Hands-on Lab — DP-700 Microsoft Fabric Data Engineer Associate

Overview

Prerequisites

Steps

1. 1.Provider, Resource Group, naming

   Standard Azure opener.

   Copy

   terraform {
     required_version = ">= 1.5"
   
     required_providers {
       azurerm = { source = "hashicorp/azurerm", version = "~> 4.0" }
       random  = { source = "hashicorp/random",  version = "~> 3.6" }
     }
   }
   
   provider "azurerm" {
     features {}
   }
   
   resource "random_id" "suffix" {
     byte_length = 3
   }
   
   data "azurerm_client_config" "current" {}
   
   locals {
     tags = {
       Project   = "certlabpro-dp-700"
       ManagedBy = "terraform"
     }
   }
   
   resource "azurerm_resource_group" "main" {
     name     = "certlabpro-dp-700-rg"
     location = "eastus"
     tags     = local.tags
   }

2. 2.Provision an Event Hubs namespace + hub as the streaming source

   Provisions:

   • Azure Event Hubs

   Event Hubs is Azure's high-throughput event ingestion service — the canonical source for streaming data into Fabric Event Streams. DP-700's Implement and manage data ingestion domain tests this combo directly: real-time events land in Event Hubs, Fabric Event Streams subscribe and route them into a Lakehouse or KQL database, downstream queries see the data within seconds.

   We use the Basic tier (cheapest, single throughput unit) with 1-day retention. The exam's Standard vs Premium trade-off question is about capture-to-storage features (Capture writes events to ADLS automatically) and longer retention — Basic doesn't have those, but it's the lab-cheap option.

   Copy

   resource "azurerm_eventhub_namespace" "main" {
     name                = "ehns-dp700-${random_id.suffix.hex}"
     resource_group_name = azurerm_resource_group.main.name
     location            = azurerm_resource_group.main.location
     sku                 = "Basic"
     capacity            = 1
   
     tags = local.tags
   }
   
   resource "azurerm_eventhub" "stream" {
     name              = "ingest"
     namespace_id      = azurerm_eventhub_namespace.main.id
     partition_count   = 2
     message_retention = 1
   }

3. 3.Set up ADLS Gen2 medallion architecture for the lakehouse

   Provisions:

   • Azure Storage

   The medallion architecture (bronze / silver / gold) is the DP-700 Implement and manage an analytics solution reference data-modeling pattern: raw ingested data in bronze/, cleaned and validated in silver/, business-aggregated in gold/. Fabric Lakehouse tables live on top of this exact layout.

   We provision an ADLS Gen2 storage account (hierarchical namespace = on, required for Delta Lake tables) with the three container layers. Fabric Lakehouse shortcuts can point at this storage account directly, so Fabric reads/writes here without copying data into OneLake.

   Copy

   resource "azurerm_storage_account" "lake" {
     name                            = "dp700lake${random_id.suffix.hex}"
     resource_group_name             = azurerm_resource_group.main.name
     location                        = azurerm_resource_group.main.location
     account_tier                    = "Standard"
     account_replication_type        = "LRS"
     account_kind                    = "StorageV2"
     is_hns_enabled                  = true
   
     https_traffic_only_enabled      = true
     min_tls_version                 = "TLS1_2"
     allow_nested_items_to_be_public = false
   
     tags = local.tags
   }
   
   resource "azurerm_storage_container" "bronze" {
     name                  = "bronze"
     storage_account_id    = azurerm_storage_account.lake.id
     container_access_type = "private"
   }
   
   resource "azurerm_storage_container" "silver" {
     name                  = "silver"
     storage_account_id    = azurerm_storage_account.lake.id
     container_access_type = "private"
   }
   
   resource "azurerm_storage_container" "gold" {
     name                  = "gold"
     storage_account_id    = azurerm_storage_account.lake.id
     container_access_type = "private"
   }

4. 4.Provision the Fabric capacity + Log Analytics observability

   Provisions:

   • Microsoft Fabric
   • Azure Monitor

   Same Fabric capacity primitive as DP-600 — the F2 SKU is the smallest dev-friendly size. The DP-700 angle is different though: where DP-600 focused on the analytics modeling workloads, DP-700 focuses on the data-ingestion side. Eventstreams, Spark notebooks, and Data Pipelines all run inside this capacity.

   Log Analytics receives the Fabric capacity diagnostics (throttling, query duration) — DP-700's Maintain and optimize domain leans on this for incident response when a streaming pipeline starts dropping events.

   With capacity in place, the full DP-700 streaming substrate is shaped: Event Hubs → Fabric Event Stream → Lakehouse table on the ADLS Gen2 storage account. The application work (creating the Event Stream, defining the Lakehouse, writing transforms) happens in the Fabric portal.

   Copy

   resource "azurerm_fabric_capacity" "main" {
     name                = "fab-dp700-${random_id.suffix.hex}"
     resource_group_name = azurerm_resource_group.main.name
     location            = azurerm_resource_group.main.location
   
     administration_members = [
       data.azurerm_client_config.current.object_id,
     ]
   
     sku {
       name = "F2"
       tier = "Fabric"
     }
   
     tags = local.tags
   }
   
   resource "azurerm_log_analytics_workspace" "main" {
     name                = "log-dp700"
     resource_group_name = azurerm_resource_group.main.name
     location            = azurerm_resource_group.main.location
     sku                 = "PerGB2018"
     retention_in_days   = 30
   
     tags = local.tags
   }
   
   resource "azurerm_monitor_diagnostic_setting" "fabric" {
     name                       = "diag"
     target_resource_id         = azurerm_fabric_capacity.main.id
     log_analytics_workspace_id = azurerm_log_analytics_workspace.main.id
   
     metric {
       category = "AllMetrics"
       enabled  = true
     }
   }
   
   resource "azurerm_monitor_diagnostic_setting" "event_hubs" {
     name                       = "diag"
     target_resource_id         = azurerm_eventhub_namespace.main.id
     log_analytics_workspace_id = azurerm_log_analytics_workspace.main.id
   
     enabled_log {
       category_group = "allLogs"
     }
   
     metric {
       category = "AllMetrics"
       enabled  = true
     }
   }

Cleanup