Software Alternatives, Accelerators & Startups
Register | Login

Snowplow VS Apache Spark

Compare Snowplow VS Apache Spark and see what are their differences

Electe
Discover Electe, our data analytics platform dedicated to SMEs. Don't let your data go unused, take your business into the future! featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Snowplow logo Snowplow

Snowplow is an enterprise-strength event analytics platform.

Apache Spark logo Apache Spark

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

  • Apache Spark Landing page
    Landing page //
    2021-12-31

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 Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Snowplow videos

+ Add

What is Snowplow

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

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

Snowplow

Apache Spark

Analytics
100 100%
Analytics
0% 0
Databases
0 0%
Databases
100% 100
Web Analytics
100 100%
Web Analytics
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Snowplow and Apache Spark. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

Snowplow Reviews

We have no reviews of Snowplow yet.
Be the first one to post

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Snowplow. It has been mentiond 70 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.

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

Apache Spark mentions (70)

  • 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 / 26 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 28 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Snowplow and Apache Spark, 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.

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

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

Hadoop - Open-source software for reliable, scalable, distributed computing

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

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Loading...