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Streamdata.io VS Apache Beam

Compare Streamdata.io VS Apache Beam and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

Streamdata.io logo Streamdata.io

Streamdata.io provides a proxy-as-a-service that turns any request-response REST APIs into an event-driven streaming API.

Apache Beam logo Apache Beam

Apache Beam provides an advanced unified programming model to implement batch and streaming data processing jobs.
  • Streamdata.io Landing page
    Landing page //
    2023-04-02
  • Apache Beam Landing page
    Landing page //
    2022-03-31

Streamdata.io features and specs

No features have been listed yet.

Apache Beam features and specs

  • Unified Model
    Apache Beam provides a unified programming model that simplifies the development of both batch and stream processing applications. This reduces the complexity in maintaining separate codebases for different types of data processing needs.
  • Portability
    The portability of Apache Beam allows developers to write their code once and run it on different execution engines like Apache Flink, Apache Spark, and Google Cloud Dataflow, offering flexibility in choosing the right runtime environment.
  • Rich SDKs
    Apache Beam offers rich SDKs for multiple languages including Java, Python, and Go, allowing a broader range of developers to leverage its capabilities without being restricted to a single programming language.
  • Windowing and Triggering
    It provides powerful abstractions for windowing and triggering, enabling developers to handle out-of-order data and late data arrivals efficiently, which is crucial for accurate stream processing.

Possible disadvantages of Apache Beam

  • Complexity
    Although Apache Beam simplifies certain aspects of data processing, its unified model and advanced features can introduce complexity, making it potentially challenging for developers unfamiliar with distributed data processing concepts.
  • Limited Language Support
    While Apache Beam supports Java, Python, and Go, the level of feature support and maturity can vary between these SDKs, which might limit adoption for developers using other programming languages.
  • Performance Overhead
    The abstraction layer provided by Beam to ensure portability might result in a performance overhead compared to using execution engines directly, potentially affecting performance-sensitive applications.
  • Evolving Ecosystem
    As an evolving framework, Apache Beam’s APIs and ecosystem components might change over time, requiring continuous learning and adaptation from developers to keep up with the latest updates and best practices.

Streamdata.io videos

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Apache Beam videos

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How to Write Batch or Streaming Data Pipelines with Apache Beam in 15 mins with James Malone

More videos:

  • Review - Best practices towards a production-ready pipeline with Apache Beam
  • Review - Streaming data into Apache Beam with Kafka

Category Popularity

0-100% (relative to Streamdata.io and Apache Beam)

Streamdata.io

Apache Beam

Big Data
16 16%
Big Data
84% 84
Analytics
100 100%
Analytics
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100
Data Management
100 100%
Data Management
0% 0

User comments

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Social recommendations and mentions

Based on our record, Apache Beam seems to be more popular. It has been mentiond 15 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.

Streamdata.io mentions (0)

We have not tracked any mentions of Streamdata.io yet. Tracking of Streamdata.io recommendations started around Mar 2021.

Apache Beam mentions (15)

  • A Quick Developer’s Guide to Effective Data Engineering
    Use distributed data processing frameworks like Apache Beam or Apache Spark. - Source: dev.to / 7 days ago
  • Ask HN: Does (or why does) anyone use MapReduce anymore?
    The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / over 1 year ago
  • How do Streaming Aggregation Pipelines work?
    Apache Beam is one of many tools that you can use. Source: over 1 year ago
  • Real Time Data Infra Stack
    Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 2 years ago
  • Google Cloud Reference
    Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / over 2 years ago
View more

What are some alternatives?

When comparing Streamdata.io and Apache Beam, you can also consider the following products

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

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

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

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

Snowflake - Snowflake is the only data platform built for the cloud for all your data & all your users. Learn more about our purpose-built SQL cloud data warehouse.

OpenZoo - OpenZoo is an open-source, distributed, stream and batch processing framework.

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