NiFi

Author: r | 2025-04-24

demo quick-import nifi current-user nifi cluster-summary nifi connect-node nifi delete-node nifi disconnect-node nifi get-root-id nifi get-node nifi get-nodes nifi offload-node nifi list-reg-clients nifi create-reg-client nifi update-reg-client nifi get-reg-client-id nifi pg-import nifi pg-connect nifi pg-start nifi pg-stop nifi pg-create nifi Nifi Utils, free and safe download. Nifi Utils latest version: Nifi Utils: Manage Your Production Nifi Instances with Ease. Nifi Utils is a free Chrom

nifi/nifi-docker/dockerhub/README.md at main apache/nifi

Empowering Data-Driven Organizations with Cloudera Flow Management 4 (powered by Apache NiFi 2.0) Apache NiFi has long been a cornerstone for data engineering, providing a powerful and flexible framework for data ingestion, transformation, and distribution. As a leading contributor to NiFi, Cloudera has been instrumental in driving its evolution and adoption. With the recent release of Cloudera Flow Management 4.0 in Technical Preview as the first NiFi 2.0-based Cloudera Flow Management release, we are excited to showcase the enhanced capabilities and how Cloudera continues to lead the way in data flow management.The Value of NiFi 2.0 and Cloudera Flow Management 4.0Cloudera Flow Management 4.0 (powered by Apache NiFi 2.0) introduces significant improvements, including:Enhanced Performance: NiFi 2.0 boasts significant performance enhancements, handling data flows more efficiently and scaling to larger workloads. These enhancements give users more power and reliability to ingest, process, and distribute larger and more complex data sets.Streamlined Development: The new flow canvas interface and improved drag-and-drop functionality make flow development faster and more intuitive. This significantly decreases flow development time, leading to cost savings.Advanced Security: NiFi 2.0 introduces enhanced security features, including improved encryption and authentication mechanisms. This provides more confidence in a secure and reliable system for processing sensitive data.Expanded Integrations: NiFi 2.0 seamlessly integrates with a wider range of data sources and systems, expanding its applicability across various use cases. Cloudera Flow Management 4.0 specifically retains components to support integrations to applications in Cloudera where many components such as Hive and Accumulo were removed in Apache NiFi 2.0. In addition, Cloudera Flow Management 4.0 includes new integrations such as Change Data Capture (CDC) capabilities for relational database systems as well as Iceberg. This allows users to design their own end-to-end systems using Cloudera applications as well as external systems .Native Python Processor Development: NiFi 2.0 provides a Python SDK for which processors can be rapidly developed in Python and deployed in flows. Some common document parsing processors written in Python are included in the release. Cloudera Flow Management 4.0 specifically adds components for embedding data, ingesting into vector databases, prompting several GenAI systems and working with Large Language Models (LLMs) via Amazon Bedrock. This provides users with an impressive set of GenAI capabilities to empower their business cases.Best Practices in Flow Design: NiFi 2.0 provides a rules engine for developing flow analysis rules that recommend and enforce best practices for flow design. Cloudera Flow Management 4.0

apache/nifi: Apache NiFi - GitHub

Provides several Flow Analysis Rules for such aspects as thread management and recommended components. Cloudera Flow Management administrators can leverage these to ensure well-designed and robust flows for their use cases.Cloudera and NiFi - Continued Support, Innovation, and Simplified Migration Cloudera has been a driving force behind NiFi's development, actively contributing to its open-source community and providing expert guidance to users. Cloudera has invested heavily in NiFi, ensuring its continued evolution and relevance in the ever-changing data landscape.Our commitment to NiFi is evident in our initiatives. We actively participate in the Apache NiFi community, sharing knowledge, best practices and supporting users through mailing lists, forums, and events. In addition to community contributions, Cloudera Flow Management Operator enables customers to deploy and manage NiFi clusters and NiFi Registry instances on Kubernetes application platforms. Cloudera Flow Management Operator simplifies data collection, transformation, and delivery across enterprises. Leveraging containerized infrastructure, the operator streamlines the orchestration of complex data flows. Cloudera is the only provider with a Migration Tool that simplifies the complex and repetitive process of migrating Cloudera Flow Management flows from the NiFi 1 set of components to use the NiFi 2 set. To these ends, Cloudera provides comprehensive training and consulting services to help organizations leverage the full potential of NiFi.Driving the Future of Data Flow ManagementWith Cloudera Flow Management 4.0.0 (powered by Apache NiFi 2.0), Cloudera fortifies its leadership in data flow management. We will continue to invest in NiFi's development, ensuring it remains a powerful and reliable tool for data engineers and data scientists. In addition, Cloudera provides cloud-based deployments of Cloudera Flow Management, optimizing your operational efficiency and allowing you to scale to the enterprise with confidence. Features enabling, integrating with, and enhancing your AI-based solutions are a central focus of Cloudera Flow Management. We also continue to provide support and guidance to our customers, helping them harness the full power of NiFi to drive business-critical data initiatives.Learn More:To explore the new capabilities of Cloudera Flow Management and discover how it can transform your data pipelines, learn more here:Data Distribution Architecture to Drive InnovationScaling NiFi for the Enterprise with Cloudera

Apache NiFi Tutorial: What is NiFi?

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Download NiFi and NiFi Registry - docs.cloudera.com

NiFi and take note of the absolute path4. Add DBCPConnectionPool Controller Service and configure its properties​To configure a Controller Service in Apache NiFi, visit the NiFi Flow Configuration page by clicking on the "gear" buttonSelect the Controller Services tab and add a new Controller Service by clicking on the + button at the top rightSearch for DBCPConnectionPool and click on the "Add" buttonThe newly added DBCPConnectionPool will be in an Invalid state by default. Click on the "gear" button to start configuringUnder the "Properties" section, input the following valuesPropertyValueRemarkDatabase Connection URLjdbc:ch: HOSTNAME in the connection URL accordinglyDatabase Driver Class Namecom.clickhouse.jdbc.ClickHouseDriverDatabase Driver Location(s)/etc/nifi/nifi-X.XX.X/lib/clickhouse-jdbc-0.X.X-patchXX-shaded.jarAbsolute path to the ClickHouse JDBC driver JAR fileDatabase UserdefaultClickHouse usernamePasswordpasswordClickHouse passwordIn the Settings section, change the name of the Controller Service to "ClickHouse JDBC" for easy referenceActivate the DBCPConnectionPool Controller Service by clicking on the "lightning" button and then the "Enable" buttonCheck the Controller Services tab and ensure that the Controller Service is enabled5. Read from a table using the ExecuteSQL processor​Add an ​​ExecuteSQL processor, along with the appropriate upstream and downstream processorsUnder the "Properties" section of the ​​ExecuteSQL processor, input the following valuesPropertyValueRemarkDatabase Connection Pooling ServiceClickHouse JDBCSelect the Controller Service configured for ClickHouseSQL select querySELECT * FROM system.metricsInput your query hereStart the ​​ExecuteSQL processorTo confirm that the query has been processed successfully, inspect one of the FlowFile in the output queueSwitch view to "formatted" to view the result of the output FlowFile6. Write to a table using MergeRecord and PutDatabaseRecord processor​To write multiple rows in a single

NiFi Registry Documentation - Apache NiFi

Apache NiFi is an open-source workflow management software designed to automate data flow between software systems. It allows the creation of ETL data pipelines and is shipped with more than 300 data processors. This step-by-step tutorial shows how to connect Apache NiFi to ClickHouse as both a source and destination, and to load a sample dataset.1. Gather your connection details​To connect to ClickHouse with HTTP(S) you need this information:The HOST and PORT: typically, the port is 8443 when using TLS or 8123 when not using TLS.The DATABASE NAME: out of the box, there is a database named default, use the name of the database that you want to connect to.The USERNAME and PASSWORD: out of the box, the username is default. Use the username appropriate for your use case.The details for your ClickHouse Cloud service are available in the ClickHouse Cloud console. Select the service that you will connect to and click Connect:Choose HTTPS, and the details are available in an example curl command.If you are using self-managed ClickHouse, the connection details are set by your ClickHouse administrator.2. Download and run Apache NiFi​For a new setup, download the binary from and start by running ./bin/nifi.sh start3. Download the ClickHouse JDBC driver​Visit the ClickHouse JDBC driver release page on GitHub and look for the latest JDBC release versionIn the release version, click on "Show all xx assets" and look for the JAR file containing the keyword "shaded" or "all", for example, clickhouse-jdbc-0.5.0-all.jarPlace the JAR file in a folder accessible by Apache

NiFi Developer’s Guide - Apache NiFi

To the cloud.● Key features: Application anddata dependency mapping, automated migration workflows, testing and validationcapabilities.● Examples: Azure Migrate, GoogleCloud Migrate for Compute Engine. 4. Cloud migration tools● Purpose: Facilitate the transferof data and applications from on-premises environments to cloud platforms orbetween cloud environments.● Key features: Secure datatransfer, support for various cloud platforms, scalability and minimaldowntime.● Examples: AWS Migration Hub,Azure Site Recovery, Google Cloud Transfer Service, Acronis Cyber Protect. 5. Data integration tools● Purpose: Combine data fromdifferent sources into a single, unified view, often used in data warehousingand ETL (extract, transform, load) processes.● Key features: Data extraction,transformation and loading capabilities; support for various data sources andformats; data quality and cleansing functions.● Examples: Talend, InformaticaPowerCenter, Apache NiFi. 6. Big data migration tools● Purpose: Handle the migration oflarge volumes of data, often involving complex data structures and high-speedtransfer requirements.● Key features: Scalability,parallel processing, support for big data platforms like Hadoop and Spark,robust error handling.● Examples: Apache Sqoop, IBM BigReplicate. 7. Content management system(CMS) migration tools● Purpose: Migrate content fromone CMS to another, commonly used in website redesigns or platform upgrades.● Key features: Content mapping,media transfer, link redirection, metadata preservation.● Examples: WordPress WP AllImport, CMS2CMS. 8. Email migration tools● Purpose: Transfer email datafrom one email system to another, such as migrating from on-premises Exchangeto Office 365.● Key features: Mailbox transfer,calendar and contact migration, email formatting preservation, secure datahandling.● Examples: Microsoft ExchangeMigration, Google Workspace Migrate, AcronisCyber Protect. 9. Data replication tools● Purpose: Continuously replicatedata from one system to another, often used for real-time data synchronizationand disaster recovery.● Key features: Real-time datareplication, conflict resolution, minimal latency, support for various datasources.● Examples: HVR, Qlik Replicate,IBM InfoSphere Data Replication, AcronisCyber Protect. 10. Hybrid migration tools● Purpose: Handle multiple typesof migration scenarios within a single platform, offering versatility andcomprehensive capabilities.● Key features: Multi-sourcesupport, integrated data transformation, user-friendly interfaces, robust

Using the NiFi API to Start and Stop NiFi Processors from a NiFi

Put simply, NiFi was built to automate the flow of data between systems. While the term 'dataflow' is used in a variety of contexts, we use it here to mean the automated and managed flow of information between systems. This problem space has been around ever since enterprises had more than one system, where some of the systems created data and some of the systems consumed data. The problems and solution patterns that emerged have been discussed and articulated extensively. A comprehensive and readily consumed form is found in the Enterprise Integration Patterns . Some of the high-level challenges of dataflow include: Systems fail Networks fail, disks fail, software crashes, people make mistakes. Data access exceeds capacity to consume Sometimes a given data source can outpace some part of the processing or delivery chain - it only takes one weak-link to have an issue. Boundary conditions are mere suggestions You will invariably get data that is too big, too small, too fast, too slow, corrupt, wrong, or in the wrong format. What is noise one day becomes signal the next Priorities of an organization change - rapidly. Enabling new flows and changing existing ones must be fast. Systems evolve at different rates The protocols and formats used by a given system can change anytime and often irrespective of the systems around them. Dataflow exists to connect what is essentially a massively distributed system of components that are loosely or not-at-all designed to work together. Compliance and security Laws, regulations, and policies change. Business to business agreements change. System to system and system to user interactions must be secure, trusted, accountable. Continuous improvement occurs in production It is often not possible to come even close to replicating production environments in the lab. Over the years dataflow has been one of those necessary evils in an architecture. Now though there are a number of active and rapidly evolving movements making dataflow a lot more interesting and a lot more vital to the success of a given enterprise. These include things like; Service Oriented Architecture [soa], the rise of the API [api][api2], Internet of Things [iot], and Big Data [bigdata]. In addition, the level of rigor necessary for compliance, privacy, and security is constantly on the rise. Even still with all of these new concepts coming about, the patterns and needs of dataflow are still largely the same. The primary differences then are the scope of complexity, the rate of change necessary to adapt, and that at scale the edge case becomes common occurrence. NiFi is built to help tackle these modern dataflow challenges.. demo quick-import nifi current-user nifi cluster-summary nifi connect-node nifi delete-node nifi disconnect-node nifi get-root-id nifi get-node nifi get-nodes nifi offload-node nifi list-reg-clients nifi create-reg-client nifi update-reg-client nifi get-reg-client-id nifi pg-import nifi pg-connect nifi pg-start nifi pg-stop nifi pg-create nifi

NiFi Rest API-1.28.0 - Apache NiFi

Insert, we first need to merge multiple records into a single record. This can be done using the MergeRecord processorUnder the "Properties" section of the MergeRecord processor, input the following valuesPropertyValueRemarkRecord ReaderJSONTreeReaderSelect the appropriate record readerRecord WriterJSONReadSetWriterSelect the appropriate record writerMinimum Number of Records1000Change this to a higher number so that the minimum number of rows are merged to form a single record. Default to 1 rowMaximum Number of Records10000Change this to a higher number than "Minimum Number of Records". Default to 1,000 rowsTo confirm that multiple records are merged into one, examine the input and output of the MergeRecord processor. Note that the output is an array of multiple input recordsInputOutputUnder the "Properties" section of the PutDatabaseRecord processor, input the following valuesPropertyValueRemarkRecord ReaderJSONTreeReaderSelect the appropriate record readerDatabase TypeGenericLeave as defaultStatement TypeINSERTDatabase Connection Pooling ServiceClickHouse JDBCSelect the ClickHouse controller serviceTable NametblInput your table name hereTranslate Field NamesfalseSet to "false" so that field names inserted must match the column nameMaximum Batch Size1000Maximum number of rows per insert. This value should not be lower than the value of "Minimum Number of Records" in MergeRecord processorTo confirm that each insert contains multiple rows, check that the row count in the table is incrementing by at least the value of "Minimum Number of Records" defined in MergeRecord.Congratulations - you have successfully loaded your data into ClickHouse using Apache NiFi !

NiFi System Administrator’s Guide - Apache NiFi

Table of ContentsWhat is Unit TestingSalient Features of JUnitHow to Install JUnit on Ubuntu 20.04 Step 1: PrerequisitesStep 2: Update Your ServerStep 3: Install JUnitStep 4: Uninstall JUnitIn this article, we will see how to install Junit on Ubuntu 20.04 in 4 Simple Steps. JUnit is an open source unit testing framework for Java Based Projects. It was originally created by Erich Gamma and Kent Beck. JUnit provides capability to write and run your own test codes. JUnit is compatible on multiple platforms which makes it the most widely used Test framework. More on JUnit.What is Unit TestingUnit testing is basically a testing of small logic or code to verify that the output of the code is as expected based on the condition and data given on the Input.Salient Features of JUnitJUnit has very rich annotations set to identify the test methods.It provides assertions for testing expected results.JUnit has test runners capability for running tests.It allow us to write quality codes which can make the test results faster.JUnit tests cases are relatively simple to write as compared to other Unit Testing frameworks.JUnit tests can be run automatically and provides its own feedback.JUnit tests can be organized into test suites containing test cases.JUnit can show the Test Progress Bar which changes its color based on results. If it runs successful then it turns green and if test fails then it turns to red.Also Read: Easy Steps to Install Apache Nifi on Ubuntu 20.04Step 1: Prerequisitesa) You should have a running Ubuntu 20.04 Server.b) You should have apt or apt-get utility installed in Your Server.c) You should have sudo or root access to run privileged commands.Step 2: Update Your ServerIf you want to update all your installed packages, then you can simply do that by running apt-get update command as shown below.root@localhost:~# apt-get updateHit:1 focal InReleaseGet:2 focal-updates InRelease [114 kB]Get:3 focal-backports InRelease [101 kB]Get:4 focal-security InRelease [109 kB]Get:5 focal-updates/main amd64 Packages [983 kB]Get:6 focal-updates/main i386 Packages [474 kB]Get:7 focal-updates/main amd64 DEP-11 Metadata [263 kB]Get:8 focal-updates/universe i386 Packages [572 kB]Get:9 focal-updates/universe amd64 Packages [774 kB]Get:10 focal-updates/universe amd64 DEP-11 Metadata [323 kB]Get:11 focal-updates/universe DEP-11 64x64 Icons [358 kB]Get:12 focal-updates/multiverse amd64 DEP-11 Metadata [2,468 B]Get:13 focal-backports/universe amd64 DEP-11 Metadata [1,768 B]Get:14 focal-security/main amd64 DEP-11 Metadata [24.4 kB]Get:15 focal-security/universe amd64 DEP-11 Metadata [58.3 kB]Fetched 4,158 kB in 3s (1,317 kB/s)Reading package lists... DoneStep 3: Install JUnitYou can either use apt or apt-get to install JUnit package. demo quick-import nifi current-user nifi cluster-summary nifi connect-node nifi delete-node nifi disconnect-node nifi get-root-id nifi get-node nifi get-nodes nifi offload-node nifi list-reg-clients nifi create-reg-client nifi update-reg-client nifi get-reg-client-id nifi pg-import nifi pg-connect nifi pg-start nifi pg-stop nifi pg-create nifi

Apache NiFi Tutorial: What is NiFi? Architecture