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Apache Flink VS Snowplow

Compare Apache Flink VS Snowplow and see what are their differences

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Apache Flink logo Apache Flink

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

Snowplow logo Snowplow

Snowplow is an enterprise-strength event analytics platform.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Snowplow Landing page
    Landing page //
    2023-10-05

Our Mission is to empower data teams to build a strategic data capability that delivers high-quality, complete, and relevant data across the business. Our users and customers use Snowplow for numerous use cases – from web and mobile analytics to advanced analytics and the production of AI & ML ready data, whilst maintaining data privacy compliance. Our customers reflect the diversity of use cases that Snowplow solves and includes Strava, The Wall Street Journal, CapitalOne, WeTransfer, Nordstrom, DataDog, Auto Trader, GitLab and many more.

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Snowplow features and specs

  • Data Ownership
    Snowplow allows organizations to own their data end-to-end, providing more control over data collection, storage, and usage compared to third-party analytics platforms.
  • Flexibility
    The platform offers a high degree of customization, allowing businesses to track custom events and define their own data structures, which is ideal for complex or unique data needs.
  • Real-time Analytics
    Snowplow supports real-time data processing, which enables organizations to make swift, data-driven decisions and insights.
  • Open Source
    Being an open-source solution, Snowplow can be adopted without licensing costs, and there is a community for support and continuous development.
  • Cross-Platform Tracking
    Snowplow allows for tracking across multiple platforms and devices, providing a unified view of the customer journey.
  • Data Enrichment
    The solution offers capabilities to enrich event data with additional context such as geo-location or user session data, adding more value to raw data.

Possible disadvantages of Snowplow

  • Complex Setup
    Setting up Snowplow requires significant technical expertise, including infrastructure management, which may be a barrier for smaller teams or companies without specialized resources.
  • Maintenance Effort
    Ongoing maintenance and updates to the Snowplow setup can be labor-intensive, requiring continuous monitoring and management.
  • Infrastructure Costs
    While Snowplow itself is open source, the infrastructure required to run it (e.g., servers, databases, data storage) can be costly.
  • Learning Curve
    Due to its flexibility and customization options, there is a steep learning curve for new users, which may delay the onboarding process.
  • Data Privacy Responsibility
    Since organizations own their data, they are also fully responsible for compliance with data privacy regulations (e.g., GDPR), necessitating additional efforts in data governance.

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Snowplow videos

+ Add

What is Snowplow

Category Popularity

0-100% (relative to Apache Flink and Snowplow)

Apache Flink

Snowplow

Big Data
100 100%
Big Data
0% 0
Analytics
0 0%
Analytics
100% 100
Stream Processing
100 100%
Stream Processing
0% 0
Web Analytics
0 0%
Web Analytics
100% 100

User comments

Share your experience with using Apache Flink and Snowplow. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Apache Flink should be more popular than Snowplow. It has been mentiond 41 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.

Apache Flink mentions (41)

  • What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community
    Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / 11 days ago
  • Is RisingWave the Next Apache Flink?
    Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / 24 days ago
  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 29 days ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / about 1 month ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / about 1 month ago
View more

Snowplow mentions (10)

  • Open-source data collection & modeling platform for product analytics
    We’ve also thought about Ops :-). There’s a backend 'Collector' that stores data in Postgres, for instance to use while developing locally, or if you want to get set up quickly. But there’s also full integration with Snowplow, which works seamlessly with an existing Snowplow setup as well. - Source: dev.to / over 2 years ago
  • What are the different ways to collect large amounts of data, like millions of rows?
    Sure thing! Say you run an online store. Your source systems could be the inventory, orders or customer databases. You could also track click/site behavior with something like snowplow. An ERP system is essentially just a combination of what I mentioned previously. Another good example is a CRM such as Salesforce or Zendesk. Hopefully that helps! Source: almost 3 years ago
  • The Big Data Game – Because even a simple query can send you on an unexpected journey. Help the 8-bit data engineer to get the data
    Well if you have to structure and create Schema and manage Data Warehouses, you need a tool to do that, so in the background you see SnowPlow, which helps you do just that. Make the data into some kind of sensible structure so that later on business analysts can come see whats up. Want to do a quarterly report on how you performed, go to the application that goes to the data warehouse and builds your report for... Source: about 3 years ago
  • Reference Data Stack for Data-Driven Startups
    We also have telemetry set up on our Monosi product which is collected through Snowplow,. As with Airbyte, we chose Snowplow because of its open source offering and because of their scalable event ingestion framework. There are other open source options to consider including Jitsu and RudderStack or closed source options like Segment. Since we started building our product with just a CLI offering, we didn’t need a... - Source: dev.to / about 3 years ago
  • Ask HN: Best alternatives to Google Analytics in 2021?
    Https://matomo.org That's the only full featured open source competitor I am aware of, so it should be mentioned. https://snowplowanalytics.com/ Somewhat FOSS. There was a story there, but I don't remember the details. - Source: Hacker News / over 3 years ago
View more

What are some alternatives?

When comparing Apache Flink and Snowplow, you can also consider the following products

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

Google Analytics - Improve your website to increase conversions, improve the user experience, and make more money using Google Analytics. Measure, understand and quantify engagement on your site with customized and in-depth reports.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Glass Analytics - Google Analytics alternative that shows you exactly how visitors become customers.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Simple Analytics - The privacy-first Google Analytics alternative located in Europe.

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