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Materialize VS Apache Storm

Compare Materialize VS Apache Storm and see what are their differences

Materialize logo Materialize

A Streaming Database for Real-Time Applications

Apache Storm logo Apache Storm

Apache Storm is a free and open source distributed realtime computation system.
  • Materialize Landing page
    Landing page //
    2023-08-27
  • Apache Storm Landing page
    Landing page //
    2019-03-11

Materialize features and specs

  • Real-time Analytics
    Materialize offers real-time stream processing and materialized views, which allow users to get instant results from their data without the need for batch processing. This is particularly useful for applications that require immediate insights.
  • SQL Support
    Materialize supports SQL, making it easy for users familiar with SQL databases to adopt the platform without needing to learn a new language or framework.
  • Consistency
    Materialize maintains strict consistency for its materialized views, ensuring that users always get accurate and up-to-date information from their streams.
  • Integration with Kafka
    It integrates smoothly with Kafka, allowing for easy handling of streaming data and simplifying the process of working with real-time data feeds.

Possible disadvantages of Materialize

  • Scaling Limitations
    Materialize may face challenges when scaling to handle very large data sets compared to some distributed systems designed for big data processing.
  • Limited Language Support
    While SQL is supported, some users may find the lack of alternative query language support limiting, especially if they're accustomed to more expressive query options available in other systems.
  • Complexity in Use Cases
    For more complex use cases involving intricate data transformations or processing, Materialize might require additional configuration and optimization, posing a challenge for less experienced users.
  • Resource Intensive
    The real-time nature of Materialize, especially with maintaining materialized views, can be resource-intensive, potentially leading to higher operational costs.

Apache Storm features and specs

  • 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.

Possible disadvantages of Apache Storm

  • Complex Setup
    Setting up and configuring Apache Storm can be complex and time-consuming, requiring detailed knowledge of its architecture and the underlying infrastructure.
  • High Learning Curve
    The architecture and components of Storm can be difficult for new users to grasp, leading to a steeper learning curve compared to some other streaming platforms.
  • Maintenance Overhead
    Managing and maintaining a Storm cluster can require significant effort, including monitoring, troubleshooting, and scaling the infrastructure.
  • Error Handling
    While Storm is fault-tolerant, its error handling at the application level can sometimes be challenging, requiring careful design to manage failures effectively.
  • Resource Intensive
    Storm can be resource-intensive, particularly in terms of memory and CPU usage, which can lead to increased costs and necessitate powerful hardware.

Materialize videos

Bootstrap Vs. Materialize - Which One Should You Choose?

More videos:

  • Review - Materialize Review | Does it compete with Substance Painter?
  • Review - Why We Don't Need Bootstrap, Tailwind or Materialize

Apache Storm videos

Apache Storm Tutorial For Beginners | Apache Storm Training | Apache Storm Example | Edureka

More videos:

  • Review - Developing Java Streaming Applications with Apache Storm
  • Review - Atom Text Editor Option - Real-Time Analytics with Apache Storm

Category Popularity

0-100% (relative to Materialize and Apache Storm)
Databases
80 80%
20% 20
Big Data
41 41%
59% 59
Database Tools
100 100%
0% 0
Stream Processing
33 33%
67% 67

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Materialize and Apache Storm

Materialize Reviews

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Apache Storm Reviews

Top 15 Kafka Alternatives Popular In 2021
Apache Storm is a recognized, distributed, open-source real-time computational system. It is free, simple to use, and helps in easily and accurately processing multiple data streams in real-time. Because of its simplicity, it can be utilized with any programming language and that is one reason it is a developerโ€™s preferred choice. It is fast, scalable, and integrates well...
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Storm is an open-source distributed real-time computational system for processing data streams. Similar to what Hadoop does for batch processing, Apache Storm does for unbounded streams of data in a reliable manner. Built by Twitter, Apache Storm specifically aims at the transformation of data streams. Storm has many use cases like real-time analytics, online machine...

Social recommendations and mentions

Based on our record, Materialize should be more popular than Apache Storm. It has been mentiond 74 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Materialize mentions (74)

  • Materialized views are obviously useful
    Did I miss in the article where OP reveals the magic database that actually does this? 3rd party solutions like https://readyset.io/ and https://materialize.com/ exist specifically because databases donโ€™t actually have what we all want materialized views to be. - Source: Hacker News / about 1 year ago
  • The Missing Manual for Signals: State Management for Python Developers
    This triggered some associations for me. Strongest was Cells[0], a library for Common Lisp CLOS. The earliest reference I can find is 2002[1], making it over 20 years old. Second is incremental view maintenance systems like Feldera[2] or Materialize[3]. These use sophisticated theories (z-sets and differential dataflow) to apply efficient updates over sets of data, which generalizes the case of single variables.... - Source: Hacker News / about 1 year ago
  • Category Theory in Programming
    It's hard to write something that is both accessible and well-motivated. The best uses of category theory is when the morphisms are far more exotic than "regular functions". E.g. It would be nice to describe a circuit of live queries (like https://materialize.com/ stuff) with proper caching, joins, etc. Figuring this out is a bit of an open problem. Haskell's standard library's Monad and stuff are watered down to... - Source: Hacker News / over 1 year ago
  • Building Databases over a Weekend
    > [...] `https://materialize.com/` to solve their memory issues [...] Disclaimer: I work at Materialize Recently there have been major improvements in Materialize's memory usage as well as using disk to swap out some data. I find it pretty easy to hook up to Postgres/MySQL/Kafka instances: https://materialize.com/blog/materialize-emulator/. - Source: Hacker News / almost 2 years ago
  • Building Databases over a Weekend
    I agree. So many disparate solutions. The streaming sql primitives are by themselves good enough (e.g. `tumble`, `hop` or `session` windows), but the infrastructural components are always rough in real life use cases. Crossing fingers for solutions like `https://github.com/feldera/feldera` to solve their memory issues, or `https://clickhouse.com/docs/en/materialized-view` to solve reliable streaming consumption.... - Source: Hacker News / almost 2 years ago
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Apache Storm mentions (11)

  • Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
    There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 3 years ago
  • Real Time Data Infra Stack
    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
  • In One Minute : Hadoop
    Storm, a system for real-time and stream processing. - Source: dev.to / almost 4 years ago
  • Elon Musk reportedly wants to fire 75% of Twitterโ€™s employees
    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: almost 4 years ago
  • Spark for beginners - and you
    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
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What are some alternatives?

When comparing Materialize and Apache Storm, you can also consider the following products

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

RisingWave - RisingWave is a stream processing platform that utilizes SQL to enhance data analysis, offering improved insights on real-time data.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.

ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.