Apache NiFi or Apache Hop: which fits your data movement
If your team must move and transform data, the first decision is whether the work runs continuously or as scheduled batch jobs. Choose Apache NiFi when data arrives all day from many sources and must be routed, enriched and delivered as it lands. Choose Apache Hop when you design ETL pipelines on a canvas and run them on a defined schedule or trigger. Both projects are Apache-2.0, so license does not separate them.
NiFi fits always-on ingestion, content-based routing and per-record lineage. Its browser UI lets operators watch flows, and its data provenance records where each record came from and where it went, which matters when auditors or incident responders ask for proof of handling. NiFi 2.x also supports zero-leader clustering coordinated through ZooKeeper, so you can run it as a shared service. Confirm that the processors you need exist in the current release and that your Java 21 runtime and clustering model match your platform team's standards before you commit. Hop fits batch-style ETL that a data engineer designs as a pipeline. Its engine choice is a runtime setting: the same pipeline can run on the native Hop engine, on Spark, on Flink or through Beam, which helps when your cluster standard is already set. Hop also imports Kettle jobs and transformations, so an existing Pentaho Data Integration estate has a migration path rather than a rewrite.
Three drivers should settle the decision. First, continuous versus scheduled work: streams that never stop point toward NiFi, while bounded loads point toward Hop. Second, provenance need: if you must trace individual records end to end, NiFi's built-in lineage is the stronger fit, and if run-level logging is enough, Hop may suffice. Third, existing Kettle assets: a large set of .ktr and .kjb files favors Hop, and a team with no Kettle history gains little from that path. Ask each vendor or your own proof of concept to run a representative flow and report the effort, the operating model and the upgrade path before you decide.
Which platform fits which scenario
This table maps six common scenarios to the platform that fits each one better and the condition under which the other would win. Read each row against your own sources, targets and team, because the right choice usually depends on whether data moves continuously or runs on a schedule.
| Scenario | Better fit | When the other option wins |
|---|---|---|
| Always-on ingestion from many sources | Apache NiFi, because flows run continuously with back pressure and per-flow monitoring in one UI | Hop wins when ingestion is a short batch job that a scheduler can start and stop, not a process that must stay up |
| Batch ETL on a schedule | Apache Hop, because pipelines are designed as discrete units and submitted by an external scheduler | NiFi wins when the schedule is really a stream of events that arrives at unpredictable times |
| Migrating Kettle (PDI) jobs and transformations | Apache Hop, because its importer converts .kjb and .ktr files into workflows and pipelines, with documented limitations to test first | NiFi wins when the Kettle work is being rewritten as flow-based movement rather than carried over as pipelines |
| Lineage and audit of every record | Apache NiFi, because data provenance records lineage from beginning to end inside the running flow | Hop wins when lineage needs only job-level logs and run history, and record-level tracing is not a requirement |
| Spark or Flink execution | Apache Hop, because the same pipeline can run on Spark, Flink or Beam by changing the runtime configuration | NiFi wins when processing stays inside its own flows and no distributed compute engine is needed |
| Small team with limited data engineering staff | Apache Hop for scheduled work, since visual pipelines are built and tested locally before deployment | NiFi wins for a small team only if its work is mainly moving data continuously and it can staff cluster operations |
| Platform team running shared infrastructure | Apache NiFi, when it can be operated as a clustered service with ZooKeeper or Kubernetes state and shared flow registries | Hop wins when the platform team runs Hop Server or Docker images as short-lived jobs under an existing orchestrator |
How NiFi and Hop are built and deployed
Apache NiFi and Apache Hop look similar on a canvas but run very differently. One is a long-lived clustered service that keeps flows running, while the other is a design tool whose pipelines can run locally, on a remote server, or on a big-data engine. The cards below show what each model asks of the team that operates it.
NiFi runs as a clustered service
NiFi is a long-running Java service that serves its own browser UI, and flows execute continuously inside it. Operating it means managing a process that stays up, holds state and needs capacity planning for throughput. Confirm the Java 21 runtime requirement and the cluster sizing your data volumes need before you commit to a topology.
Zero-leader clustering and state
NiFi clusters coordinate through ZooKeeper, either embedded or external, or on Kubernetes using ConfigMaps or Leases for state and leader election. Your platform team owns that coordination layer alongside NiFi itself. Ask which option matches your Kubernetes or VM standards and who patches and monitors the coordination service.
Loopback binding by default
When no security configuration is supplied, NiFi binds to 127.0.0.1, so the UI is reachable only through the loopback interface. This protects a fresh install but means remote access, TLS and an authentication provider must be configured deliberately. Check the chosen provider, whether single-user, LDAP, OpenID Connect, SAML 2.0 or X.509, against your identity standards before exposing the UI.
Hop designs run on a chosen engine
Hop pipelines are designed in the Hop GUI and can run on the native engine locally, on a remote Hop Server, on Apache Spark natively or through Beam, on Flink or Google Dataflow through Beam, or on Databricks. The engine is a runtime setting, not a rewrite. Confirm which engine your data volumes need and whether your team already operates Spark, Flink or Databricks.
Hop Server is not a scheduler
Hop Server is a long-lived process that executes pipelines and workflows, but it does not decide when work starts. An external scheduler or orchestrator must submit each run, so that tool becomes a dependency you must own. Decide early whether NiFi's always-on flows, a cron job, or an orchestrator will trigger Hop runs, and who gets paged when a trigger fails.
Docker images and project packaging
Hop publishes the apache/hop image for short-lived runs or a long-lived Hop Server, and a separate apache/hop-web image for the browser-based Hop Web GUI, both built on Alpine Linux with OpenJDK 21. Projects can be deployed as Docker images, exported ZIP resources or from a Git checkout. Your team should decide which packaging path and image tagging policy it will govern through its own pipeline.
Capability comparison: NiFi against Hop
This table compares Apache NiFi and Apache Hop on licence, current release, runtime, authentication, provenance, scheduling, execution engines and connectors. Read each row as a question to answer for your own environment, since several capabilities need confirmation against the release you plan to deploy. The main takeaway is that both are Apache-2.0 and self-hostable, but NiFi is a long-running dataflow service while Hop is a design tool whose pipelines run on engines you select.
| Capability | Apache NiFi | Apache Hop |
|---|---|---|
| Licence | Apache-2.0. The project offers no paid edition. | Apache-2.0, maintained by the Apache Software Foundation. |
| Current release | 2.12.0, released September 13, 2026. | 2.19.0, released August 17, 2026. |
| Runtime requirement | Java 21 minimum. Python 3.10, 3.11 or 3.12 for optional Python processors, which are beta. | Requires Java 21. Docker images are built on OpenJDK 21. |
| Authentication options | Single-user, LDAP, OpenID Connect, SAML 2.0 and X.509 client certificates. Kerberos is deprecated. | Not covered by the supplied facts. Confirm authentication for the Hop Server and Hop Web deployment you plan to run. |
| Data provenance and lineage | Built in. Records lineage from beginning to end of each flow. | Not covered by the supplied facts. Confirm what run history and lineage Hop records for your pipelines. |
| Scheduling and triggering | Flows run continuously as always-on dataflows. | Hop Server is not a scheduler. An external scheduler or orchestrator must submit pipelines and workflows. |
| Execution engines | NiFi's own clustered Java service, with zero-leader clustering coordinated through ZooKeeper. | Native Hop engine locally or on Hop Server, Apache Spark, Apache Beam, Apache Flink, Google Dataflow or Databricks, chosen as runtime configuration. |
| Connectors and transforms | Processor catalogue for routing, transformation and movement. Confirm coverage for each source and destination you need. | 250+ pipeline transforms, 80+ workflow actions and 40+ database dialects, including PostgreSQL, Snowflake, BigQuery, MongoDB and Kafka. |
What is free and what is paid in each platform
Apache NiFi is an Apache Software Foundation project released under the Apache License 2.0, and the project itself has no paid edition. Downloading, running, clustering and using NiFi carries no license fee. The costs that remain are the infrastructure it runs on, the staff who build and operate flows, and any commercial support you choose to buy from a third party.
- Software license: The Apache-2.0 license covers use, modification and redistribution without a per-node or per-user fee from the project.
- Paid support: Commercial support, hosting or managed services come from companies outside the project, so their terms, coverage windows and SLAs vary by provider.
- Infrastructure: Clusters need compute, storage for provenance and repositories, and ZooKeeper or Kubernetes state components, all of which you pay for directly.
- Staff time: Building and maintaining flows, upgrades and security configuration are the largest recurring costs for most teams.
- Registry and extensions: Confirm the license and support status of any third-party processors or flow registry clients you add.
Before signing a support contract, confirm with the provider which NiFi line and distribution they cover, and get the coverage window and response terms in writing.
Apache Hop is an Apache-2.0 licensed data integration and orchestration platform maintained by the Apache Software Foundation, with no paid edition from the project. Running pipelines on the native engine, Hop Server or a supported engine carries no license fee from Apache. Costs appear in the runtimes you choose, such as Spark, Flink or Databricks, which have their own pricing and licensing, and in the people who design and maintain projects.
- Software license: Apache-2.0 applies to the Hop GUI, engine and Hop Server without a paid tier from the project.
- Runtime costs: Running on Spark, Flink, Google Dataflow or Databricks is billed by those platforms, so confirm their terms separately.
- Commercial support: Vendor or consultancy support is available from third parties, and coverage differs by provider.
- Migration effort: Converting Kettle jobs and transformations is a one-time cost that depends on how much of the estate you import and how much you rework.
- Hosting: Hop Server, Docker images and any scheduler you add need hosting and operations time.
Confirm with any support provider which Hop release and deployment pattern they cover, and ask what they will do if you run on a runtime they do not support.
NiFi's version history is the factor that most affects the cost of staying current. The NiFi downloads page states that NiFi 1.28 is the last minor release of the version 1 series and lists an end-of-support date of December 8, 2024 for 1.28.1. The project explains that 1.x cannot be maintained further because it depends on Jetty 9.4, Spring Framework 5.3 and AngularJS 1.8, which cannot be upgraded in that line. NiFi 2.12.0 was released on September 13, 2026.
- 1.x end of support: Estates still on 1.x no longer receive project updates, so the upgrade is not optional for long-term operation.
- Required path: The migration guidance requires moving to NiFi 1.27.0 before moving to 2.0.0.
- Configuration change: The flow configuration file changes from flow.xml.gz to flow.json.gz.
- Breaking changes: The variable registry expression language scope was removed and replaced with an ENVIRONMENT scope, and distributed map cache components were renamed.
- Runtime: NiFi's administration guide lists Java 21 as the minimum runtime.
If you run 1.x, budget for the upgrade as a planned project, and confirm with any support provider which 2.x release they cover.
What drives running cost for each platform
Both NiFi and Hop are Apache-2.0 projects, so license fees are not the cost driver. Running cost comes from the compute that executes flows, the people who build and operate them, and the upgrade work each platform demands. Use these components to build a like-for-like quote request.
Cluster and compute sizing
NiFi runs as an always-on Java service, so its cost is the cluster you keep running at all hours, including nodes, storage for the content and provenance repositories, and the coordination layer. Hop pipelines run on the native engine, a remote Hop Server, or Spark, Flink or Beam clusters, so cost depends on how many of those processes you keep warm. Ask the implementer to size NiFi nodes against peak ingest volume and Hop against your scheduled workloads, and to state which items are always-on and which are billed only while a job runs.
Scheduler and orchestration layer
Hop Server executes pipelines and workflows but does not schedule them, so an external scheduler or orchestrator must submit work. That component is an extra system to license, host, monitor and patch, and it is often the piece teams underestimate. NiFi does not need one for continuous flows, but teams that also run batch jobs may still add a scheduler. Ask whether the quote includes the scheduler, who operates it, and how job failures are alerted.
Build and migration skills
The largest recurring cost is usually people. NiFi flows are built on the canvas and tuned through processor, queue and back-pressure settings, while Hop pipelines are built on its GUI and often need Java or Spark knowledge for engine-specific tuning. Kettle migration adds one-time effort to review converted jobs, since the importer has documented limits such as no metastore import. Ask for the migration effort split between conversion review and rebuild, and for named skills required to run each platform after handover.
Upgrade and version path
Upgrade cost is where the two platforms differ most in one-time effort. NiFi 1.x cannot be maintained further, so any legacy 1.x estate needs a staged move through 1.27.0 to 2.x, with a change from flow.xml.gz to flow.json.gz and renamed scope and cache components. Hop releases move on their own cadence, and its images need Java 21. Ask for a costed upgrade plan, the test effort per release, and whether the quote covers flow regression testing.
Support and security hardening
Both projects are community-supported, so there is no vendor support fee to budget. You pay for the people who keep the platform patched, authenticated and reachable only where intended, such as NiFi's secure configuration, which otherwise binds to loopback only. Ongoing retainers cover upgrades, monitoring and testing, and they recur. Ask for the split between one-time hardening and recurring retainer hours, and confirm who holds the on-call duty.
Tooling and registry overhead
Version control, a flow registry, container registries and monitoring add recurring cost even with free software. NiFi can version flows through registry clients such as GitHub or GitLab, and Hop projects can be deployed from Docker images or Git checkouts. Each choice adds pipeline and storage work. Ask which registry, CI runner, log store and monitoring stack the quote assumes, and whether those are client-owned or included, so you can compare totals on equal terms.
Moving from Kettle or NiFi to Hop, or the reverse
This sequence takes a team from an inventory of existing Kettle jobs or NiFi flows to a working Hop estate, with clear checks on what transferred, what was rebuilt, and when the old system can be retired. It is written for teams that want to move carefully rather than in one switch.
Inventory flows, jobs and their dependencies
Before any conversion, the data or platform lead lists every Kettle transformation (.ktr) and job (.kjb) or every NiFi process group, and records what each one reads, writes, and triggers. For Kettle, note the kettle.properties files and shared database connections in use. For NiFi, note the flow configuration, the controller services, and any scheduling that sits outside NiFi. Mark which items are business-critical and which are dormant. The output is a signed-off inventory that names an owner for each item, so no flow is migrated by accident or left behind.
Run the Kettle importer on a copy
An engineer runs the Hop importer against a copy of the Kettle project, never against the live repository. This can be done from the Hop GUI through File, then Import from Kettle/PDI, or with the hop-import command-line tool for repeatable runs. The importer converts .kjb files to .hwf workflows, .ktr files to .hpl pipelines, and carries kettle.properties and shared database connections into Hop metadata. The output is a converted project tree in a separate branch that can be diffed against the source. For NiFi flows, there is no equivalent importer, so this step applies only to Kettle sources.
Review importer limits and fix gaps
The data engineer reviews the converted output against the documented importer limits. Same-named database connections keep only one copy, so any conflicting definitions must be split and renamed by hand. Pentaho metastore content is not imported, so any metastore-backed configuration needs to be recreated as Hop metadata. Each pipeline is then run in the Hop GUI against test data, and row counts and error handling are compared with the Kettle output. The check is complete when every flagged limit has a recorded fix and a test result. Confirm current import coverage with the Apache Hop project, since the importer may change between releases.
Rebuild NiFi flows or Kettle logic as needed
Where the source is NiFi, or where a Kettle transformation relies on behavior the importer cannot carry across, the team rebuilds the logic as Hop pipelines or as NiFi flows. NiFi processor-specific flows, provenance expectations, and back-pressure settings do not map one to one, so each rebuilt flow needs its own design review. Hop pipelines can run on the native engine, on Spark or Flink through Beam, or on other runtimes, and the runtime should be chosen here, not later. The output is a list of rebuilt flows with a reviewer for each and a documented runtime choice.
Reconnect scheduling and deployment
Scheduling does not transfer with the pipelines. Hop Server is not a scheduler, so an external scheduler or orchestrator must submit work to it, and NiFi's timing rules must be replaced by whatever triggers the new pipelines. The platform team sets up deployment, either as Docker images, as exported ZIP resources, or from a Git checkout, and records how each environment pulls its version. The check is that every converted job runs on its intended trigger in a non-production environment, and that failures alert the same people as before.
Run old and new in parallel
For a defined period agreed in advance by the project owners, both the legacy system and the Hop estate run the same inputs, and their outputs are compared. The data team checks row counts, totals, timestamps, and error queues daily. For NiFi sources, the team also compares the provenance records and lineage that downstream auditors rely on, and documents where Hop gives a different record. Discrepancies are logged with a root cause and an owner. The parallel run is complete when the discrepancy log has no open items that affect consumers.
Cut over and retire the old system
The team cuts over flow by flow, starting with low-risk items, and moves consumers to the Hop outputs once each one has passed parallel running. The legacy schedules are disabled only after the new triggers are confirmed. Backups and exported definitions of the old Kettle or NiFi estate are kept under the retention rules the organization sets. The output is a decommission record listing each retired job, its replacement, and the approver. Keep the legacy configuration available for the audit period your compliance team specifies.
Where NiFi and Hop implementation projects run long
The longest NiFi work is usually the move from 1.x to 2.x, because the two lines differ in ways that touch every flow. The migration guidance requires upgrading to NiFi 1.27.0 before moving to 2.0.0, and the flow configuration file changes from flow.xml.gz to flow.json.gz. The variable registry expression language scope was removed and replaced by an ENVIRONMENT scope, and the distributed map cache components were renamed, so any processor or parameter that relied on the old names needs review. Clustering adds its own work: confirm whether you will use embedded or external ZooKeeper, or Kubernetes ConfigMaps or Leases, as described in the NiFi administration guide. Identity provider wiring (LDAP, OpenID Connect, SAML 2.0 or X.509) is also a per-environment task, and Kerberos is deprecated, so confirm which provider your directory team will support. Python-based processors are marked as a beta feature, so decide early whether any flow depends on them.
Hop effort concentrates in three decisions. First, the Kettle import is a review exercise, not a conversion you can trust blindly. The importer maps .kjb jobs to .hwf workflows and .ktr transformations to .hpl pipelines, but its documented limitations include keeping only one copy of same-named database connections and no metastore import, so each migrated project needs a manual check of connections and metadata. Second, Hop Server is not a scheduler, so you must choose and operate an external scheduler or orchestrator that submits work to it, which is an extra component to own. Third, pick the engine (native Hop engine, Spark, Flink, Beam or Databricks) and the deployment path: Docker image, exported ZIP resources or a Git checkout. Illustratively, a team that deploys from Git must agree on branching and promotion rules before the first production run. Confirm with your own platform team which of these paths and schedulers your environment already supports.
Questions buyers ask before choosing NiFi or Hop
Can Apache Hop replace Apache NiFi for streaming workloads?
Not in the same way. NiFi is a continuous dataflow system: flows run as long-lived processors that move, route and transform records as they arrive, and its provenance records lineage for each piece of data. Hop is a design tool for pipelines and workflows, and its pipelines run on the native Hop engine, Apache Spark, Apache Flink or Apache Beam, selected as a runtime configuration. If your streaming need is continuous ingestion with per-record lineage and back pressure, NiFi is the closer fit. If you already run Flink or Spark and want the same visual pipeline design on top of them, Hop may suit you. Confirm with a proof of concept how each handles your message rates and failure paths.
Can NiFi run scheduled batch ETL jobs without another tool?
NiFi can run batch-style work, but it is built around continuous flows rather than calendar-driven jobs. Scheduling is configured on processors, and teams often pair NiFi with an external scheduler for complex dependencies across many jobs. Hop has the opposite profile: its Hop Server executes pipelines and workflows but is explicitly not a scheduler, so an external scheduler or orchestrator must submit work to it. In both cases, the scheduling layer is a design decision you own. Ask your team to list the job dependencies, retry rules and calendar windows you need, then test those against each tool before choosing.
How hard is it to migrate Pentaho Kettle jobs to Hop?
The effort depends on how much of your Kettle estate is standard transformations and jobs and how much relies on custom plugins, metastore content or environment-specific settings. Hop includes an importer that converts Kettle jobs (.kjb) to workflows (.hwf), transformations (.ktr) to pipelines (.hpl), and kettle.properties and shared database connections to Hop metadata. The Hop manual documents limitations: only one copy of same-named database connections is kept, and metastore content is not imported. Expect manual rework for those items and for any step that has no direct equivalent. Start with an inventory of your jobs, plugins and connections, then convert a representative sample and test output row counts before committing.
What skills does the internal team need to run NiFi or Hop?
Both require Java 21, so your platform team needs to manage a JVM runtime, upgrades and container images. For NiFi, the team also needs to understand flow design, processor configuration, clustering through ZooKeeper or on Kubernetes, and the security setup for its authentication providers. NiFi's optional Python processors need Python 3.10, 3.11 or 3.12 and are described as a beta feature. For Hop, the team needs pipeline and workflow design, metadata management in projects, and either a Spark, Flink or Beam runtime or the native engine. Apache Hop publishes Docker images, which simplifies packaging. Map your existing skills against these requirements and plan training where gaps appear.
Can NiFi or Hop be run without a paid vendor?
Yes, both are Apache Software Foundation projects under the Apache License 2.0, and neither project offers a paid edition of its own. Running them yourself means your organization carries the operating costs: infrastructure, staff time, upgrades, backups and security hardening. Any commercial support you buy comes from a third party, not the project. For NiFi, note the version path: NiFi 1.x can no longer be maintained because of its Jetty, Spring and AngularJS dependencies, so new deployments should start on the 2.x line, and existing 1.x estates need the migration path described in the NiFi migration guidance. Confirm the support model you need before you commit.
How is the cost of implementing NiFi or Hop quoted?
The cost has three parts: software, which is free for both under Apache-2.0; infrastructure, which depends on cluster size and the runtime you choose; and people, which covers design, migration, testing, security configuration and ongoing operation. DeployOpen does not publish a price list. Engagements are typically fixed-scope, with scope, deliverables and timelines agreed in writing before work starts, and the quote follows from that scope. Ask any provider to itemize the flows or pipelines in scope, the number of Kettle jobs to migrate, the cluster topology, the authentication provider, the testing and handover work, and whether ongoing support is priced separately as a retainer.
Which security settings matter first for a NiFi or Hop deployment?
Start with NiFi's defaults. When no security configuration is provided, NiFi binds to 127.0.0.1, so the UI is reachable only through the loopback interface. Before exposing it to users, configure one of its supported authentication providers: single-user, LDAP, OpenID Connect, SAML 2.0 or X.509 client certificates. The Kerberos provider is deprecated. For Hop, focus on how project resources are deployed (Docker images, exported ZIP resources or a Git checkout), how credentials for database connections are stored and injected, and who can reach the Hop Web GUI. Confirm each setting against the current administration guide and your own security baseline.
How DeployOpen helps choose, implement or migrate
DeployOpen is a Chainscore Labs company that plans, implements, customizes, integrates, hardens and documents self-hosted open-source platforms inside the client's own infrastructure. For NiFi against Hop, the work starts with a written scope that decides whether continuous flows, scheduled pipelines or both fit the workload, and that scope is agreed before any build begins.
How we scope
- Engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts.
- The written scope covers which sources, sinks and flows move data continuously and which run as scheduled Hop pipelines.
- We confirm Kettle (.kjb and .ktr) inventory, database connections and any Hop Server or external scheduler the team already runs.
How we build
- We stand up NiFi 2.x clusters or Hop environments inside your cloud accounts, data centers or air-gapped networks.
- Authentication is configured against your identity provider, choosing from the methods NiFi supports such as LDAP, OpenID Connect or SAML 2.0.
- Where Hop is chosen, we set up its runtime, whether local, Hop Server, Spark, Flink or Beam, and test migrated Kettle jobs against source data.
How we hand over
- Handover includes architecture documentation, configuration records, runbooks and evidence of tests run against the agreed scope.
- Operators are walked through provenance review in NiFi or run history and logging in Hop, matched to the team's own skills.
- Migration notes list which Kettle objects were converted, which were rebuilt by hand and which need your confirmation.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers after handover.
- Version upgrades are planned against the vendor's published release and migration guidance, not assumed to be routine.
- Ask any partner for a quote built from written scope, including what recurs after handover and what drives it up.
