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Snowplow VS Google Cloud Dataflow

Compare Snowplow VS Google Cloud Dataflow and see what are their differences

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Contents:
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  3. » Reviews
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Snowplow logo Snowplow

Snowplow is an enterprise-strength event analytics platform.

Google Cloud Dataflow logo Google Cloud Dataflow

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

  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

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.

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

Snowplow videos

+ Add

What is Snowplow

Google Cloud Dataflow videos

+ Add

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to Snowplow and Google Cloud Dataflow)

Snowplow

Google Cloud Dataflow

Analytics
100 100%
Analytics
0% 0
Big Data
0 0%
Big Data
100% 100
Web Analytics
100 100%
Web Analytics
0% 0
Data Dashboard
17 17%
Data Dashboard
83% 83

User comments

Share your experience with using Snowplow and Google Cloud Dataflow. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Snowplow and Google Cloud Dataflow

Snowplow Reviews

We have no reviews of Snowplow yet.
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Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Google Cloud Dataflow might be a bit more popular than Snowplow. We know about 14 links to it since March 2021 and only 10 links to Snowplow. 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.

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
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Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / almost 3 years ago
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What are some alternatives?

When comparing Snowplow and Google Cloud Dataflow, you can also consider the following products

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.

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

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

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

Google Analytics 360 Suite - Enterprise analytics for your marketing.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?

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