Need advice about which tool to choose?Ask the StackShare community!
Add tool
Apache Storm vs Confluent: What are the differences?
Apache Storm vs. Confluent
1. **Real-time data processing**: Apache Storm is a real-time computation system that allows processing streaming data as it arrives. On the other hand, Confluent is a platform that provides tools and services for stream processing, built on top of Apache Kafka. While Storm is more focused on real-time data processing, Confluent offers a broader range of capabilities.
2. **Scalability**: Apache Storm is highly scalable and can handle large volumes of data across distributed systems, making it suitable for high-throughput applications. Confluent, with its integration with Apache Kafka, also offers scalability but focuses more on providing a unified platform for stream processing applications.
3. **Ease of Use**: Confluent provides a more user-friendly and streamlined experience for setting up and managing stream processing pipelines compared to Apache Storm. This makes it easier for developers and organizations to adopt and integrate streaming technologies into their workflows.
4. **Ecosystem Integration**: Apache Storm has a mature ecosystem with a wide range of integrations and support for various programming languages. In contrast, Confluent comes with a built-in ecosystem that integrates seamlessly with Apache Kafka, allowing users to leverage the existing Kafka ecosystem for stream processing.
5. **Monitoring and Management**: Confluent provides enhanced monitoring and management features compared to Apache Storm. With Confluent Control Center, users have better visibility into their stream processing applications, enabling them to monitor performance, troubleshoot issues, and optimize their workflows more effectively.
6. **Cost and Licensing**: Apache Storm is open-source software and is free to use, while Confluent offers both open-source components and commercial services. Depending on the requirements and resources available, organizations can choose between the cost-effective option of Apache Storm or the added features and support provided by Confluent.
In Summary, the key differences between Apache Storm and Confluent lie in real-time data processing focus, scalability, ease of use, ecosystem integration, monitoring and management capabilities, and cost and licensing models.
Manage your open source components, licenses, and vulnerabilities
Learn MorePros of Apache Storm
Pros of Confluent
Pros of Apache Storm
- Flexible10
- Easy setup6
- Event Processing4
- Clojure3
- Real Time2
Pros of Confluent
- Free for casual use4
- No hypercloud lock-in3
- Dashboard for kafka insight3
- Easily scalable2
- Zero devops2
Sign up to add or upvote prosMake informed product decisions
Cons of Apache Storm
Cons of Confluent
Cons of Apache Storm
Be the first to leave a con
Cons of Confluent
- Proprietary1
Sign up to add or upvote consMake informed product decisions
- No public GitHub repository available -
What is Apache Storm?
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
What is Confluent?
It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
Need advice about which tool to choose?Ask the StackShare community!
What companies use Apache Storm?
What companies use Confluent?
What companies use Confluent?
Manage your open source components, licenses, and vulnerabilities
Learn MoreSign up to get full access to all the companiesMake informed product decisions
What tools integrate with Apache Storm?
What tools integrate with Confluent?
What tools integrate with Apache Storm?
What tools integrate with Confluent?
Sign up to get full access to all the tool integrationsMake informed product decisions
Blog Posts
+35 41
19431
What are some alternatives to Apache Storm and Confluent?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Amazon Kinesis
Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.
Apache Flume
It is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.