In the GenAI era, I’ve really enjoyed how tools like LLMs can boost productivity in day-to-day operations. However, I found that NiFi’s Web UI doesn’t integrate very naturally with GenAI workflows. I also explored options like MCP Server to bridge that gap, but it requires additional setup and a specific skillset. Recently, I discovered a much simpler approach—leveraging NiFi’s REST APIs directly. Without any MCP setup, we can use LLMs as an operations co-pilot to extract, audit, and document NiFi flows and Ranger policies. What Is Vibe Coding for NiFi Development? Vibe coding means you describe your intent in natural language and let AI write the code. Applied to CDP NiFi flow development, it looks like this: You say: "Show me all Process Groups and their processor counts" AI does: Generates the correct  curl  commands, calls the NiFi REST API, parses the JSON, and returns a clean summary table As a NiFi flow developer, you spend most of your time designing data pipelines -- not memorizing API paths. By pairing an AI assistant (like Claude) with the NiFi & Ranger REST APIs, you can: Develop your flow with description language Explore your flow topology and processor configurations instantly Inspect Controller Services, connections, and versioning without clicking through the UI Review Ranger security policies that govern your flows Document your flow architecture for team handoff or migration Iterate faster -- ask follow-up questions, drill into specific Process Groups, compare environments This article provides ready-to-use prompt templates to get started. Prerequisites A running CDP environment with NiFi and Ranger services Workload credentials (username/password) with API access Network access to the CDP management endpoints (direct or via proxy) An AI assistant (Claude, ChatGPT, etc.) that can execute or generate  curl  commands Step 1: Set Up Environment Variables Store your endpoints and credentials as environment variables. Never hardcode credentials in scripts or documents. # --- Credentials (store in a separate file, e.g. .cdp-creds, and gitignore it) --- WORKLOAD_USERNAME=<your-workload-username> WORKLOAD_PASSWORD=<your-workload-password> # --- NiFi Endpoints --- # Find these in CDP > Data Hub > your NiFi cluster > Endpoints tab NIFI_BASE_HOST=<nifi-management-host>.cloudera.site NIFI_BASE_URL=https://$NIFI_BASE_HOST/<cluster-name>/cdp-proxy-api/nifi-app/nifi-api/ # --- Ranger Endpoints --- # Find these in CDP > Data Lake > Endpoints tab DATA_LAKE_GATEWAY=<datalake-gateway-host>.cloudera.site RANGER_BASE_URL=https://$DATA_LAKE_GATEWAY/<datalake-name>/cdp-proxy-api/ranger/ Tip: Create a  .cdp-creds  file with  chmod 600 , add it to  .gitignore , and  source  it before running commands. Step 2: Verify Connectivity CDP in Public Subnet (Direct Access) # Test NiFi connectivity curl -s "${NIFI_BASE_URL}system-diagnostics" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -o /dev/null -w "NiFi: HTTP %{http_code}\n" # Test Ranger connectivity curl -s "${RANGER_BASE_URL}service/public/v2/api/service" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -o /dev/null -w "Ranger: HTTP %{http_code}\n" # Expected output: # NiFi: HTTP 200 # Ranger: HTTP 200 CDP in Private Subnet (via SOCKS5 Proxy) If your CDP cluster is in a private subnet, use SSH dynamic forwarding through a jump host: # Architecture: Local Machine --> Bastion/Jump Host --> CDP Cluster Internal Network # Step 1: Set up SOCKS5 proxy via SSH tunnel to your jump host ssh -D 1084 -N -f user@jump-host # Step 2: Use socks5h:// (the 'h' means remote DNS resolution) curl -s --proxy socks5h://127.0.0.1:1084 \ "${NIFI_BASE_URL}system-diagnostics" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -o /dev/null -w "NiFi: HTTP %{http_code}\n" # socks5h:// resolves hostnames on the proxy side (jump host) # No need for --resolve hacks or /etc/hosts modifications Step 3: Start Vibe Coding--Prompt Templates Below are ready-to-use prompt templates. Copy one to your AI assistant, replace  {{ }}  placeholders, and let it do the heavy lifting. Prompt 1: Explore Flow Topology "I just joined the project -- give me a map of what's running." You are a NiFi flow developer assistant. Help me understand the current flow topology via the NiFi REST API. ## Connection Info - NiFi API Base URL: {{ NIFI_BASE_URL }} - Authentication: Basic Auth - Username/Password: {{ WORKLOAD_USERNAME }} / {{ WORKLOAD_PASSWORD }} ## Tasks Use curl to complete the following and output results as structured Markdown: 1. Get all Process Group list (name, ID, status) GET /process-groups/root/process-groups 2. Get the complete flow configuration of a specific Process Group GET /process-groups/{id}/flow 3. List all Controller Services (type, name, status, properties) GET /flow/controller/controller-services 4. Export a specific Process Group as template JSON POST /process-groups/{id}/templates/export ## Output Format - Summarize Processor list in a Markdown table (name, type, status, scheduling strategy) - Save complete JSON configuration in code blocks - Flag sensitive properties that require manual review (passwords, keys, etc.) Prompt 2: Inspect Flow Versioning & Registry "Which flows are version-controlled? What changed in the last release?" You are a NiFi flow developer assistant. Help me inspect Registry and version control configuration via the NiFi REST API. ## Connection Info - NiFi API Base URL: {{ NIFI_BASE_URL }} - Authentication: Basic Auth ({{ WORKLOAD_USERNAME }}) ## Tasks 1. List all Registry Client configurations GET /controller/registry-clients 2. View versioned Process Group list GET /flow/registries/{registryId}/buckets/{bucketId}/flows 3. Get a specific version's Flow snapshot GET /buckets/{bucketId}/flows/{flowId}/versions/{versionNumber} ## Output - Registry inventory (name, URL, type) - Version history table for each Flow Prompt 3: Review Ranger Security Policies "Who has access to what? Are there any overly permissive policies?" You are a NiFi flow developer assistant. Help me review the Ranger security policies that govern my NiFi flows. ## Connection Info - Ranger API Base URL: {{ RANGER_BASE_URL }} - Authentication: Basic Auth ({{ WORKLOAD_USERNAME }}) ## Tasks 1. Get all Service (plugin) list GET /service/public/v2/api/service 2. Get all Policies for a specific Service GET /service/public/v2/api/policy?serviceName={{ SERVICE_NAME }} 3. Get all users and user groups GET /service/xusers/users GET /service/xusers/groups 4. Get role list GET /service/roles/roles ## Output Format - Policy summary table: Policy Name | Resource Path | Allowed Users/Groups | Permissions | Enabled - User-Role mapping table - Flag high-privilege policies (containing * wildcard or admin permissions) Prompt 4: Audit NiFi-Specific Ranger Policies "My processor is getting 'access denied' -- what Ranger policies apply to my flow?" You are a NiFi flow developer assistant. Help me find and audit the Ranger policies specific to NiFi. ## Connection Info - Ranger API Base URL: {{ RANGER_BASE_URL }} - Authentication: Basic Auth ({{ WORKLOAD_USERNAME }}) ## Tasks 1. Find NiFi Service definition GET /service/public/v2/api/service?serviceType=nifi 2. Get all policies under that Service GET /service/public/v2/api/policy?serviceName={{ NIFI_SERVICE_NAME }} 3. Export policies as JSON file (for backup or migration) ## Output - NiFi resource path permission inventory - Access control policies for each Process Group - Flag entries that conflict with or duplicate NiFi built-in policies Prompt 5: Full Flow Documentation for Migration "We're migrating to a new environment -- generate the complete inventory." You are a NiFi flow developer assistant. Generate a complete configuration inventory of NiFi flows and Ranger policies for documentation and migration. ## Connection Info - NiFi API Base URL: {{ NIFI_BASE_URL }} - Ranger API Base URL: {{ RANGER_BASE_URL }} - Authentication: Basic Auth ({{ WORKLOAD_USERNAME }}) ## Execution Steps (in order) ### Step 1: NiFi Flow Inventory - Get all Process Groups (including hierarchy) - Count Processor quantities and type distribution in each Group ### Step 2: NiFi Controller Services - List all Controller Services (type, name, status) - Flag critical services such as DBCPConnectionPool, SSLContextService, etc. ### Step 3: Ranger Policy Inventory - Get all NiFi-related Services and Policies - Output Policy permission matrix ### Step 4: Summary Report Generate a Markdown report containing: - NiFi flow architecture overview (Mermaid diagram) - Controller Service configuration table - Ranger permission matrix - Discovered issues or recommendations ## Output Files - nifi-flow-inventory.md - ranger-policy-matrix.md - full-config-report.md API Quick Reference NiFi REST API Operation Method Path Get root Process Group GET /process-groups/root List child Process Groups GET /process-groups/{id}/process-groups Get Flow details GET /process-groups/{id}/flow List Processors GET /process-groups/{id}/processors List Connections GET /process-groups/{id}/connections List Controller Services GET /flow/controller/controller-services Get system diagnostics GET /system-diagnostics Get cluster status GET /controller/cluster Token authentication POST /access/token Ranger REST API Operation Method Path List all Services GET /service/public/v2/api/service List all Policies GET /service/public/v2/api/policy Query Policies by Service GET /service/public/v2/api/policy?serviceName={name} Create Policy POST /service/public/v2/api/policy List users GET /service/xusers/users List user groups GET /service/xusers/groups List roles GET /service/roles/roles Service type definitions GET /service/public/v2/api/servicedef The Vibe Coding Workflow Set up environment variables and credentials ( .cdp-creds  file) Verify connectivity with a quick  curl  test against  /system-diagnostics Describe what you want to know or do -- in natural language AI generates the correct  curl  commands targeting NiFi/Ranger APIs AI parses the JSON responses and produces structured reports Iterate -- ask follow-up questions, drill into specific flows, compare environments Real-World Example: Building a CSV-to-S3 Ingestion Flow with AI Here's what vibe coding NiFi development looks like in practice. Instead of dragging processors in the UI and manually configuring each one, you have a conversation with AI: The Conversation You: I need to build a flow that picks up CSV files from a local directory, validates the schema, converts them to Parquet, and writes to S3. Set it up in my "Data-Ingestion" Process Group. AI responds with the exact REST API calls: # Step 1: Find the target Process Group curl -s "${NIFI_BASE_URL}process-groups/root/process-groups" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" | jq '.processGroups[] | {name: .component.name, id: .id}' # Step 2: Create a GetFile processor curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/processors" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.processors.standard.GetFile", "name": "Pick up CSV files", "config": { "properties": { "Input Directory": "/data/incoming/csv", "File Filter": "[^\\.].*\\.csv", "Keep Source File": "false" } } } }' # Step 3: Create a ValidateRecord processor for schema validation curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/processors" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.processors.standard.ValidateRecord", "name": "Validate CSV Schema", "config": { "properties": { "Record Reader": "<csv-reader-controller-service-id>", "Record Writer": "<csv-writer-controller-service-id>" } } } }' # Step 4: Create a ConvertRecord processor (CSV -> Parquet) curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/processors" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.processors.standard.ConvertRecord", "name": "Convert CSV to Parquet", "config": { "properties": { "Record Reader": "<csv-reader-controller-service-id>", "Record Writer": "<parquet-writer-controller-service-id>" } } } }' # Step 5: Create a PutS3Object processor curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/processors" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.processors.aws.s3.PutS3Object", "name": "Write Parquet to S3", "config": { "properties": { "Bucket": "my-data-lake-bucket", "Object Key": "ingested/${now():format('\''yyyy/MM/dd'\'')}/${filename}.parquet" } } } }' # Step 6: Connect the processors in sequence # GetFile -> ValidateRecord -> ConvertRecord -> PutS3Object curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/connections" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "source": {"id": "<getfile-id>", "type": "PROCESSOR"}, "destination": {"id": "<validate-id>", "type": "PROCESSOR"}, "selectedRelationships": ["success"] } }' # ... (AI generates the remaining connections) Then You Iterate You: "Add error handling -- route validation failures to a separate PutFile that writes to  /data/rejected/ " AI generates the additional processor and connection API calls. You: "What Controller Services do I need for the CSV reader and Parquet writer?" AI creates the  CSVReader  and  ParquetRecordSetWriter  Controller Services: # Create CSVReader Controller Service curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/controller-services" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.csv.CSVReader", "name": "CSV Reader", "properties": { "Schema Access Strategy": "Use String Fields From Header", "Date Format": "yyyy-MM-dd", "Timestamp Format": "yyyy-MM-dd HH:mm:ss" } } }' # Create ParquetRecordSetWriter Controller Service curl -s -X POST "${NIFI_BASE_URL}process-groups/<pg-id>/controller-services" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{ "revision": {"version": 0}, "component": { "type": "org.apache.nifi.parquet.ParquetRecordSetWriter", "name": "Parquet Writer", "properties": { "Schema Access Strategy": "Inherit Record Schema", "Compression Type": "SNAPPY" } } }' # Enable the Controller Services curl -s -X PUT "${NIFI_BASE_URL}controller-services/<service-id>/run-status" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{"revision": {"version": 1}, "state": "ENABLED"}' You: "Now start the entire Process Group." # Start all processors in the Process Group curl -s -X PUT "${NIFI_BASE_URL}flow/process-groups/<pg-id>" \ -u "$WORKLOAD_USERNAME:$WORKLOAD_PASSWORD" \ -H "Content-Type: application/json" \ -d '{"id": "<pg-id>", "state": "RUNNING"}' What Just Happened? In a few minutes of conversation, you built a complete ingestion pipeline: No UI clicking. No API doc searching. No API doc searching. Just describing, reviewing, and iterating.  Security Best Practices Never commit credentials to Git. Use environment variables loaded from a gitignored file. Use  chmod 600  on credential files to restrict read access. Rotate workload passwords regularly through CDP Management Console. Review AI output before acting on any write operations -- always treat AI suggestions as recommendations, not commands. Use read-only operations (GET) for exploration. Only use write operations (POST/PUT/DELETE) after careful review. Conclusion Vibe coding flips the NiFi development workflow: instead of navigating the UI or looking up REST API docs, you describe your intent and let AI handle the plumbing. Whether you're onboarding to an existing project, debugging access control issues, or preparing a migration inventory, these prompt templates let you stay in the flow -- pun intended. Applicable to: Cloudera Data Platform (CDP) Public Cloud -- Data Hub NiFi clusters and Data Lake Ranger services. ... View more

This guide provides a step-by-step approach to extracting data from SAP S/4HANA via OData APIs, processing it using Apache NiFi in Cloudera Data Platform (CDP), and storing it in an Iceberg-based Lakehouse for analytics and AI workloads.   1. Introduction 1.1 Why Move SAP S/4HANA Data to a Lakehouse? SAP S/4HANA is a powerful ERP system designed for transactional processing, but it faces limitations when used for analytics, AI, and large-scale reporting: Performance Impact: Running complex analytical queries directly on SAP can degrade system performance. Limited Scalability: SAP systems are not optimized for big data workloads (e.g., petabyte-scale analytics). High Licensing Costs: Extracting and replicating SAP data for analytics can be expensive if done inefficiently. Lack of Flexibility: SAP’s data model is rigid, making it difficult to integrate with modern AI/ML tools. A Lakehouse architecture (built on Apache Iceberg in CDP) solves these challenges by: Decoupling analytics from SAP – Reduce operational load on SAP while enabling scalable analytics. Supporting structured & unstructured data – Unlike SAP’s tabular model, a Lakehouse can store JSON, text, and IoT data. Enabling ACID compliance – Iceberg ensures transactional integrity (critical for financial and inventory data). Reducing costs – Store historical SAP data in cheaper object storage (S3, ADLS) rather than expensive SAP HANA storage.   1.2 Why Use OData API for SAP Data Extraction? SAP provides several data extraction methods, but OData (Open Data Protocol) is one of the most efficient for real-time replication:   Method Pros Cons Best For OData API Real-time, RESTful, easy to use Requires pagination handling Incremental, near-real-time syncs SAP BW/Extractors SAP-native, optimized for BW Complex setup, not real-time Legacy SAP BW integrations Database Logging (CDC) Low latency, captures all changes High SAP system overhead Mission-critical CDC use cases SAP SLT (Trigger-based) Real-time, no coding needed Expensive, SAP-specific Large-scale SAP replication Why OData wins for Lakehouse ingestion? REST-based – Works seamlessly with NiFi’s InvokeHTTP processor. Supports filtering ($filter) – Enables incremental extraction (e.g., modified_date gt ‘2024-01-01’). JSON/XML output – Easy to parse and transform in NiFi before loading into Iceberg. 1.3 Why Apache NiFi in Cloudera Data Platform (CDP)? NiFi is the ideal tool for orchestrating SAP-to-Lakehouse pipelines because: Low-Code UI: Drag-and-drop processors simplify pipeline development (vs. writing custom Spark/PySpark code). Built-in SAP Connectors: Use InvokeHTTP for SAP S/4 HANA OData for deeper integrations. Scalability & Fault Tolerance: Backpressure handling – Prevents SAP API overload. Automatic retries – If SAP API fails, NiFi retries without data loss. 2. Prerequisites Before building the SAP S/4HANA → NiFi → Iceberg pipeline, ensure the following components and access rights are in place. Cloudera Data Platform (CDP) with: Apache NiFi (for data ingestion) Apache Iceberg (as the Lakehouse table format) Storage: HDFS or S3 (via Cloudera SDX) SAP S/4HANA access with OData API permissions T-Code SEGW: Confirm OData services are exposed (e.g., API_MATERIAL_SRV).   Permissions: SAP User Role: Must include S_ODATA and S_RFC authorizations. Whitelist NiFi IP if SAP has network restrictions. Test OData Endpoints curl -u "USER:PASS" "https://sap-odata.example.com:443/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=2" Validate: Pagination ($skip, $top). Filtering ($filter=LastModified gt '2025-05-01'). Basic knowledge of NiFi flows, SQL, and Iceberg 3. Architecture Overview Data movement： SAP S/4HANA (OData API) → Apache NiFi (CDP) → Iceberg Tables (Lakehouse) → Analytics (Spark, Impala, Hive) Archtecture Overview : 4. Step-by-Step Implementation Step 1: Identify SAP OData Endpoints SAP provides OData services for tables like: MaterialMaster (MM) SalesOrders (SD) FinancialDocuments (FI) Example endpoint: https://<SAP_HOST>:<PORT>/sap/opu/odata/sap/API_SALES_ORDER_SRV/A_SalesOrder?$top=2       Step 2: Configure NiFi to Extract SAP Data Use InvokeHTTP processor to call SAP OData API. Configure authentication (Basic Auth). Handle pagination ($skip & $top parameters).   To get the JSON response, I added  Accept=application/json Property. Parse JSON responses using EvaluateJsonPath or JoltTransformJSON.   Step 3: Transform Data in NiFi Filter & clean data using: ReplaceText (for SAP-specific formatting) QueryRecord (to convert JSON to Parquet/AVRO) Enrich data (e.g., join with reference tables). Check the Data using Provinance : Step 4: Load into Iceberg Lakehouse   Use PutIceberg processor (NiFi 1.23+) to write directly to Iceberg.   Alternative Option: Write to HDFS/S3 as Parquet, then use Spark SQL to load into Iceberg   CREATE TABLE iceberg_db.sap_materials ( material_id STRING, material_name STRING, created_date TIMESTAMP ) STORED AS ICEBERG;   5. Conclusion By leveraging Cloudera’s CDP, NiFi, and Iceberg, organizations can efficiently move SAP data into a modern Lakehouse, enabling real-time analytics, ML, and reporting without impacting SAP performance. Next Steps Explore Cloudera Machine Learning (CML) for SAP data analytics. ... View more