Real-Time Processing
Apache Storm is designed for processing data in real-time, which makes it ideal for applications like fraud detection, recommendation systems, and monitoring tools.
Scalability
Storm is capable of scaling horizontally, allowing it to handle increasing amounts of data by adding more nodes, making it suitable for large-scale applications.
Fault Tolerance
Storm provides robust fault-tolerance mechanisms by rerouting tasks from failed nodes to operational ones, ensuring continuous processing.
Broad Language Support
Apache Storm supports multiple programming languages, including Java, Python, and Ruby, allowing developers to use the language they are most comfortable with.
Open Source Community
Being an Apache project, Storm benefits from a strong open-source community, which contributes to its development and offers abundant resources and support.
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Check the traffic stats of Apache Storm 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 Storm 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 Storm'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 Storm 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 Storm on Reddit. This can help you find out how popualr the product is and what people think about it.
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 3 years ago
Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 3 years ago
Storm, a system for real-time and stream processing. - Source: dev.to / over 3 years ago
Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 3 years ago
Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 4 years ago
ListItem(name='Apache Storm', website='https://storm.apache.org/', category='Stream Processing', short_description='Apache Storm is an open-source distributed stream processing computation framework written predominantly in the Clojure programming language.'),. Source: over 4 years ago
So Yahoo bought that. I think it was 2013 or 2014. Timelines are hard. But I wanted to go join the Games team and start things back up. But that was also my first kind of experience in actually building recommendation engines or working with lots of data. And I think for me, like that was, I guess...at the time, we were using something called Apache Storm. We had Hadoop, which had been around for a while. And it... - Source: dev.to / over 4 years ago
Apache Storm is a distributed tool for real-time processing of large data volumes. The project is based on Clojure and Java and was open-sourced under Apache License 2.0 after it was purchased by Twitter. Interoperability with Java is the main feature of Clojure that allows integrating the Clojure code into any project already written in Java. - Source: dev.to / almost 5 years ago
Finally, we are beginning to see some real back end applications of wasm apart from envoy proxy. This seems very similar to apache storm [1], where users can define UDFs (user defined functions) on their streams. Although, I dont understand whats the value add of wasm (apart from security) if the user still has to write code in Rust -> wasm. Why not just execute in rust alone? [1] https://storm.apache.org/. - Source: Hacker News / almost 5 years ago
Both Fortune 500 and small companies are looking for competent people who can derive useful insight from their huge pile of data and that's where Big Data Framework like Apache Hadoop, Apache Spark, Flink, Storm, and Hive can help. - Source: dev.to / over 5 years ago
Storm is a popular distributed real-time computation system that works for big data with a simple processing model to carry out powerful abstractions. This framework --- made an open source project by Twitter --- has been touted as the real-time Hadoop. - Source: dev.to / over 5 years ago
Apache Storm, once a pioneering real-time stream processing system, has received diverse commentary in the data engineering domain. Developed initially by Twitter and later adopted as an Apache project, Storm holds a distinguished position in the big data ecosystem. Its primary advantage lies in its ability to process unbounded streams of data efficiently, bringing analogies to Hadoop in the world of batch processing.
Apache Storm is recognized for its simplicity and flexibility, which has led to significant adoption among developers for real-time data processing tasks. The system is easily integrable with existing queuing technologies, providing a versatile platform for real-time analytics, online machine learning, ETL tasks, distributed RPC, and continuous computation. The framework's compatibility with any programming language further enhances its usability across various computing environments, making it a developer's choice for stream processing needs.
Within the domain of real-time data processing, Apache Storm competes with several other frameworks, notably Apache Spark, Apache Flink, and Hadoop. While Spark's structured streaming approach can introduce latency, Storm and Flink have been noted for their capabilities in low-latency applications, offering an edge in scenarios requiring immediate data handling. Storm's competitive positioning is strengthened by its robust and scalable architecture, yet it faces overshadowing by more modern solutions like Apache Flink, which are often considered more sophisticated and efficient for contemporary real-time stream processing needs.
Despite its solid technical foundation, industry sentiment as of recent years suggests a decline in Storm's prominence. Descriptors such as "outdated" and "less-used" in various articles reflect its waning use as organizations opt for newer alternatives that offer enhanced performance and more comprehensive feature sets. Storm's association with the Clojure programming languageโa deviation from the more ubiquitously adopted Javaโmight contribute to its reduced appeal, despite its interoperability with Java.
Apache Storm has been a cornerstone in advancing real-time data processing solutions. Its legacy as a "real-time Hadoop" has cemented its place in the history of big data innovations. However, the growing complexity of data infrastructure requirements and the evolution of technical capabilities in competing frameworks have challenged its standing in the contemporary tech landscape.
In summary, Apache Storm remains a respected player that has contributed significantly to real-time stream processing strategies. While its application and relevance might have witnessed a downward trend amid fierce competition and advancements in the ecosystem, its influence and the foundational concepts it popularized continue to impact the development of real-time data processing technologies.
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