User-Friendly Interface
Apache NiFi offers a drag-and-drop interface for designing data flows, making it easy to use even for those without extensive coding experience.
Extensive Connector Support
NiFi comes with a wide range of pre-built connectors for various data sources and destinations, simplifying integration tasks.
Real-time Data Processing
NiFi supports real-time data ingestion and processing, enabling timely data flow management.
Scalability
Designed to be highly scalable, NiFi can handle both small and large data volumes, adjusting to organizational needs as they grow.
Flexible Data Routing
NiFi allows dynamic routing of data based on content, making it versatile for various data transformation and routing needs.
Visual Data Monitoring
It offers real-time monitoring of data flows with visual representations, aiding in quick issue identification and resolution.
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Overall, Apache NiFi is considered a robust and flexible tool for managing data flows efficiently. It offers a comprehensive set of features for developers and data engineers looking to simplify and automate their data processing tasks. However, it may not be the best fit for every use case, particularly for those with simpler requirements or who prefer a lightweight tool.
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Check the traffic stats of Apache NiFi on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Apache NiFi on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Apache NiFi's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Apache NiFi on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Apache NiFi on Reddit. This can help you find out how popualr the product is and what people think about it.
They also contributed Apache NiFi but that was much earlier: https://nifi.apache.org/. - Source: Hacker News / about 2 years ago
This article presents the concept and implementation of a universal workbench for Apache NiFi data flows. - Source: dev.to / about 2 years ago
Apache NIFI (https://nifi.apache.org/). It uses the concept of Flow-based programming. Also its so underacknolged but this tool is very flexible. I have used as an Event Bus all the 3rd-Party Integrations. - Source: Hacker News / almost 3 years ago
Presently setting up Apache Nifi + Apache MiNiFi for the ETL portion of my work. NiFi was easy enough to figure out; but the docs for MiNiFi have been a pain due to differences between the Java and C++ versions. I then entirely configured it with the Java version so that it was easier to search for answers for the MiNiFi yaml syntax. Source: about 3 years ago
NIFI, like most Apache projects does most of its discussion on its mailing lists, but also has a slack. Source: about 3 years ago
You might want to give a tool like nifi a try: Https://nifi.apache.org/. Source: about 3 years ago
Recently I got a job at a new company and now I need to learn about Clickhouse, Docker, Apache Nifi and Kafka. I've found the decks about Docker and Kafka, but I can't find ones about Clickhouse and Nifi. Https://clickhouse.com/docs/en/intro Https://nifi.apache.org/. Source: over 3 years ago
This suggestion isn't a standard one, but when a coworker and I were looking for API gateways with a very specific feature set, we couldn't find a single one to do what we needed. We did, however, come across Apache NiFi. It's a flow-based programming tool that allowed us to translate an http-based request to streaming text sent via netcat. Source: over 3 years ago
For a simple sequential Pipeline, my goto would be Apache Camel. As soon as you want complexity its either Apache Nifi or a micro service architecture. Source: over 3 years ago
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Hereโs... - Source: dev.to / over 3 years ago
Pretty sure you can do this with a NiFi connector. Https://nifi.apache.org. Source: over 3 years ago
It's kind of a big tool for the job, but you could do this with Nifi. Source: about 4 years ago
Apache NiFi is a tool for moving and processing data between systems. It started as an internal project from the National Security Agency (NSA), where it was known by the codename NiagaraFiles. Through a technology transfer program, the NSA declassified it as an open source project under the Apache Software Foundation (ASF). NiFi models streaming data as a series of flows through a directed acyclic graph (DAG).... - Source: dev.to / over 4 years ago
Tangentially, if you like Dataflow you might also want to check out Apache NiFi. Same basic idea, but with a user interface that's pretty easy to use and fairly sophisticated. Source: almost 5 years ago
Presuming you mean this: https://nifi.apache.org/ ? Why not go with AWS Glue or similar AWS service directly? Source: almost 5 years ago
As part of your analysis take a look at Apache NiFi. It may not be right for you but focus on the features. It is more mature, spun out of the NSA, and has a pretty decent ecosystem. It is a Java focused stack with some Python friendly capabilities, eg can execute Jython if you want it too. Airflow is a really neat tool and I am not trying to persuade you away from exploring its use just offering a different... Source: almost 5 years ago
Fortunately, we have tools such as Apache NiFi, which allow us to design and manage our data pipelines, reducing the amount of programming and increasing overall efficiency. Yet, when it comes to creating them, a key and often neglected aspect is minimizing potential errors. - Source: dev.to / about 5 years ago
How does it differ from Apache NiFi? https://nifi.apache.org/. - Source: Hacker News / over 5 years ago
Apache NiFi, a prominent player in the realm of data workflow automation and open-source ETL (Extract, Transform, Load) tools, has garnered a diverse perception within the industry. NiFi's primary advantage lies in its versatile, web-based, drag-and-drop user interface, which eliminates the necessity for coding expertise, making it accessible for both technical and non-technical users alike.
A substantial portion of the public discourse highlights NiFiโs focus on flow-based programming and its ability to automate complex data flows across disparate systemsโa feature highly valued by organizations seeking efficiency in data processing and distribution. This flow-based design pattern allows users to create sophisticated data pipelines with minimal manual intervention, underscoring its utility in managing large-scale data transfers.
In comparison to its notable competitor, Apache Airflow, NiFi is frequently praised for its intuitive user interface, which is especially beneficial for teams lacking deep programming skills. Airflow, though popular and robust in scheduling and orchestrating complex workflows, necessitates a greater emphasis on coding, which can pose barriers for some users. Therefore, for enterprises prioritizing ease of use and rapid deployment of data flows, NiFi presents a compelling alternative.
NiFi's extensibility and configurability are other areas of acclaim. Users can create custom processors that integrate seamlessly within broader workflows, adding significant flexibility and customization capacity to meet specific organizational needs. Moreover, its capability to handle both batch and streaming data enhances its appeal to diverse business use cases.
Security and data provenance are frequently highlighted as critical features of Apache NiFi, offering organizations comprehensive tracking and auditing capabilities. These features enable enterprises to maintain data integrity and transparency, which is especially vital in regulated industries like healthcare and financial services.
However, while NiFi is lauded for its flexibility and comprehensive feature set, there are noted challenges, particularly in community support and documentation. As mentioned in the context data, some users have reported difficulties in navigating the documentation for NiFi's extensions, such as MiNiFi, which can impede seamless integration and deployment. This points towards potential areas for improvement in user support and resource accessibility.
In summary, Apache NiFi stands out as an adaptable, user-friendly solution for automating and managing data flows, with robust features that cater to a wide spectrum of data integration needs. While it is not without its challenges, particularly concerning documentation and support, its strengths in flexibility, ease of use, and security make it a strong contender in the landscape of open-source ETL tools. Users considering NiFi should weigh these factors against their specific requirements and infrastructure capabilities to make an informed choice.
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Is Apache NiFi good? This is an informative page that will help you find out. Moreover, you can review and discuss Apache NiFi here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
