48-Hour Apache NiFi Expert: Complete Pipeline Optimization

We are looking for an experienced Apache NiFi consultant to take over a midway performance optimization project for a production data pipeline system and drive it to a fully optimized, well-documented state within 48 hours. The engagement is fixed-deadline and requires autonomous work, clear communication, and deep NiFi expertise.

The pipelines are already built and partially optimized; your role is to refine, restructure where needed, and leave us with a system that is fast, stable, and easy for our team to operate and extend. You will have full access to the existing flows (under our NiFi installation and project repo) and the freedom to change components as long as data integrity and functional behaviour are preserved.

Current State of the System

Apache NiFi 2.x deployment running on a single-node environment (desktop over rdp). Data pipeline process groups with 40+ processors implementing a 7-stage flow pattern: API fetch → XML/JSON parse → detail fetch → extraction → format conversion → enrichment → database persistence. Pipelines are functionally working and in production-like use, but overall execution time is still in the range of ~2–2.5 hours for a full run. Some prior tuning has been done, but we have not yet systematically optimized the components. Your starting point is a working but sub-optimal system; the goal is to push it as far as realistically possible toward a 20–45 minute end-to-end runtime while keeping the flows robust and maintainable.

Objectives & Scope of Work

Your primary objective is to transform the current mid-way optimized pipelines into a high-performance, production-ready data flow with strong observability and documentation. Specifically, you will:

• Analyse existing NiFi flows for bottlenecks
• Optimize pipeline performance end-to-end, including scheduling strategies, concurrent tasks, batching patterns, and connection pooling for both database and HTTP/API calls.
• Refactor or replace inefficient processors where appropriate.
• Tune NiFi configuration relevant to these flows where it directly affects pipeline performance and stability.
• Produce comprehensive documentation (monitoring, logging, metrics, troubleshooting, maintainability) suitable for a DevOps/engineering team to run and extend the pipelines independently.

You will have full authority to modify process groups, processors, controller services, and scheduling strategies, as long as:

• The functional outcome and data integrity remain the same (same logical outputs, consistent row counts/records).
• The solution remains understandable and maintainable by a small engineering/DevOps team.

Key Deliverables & Acceptance Criteria

You are expected to deliver the following within 48 hours from the agreed start time:

• Optimized, Working Pipelines
• All processes run end-to-end without errors for at least one full test cycle using our test data set.
• Execution time is reduced from the current ~2–2.5 hours to ≤45 minutes for the same workload, with an ideal target of ~20 minutes if technically feasible on the given hardware.
• No connection pool exhaustion, chronic backpressure, or recurring NiFi bulletins indicating critical issues.
• Data integrity is preserved (validated via row counts / record counts, and any domain-specific checks we provide).

Performance Validation Report

A concise report (Markdown) covering:

• Baseline performance (current state when you start): total runtime, key bottlenecks observed.
• After-optimization metrics: end-to-end runtime, throughput by key stages or process groups, error rates.
• Changes you made, grouped logically (e.g., "DB connection pooling", "MergeRecord bulk inserts", "scheduling & concurrency", "repository tuning").
• Remaining bottlenecks (if any) and realistic recommendations for further improvements (e.g., hardware limits, clustering, DB limitations).

Monitoring & Logging Guide

A Markdown document focused on day-to-day observability:

• Key KPIs to watch (throughput, queue sizes, backpressure indicators, processor latency, error counts).
• How to monitor the flow in the NiFi UI (processor stats, queues, provenance where relevant, bulletin board).
• Log file locations and purpose (e.g., nifi-, bootstrap, etc.) and any specific log messages/signatures to watch for.
• Suggested alert thresholds or conditions (e.g., persistent backpressure, frequent retries, connection pool errors).

Troubleshooting Runbook

A practical runbook for our team:

• Common failure modes for these pipelines (API issues, DB issues, repository saturation, timeouts, etc.) and how to diagnose them.
• Steps to identify and resolve backpressure, slow processors, and connection pool issues.
• Guidance on safe rollback procedures for configuration changes (e.g., reverting processor settings, controller service changes, or flow versions).
• Tips for JVM/heap-related problems that commonly affect NiFi performance (high GC, OOM) and how to recognize them from logs/metrics.

Maintainability & Architecture Guide

A Markdown document that helps our team understand and evolve the system:

• High-level architecture overview of the 5 process groups and the 7-stage flow pattern (API fetch → parse → detail fetch → extraction → conversion → enrichment → DB).
• Updated diagrams or textual descriptions of each process group's role, key processors, and external dependencies (DBs, APIs, file systems, etc.).
• Processor configuration reference for critical processors and controller services that you changed (e.g., concurrency, scheduling, connection pool sizes, backpressure settings).
• Guidelines for adding new pipelines or extending existing ones while staying consistent with the optimized design (e.g., how to apply the same batching, concurrency, and monitoring patterns).
• Recommendations for routine maintenance (repository cleanup, configuration backups, version control usage for flow definitions if applicable).

Metrics & Reporting Strategy (Lightweight)

We do not expect you to build a full external monitoring stack, but we do expect to outline and, where feasible, implement lightweight metrics collection:

• Processor-level metrics to track (bytes processed, records processed, task duration) and how to use NiFi's built-in views to access them.
• Suggested approach for exporting or capturing historical performance metrics (e.g., periodic screenshots, reports, or integration points with common monitoring tools—no need to fully set them up unless trivial).
• Capacity planning notes: what to look at if data volume or concurrency doubles.

Code/Flow Review Notes for Scripting Components

If the pipelines use ExecuteScript (Python, Groovy, etc.) or custom scripting:

• Brief review notes on each critical script you touch, especially where you improve connection handling, retries, or batching.
• Comments in scripts (where appropriate) explaining key changes related to performance and resilience.

Knowledge Transfer (Recorded or Live)

A 45–60 minute walkthrough (live screen-share) that explains the final architecture, key optimizations, monitoring approach, and troubleshooting steps. Time for Q&A with our team to clarify decisions and ensure we can operate the system without you.

We are open to your suggested adjustments as long as all deliverables are met within 48 hours.

Access & Collaboration Expectations

Access method: We will provide access to the NiFi UI and relevant configuration/files via a secure channel (e.g., VPN or screen-share-assisted remote session) agreed upon before the start.

Communication: Status updates every ~4–6 hours during the 48-hour window via Upwork messages (or another agreed channel, but decisions and files must be mirrored on Upwork). Immediate notification if you are blocked for more than 1 hour by an environment, access, or data issue.

If you are a senior NiFi engineer who enjoys taking a partially-complete system and pushing it to its limits with clear, measurable outcomes and strong documentation, we would like to hear from you.

Contract duration of less than 1 month.

Mandatory skills: ETL Pipeline, PostgreSQL, Apache NiFi, Data Engineering