
   to prevent accidental data modification. Memgraph supports 
   [role-based access control](/database-management/authentication-and-authorization/role-based-access-control), 
   allowing you to limit access to **read-only roles** for GraphRAG users and services.
 
-- 🏷️ **Label-based access control**  
+- **Label-based access control**  
   For more granular permissions, Memgraph supports 
   [label-based access control](/database-management/authentication-and-authorization/role-based-access-control#label-based-access-control).
   This enables defining access rules at the **label and edge type level**, so users can only view and query the parts of the 
   graph that are relevant to them—ensuring **data isolation and privacy** across teams or tenants.
 
-- 🔐 **SSO support for GraphChat**  
+- **SSO support for GraphChat**  
   If you're using **GraphChat** inside **Memgraph Lab** to power LLM-based querying, 
   [single sign-on (SSO)](/database-management/authentication-and-authorization/auth-system-integrations#single-sign-on) makes it easy 
   to integrate with your existing identity provider and streamline user access across your organization.
 
-- 🔄 **Dynamic graph algorithms**  
+- **Dynamic graph algorithms**  
   In scenarios where the graph is **changing in real-time** and you need **continuous summarization or insights**, Memgraph 
   provides enterprise-only [dynamic graph algorithms](/advanced-algorithms/available-algorithms#dynamic-graph-algorithms-enterprise), 
   such as **online community detection**. These allow your GraphRAG pipeline to stay up-to-date with evolving data without expensive 
@@ -179,7 +178,7 @@ Memgraph’s **in-memory architecture**, offering fast and consistent response t
 
 ## Memgraph ecosystem
 
-To accelerate your GraphRAG journey, we recommend exploring the [**AI ecosystem**](/ai-ecosystem) page. There,
+To accelerate your GraphRAG journey, we recommend exploring the [AI ecosystem](/ai-ecosystem) page. There,
 you’ll find detailed information about:
 
 - **GraphChat**, Memgraph’s natural language interface powered by LLMs  

pages/deployment/azure.mdx renamed to pages/deployment/environments/azure.mdx

@@ -9,8 +9,7 @@ import { CommunityLinks } from '/components/social-card/CommunityLinks'
 # Memgraph in high-throughput workloads
 
 <Callout type="info">
-👉 **Start here first**  
-Before diving into this guide, we recommend starting with the [**General suggestions**](/memgraph-in-production/general-suggestions) 
+Before diving into this guide, we recommend starting with the [Deployment best practices](/deployment/best-practices) 
 page. It provides **foundational, use-case-agnostic advice** for deploying Memgraph in production.
 
 This guide builds on that foundation, offering **additional recommendations tailored to specific workloads**. 
@@ -24,10 +23,10 @@ This guide is for you if you're working with **high-throughput graph workloads**
 and scale are critical.
 You’ll benefit from this content if:
 
-- ⚡ You’re handling **more than a thousand writes per second**, and your graph data is constantly changing at high velocity.  
-- 🔍 You want your **read performance to remain consistent**, even as new data is continuously ingested.  
-- 🔁 You’re dealing with **high volumes of concurrent reads and writes**, and need a database that can handle both without performance degradation.  
-- 🌊 Your data is flowing in from **real-time streaming systems** like **Kafka**, and you need a database that can keep up.  
+- You’re handling **more than a thousand writes per second**, and your graph data is constantly changing at high velocity.  
+- You want your **read performance to remain consistent**, even as new data is continuously ingested.  
+- You’re dealing with **high volumes of concurrent reads and writes**, and need a database that can handle both without performance degradation.  
+- Your data is flowing in from **real-time streaming systems** like **Kafka**, and you need a database that can keep up.  
 
 If this sounds like your use case, this guide will walk you through how to configure and scale Memgraph for **reliable, high-throughput performance** in production.
 
@@ -211,7 +210,7 @@ If your payload contains **dynamic labels or edge types** and you still need **i
 - **Optionally use the [`merge`](/advanced-algorithms/available-algorithms/merge) procedure from MAGE**
 
 <Callout type="warning">
-Note: While MAGE procedures are **written in C++ and highly optimized**, they still introduce **slightly more overhead**
+While MAGE procedures are **written in C++ and highly optimized**, they still introduce **slightly more overhead**
 compared to **pure Cypher**, as they are executed as external modules. We recommend favoring pure Cypher when
 possible for the **highest performance**.
 </Callout>