Alternatives to Snowplow logo

Alternatives to Snowplow

Google Analytics, Segment, Mixpanel, Piwik, and Heap are the most popular alternatives and competitors to Snowplow.
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What is Snowplow and what are its top alternatives?

Snowplow is a real-time event data pipeline that lets you track, contextualize, validate and model your customers’ behaviour across your entire digital estate.
Snowplow is a tool in the Custom Analytics category of a tech stack.
Snowplow is an open source tool with 6K GitHub stars and 1.2K GitHub forks. Here’s a link to Snowplow's open source repository on GitHub

Top Alternatives to Snowplow

  • Google Analytics

    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

  • Segment

    Segment

    Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...

  • Mixpanel

    Mixpanel

    Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...

  • Piwik

    Piwik

    Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code. ...

  • Heap

    Heap

    Heap automatically captures every user action in your app and lets you measure it all. Clicks, taps, swipes, form submissions, page views, and more. Track events and segment users instantly. No pushing code. No waiting for data to trickle in. ...

  • Snowflake

    Snowflake

    Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn. ...

  • Kafka

    Kafka

    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. ...

  • Amplitude

    Amplitude

    Amplitude provides scalable mobile analytics that helps companies leverage data to create explosive user growth. Anyone in the company can use Amplitude to pinpoint the most valuable behavioral patterns within hours. ...

Snowplow alternatives & related posts

Google Analytics logo

Google Analytics

113.1K
37.5K
5K
Enterprise-class web analytics.
113.1K
37.5K
+ 1
5K
PROS OF GOOGLE ANALYTICS
  • 1.5K
    Free
  • 925
    Easy setup
  • 888
    Data visualization
  • 696
    Real-time stats
  • 403
    Comprehensive feature set
  • 180
    Goals tracking
  • 154
    Powerful funnel conversion reporting
  • 137
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
  • 13
    Updated regulary
  • 8
    Interactive Documentation
  • 3
    Google play
  • 2
    Advanced ecommerce
  • 2
    Industry Standard
  • 2
    Walkman music video playlist
  • 1
    Medium / Channel data split
  • 1
    Financial Management Challenges -2015h
  • 1
    Lifesaver
  • 1
    Easy to integrate
CONS OF GOOGLE ANALYTICS
  • 9
    Confusing UX/UI
  • 6
    Super complex
  • 5
    Very hard to build out funnels
  • 3
    Poor web performance metrics
  • 2
    Very easy to confuse the user of the analytics
  • 2
    Time spent on page isn't accurate out of the box

related Google Analytics posts

Tassanai Singprom

This is my stack in Application & Data

JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

My Utilities Tools

Google Analytics Postman Elasticsearch

My Devops Tools

Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

My Business Tools

Slack

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Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 136.9K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

See more
Segment logo

Segment

2.8K
742
275
A single hub to collect, translate and send your data with the flip of a switch.
2.8K
742
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PROS OF SEGMENT
  • 86
    Easy to scale and maintain 3rd party services
  • 49
    One API
  • 39
    Simple
  • 25
    Multiple integrations
  • 19
    Cleanest API
  • 10
    Easy
  • 9
    Free
  • 8
    Mixpanel Integration
  • 7
    Segment SQL
  • 6
    Flexible
  • 4
    Google Analytics Integration
  • 2
    Salesforce Integration
  • 2
    SQL Access
  • 2
    Clean Integration with Application
  • 1
    Own all your tracking data
  • 1
    Quick setup
  • 1
    Clearbit integration
  • 1
    Beautiful UI
  • 1
    Integrates with Apptimize
  • 1
    Escort
  • 1
    Woopra Integration
CONS OF SEGMENT
  • 2
    Not clear which events/options are integration-specific
  • 1
    Limitations with integration-specific configurations
  • 1
    Client-side events are separated from server-side

related Segment posts

Robert Zuber

Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 136.9K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

See more
Mixpanel logo

Mixpanel

5.7K
2.5K
433
Powerful, self-serve product analytics to help you convert, engage, and retain more users
5.7K
2.5K
+ 1
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PROS OF MIXPANEL
  • 143
    Great visualization ui
  • 107
    Easy integration
  • 76
    Great funnel funcionality
  • 58
    Free
  • 22
    A wide range of tools
  • 15
    Powerful Graph Search
  • 11
    Responsive Customer Support
  • 1
    Nice reporting
CONS OF MIXPANEL
  • 2
    Messaging (notification, email) features are weak
  • 2
    Paid plans can get expensive

related Mixpanel posts

Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 136.9K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

See more
Yasmine de Aranda
Chief Growth Officer at Huddol · | 6 upvotes · 100.8K views

Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

See more
Piwik logo

Piwik

1.4K
504
74
The ultimate open source alternative to Google Analytics
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PROS OF PIWIK
  • 35
    It's good to have an alternative to google analytics
  • 27
    Self-hosted
  • 10
    Easy setup
  • 2
    Not blocked by Brave
  • 0
    Great customs
CONS OF PIWIK
  • 2
    Hard to export data

related Piwik posts

Heap logo

Heap

637
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Automatically capture every user action in your app and measure it all
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PROS OF HEAP
  • 36
    Automatically capture every user action
  • 23
    No code required
  • 21
    Free Plan
  • 14
    Real-time insights
  • 11
    Track custom events
  • 10
    Define user segments
  • 7
    Define active users
  • 2
    Fun to use
  • 2
    Redshift integration
CONS OF HEAP
    Be the first to leave a con

    related Heap posts

    Shared insights
    on
    HeapHeapPendoPendoMixpanelMixpanel

    Hello, We are a medical technology company looking to integrate an in-app analytics tool. We've evaluated Mixpanel, Pendo, and Heap and are most impressed that Heap will solve our issues. We'd like to be able to determine not only clicks (con of Pendo) but also swipes and other user gestures within our app. Not sold on all three of these, can also look at other tools. We use Cordova, so hoping to find something compatible with that. Any advice?

    Thanks

    See more
    Snowflake logo

    Snowflake

    669
    802
    16
    The data warehouse built for the cloud
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    PROS OF SNOWFLAKE
    • 3
      Good Performance
    • 2
      User Friendly
    • 2
      Serverless
    • 2
      Great Documentation
    • 2
      Multicloud
    • 2
      Public and Private Data Sharing
    • 1
      Usage based billing
    • 1
      Innovative
    • 1
      Economical
    CONS OF SNOWFLAKE
      Be the first to leave a con

      related Snowflake posts

      Shared insights
      on
      Google BigQueryGoogle BigQuerySnowflakeSnowflake

      I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.

      What's nice too is that it has SQL-based ML tools, and it has great GIS support!

      See more
      Shared insights
      on
      SnowflakeSnowflakeHadoopHadoopMarkLogicMarkLogic

      For a property and casualty insurance company, we currently use MarkLogic and Hadoop for our raw data lake. Trying to figure out how snowflake fits in the picture. Does anybody have some good suggestions/best practices for when to use and what data to store in Mark logic versus Snowflake versus a hadoop or all three of these platforms redundant with one another?

      See more
      Kafka logo

      Kafka

      16.2K
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      Distributed, fault tolerant, high throughput pub-sub messaging system
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      PROS OF KAFKA
      • 122
        High-throughput
      • 116
        Distributed
      • 87
        Scalable
      • 81
        High-Performance
      • 65
        Durable
      • 36
        Publish-Subscribe
      • 19
        Simple-to-use
      • 15
        Open source
      • 10
        Written in Scala and java. Runs on JVM
      • 6
        Message broker + Streaming system
      • 4
        Avro schema integration
      • 2
        Suport Multiple clients
      • 2
        Robust
      • 2
        KSQL
      • 2
        Partioned, replayable log
      • 1
        Fun
      • 1
        Extremely good parallelism constructs
      • 1
        Simple publisher / multi-subscriber model
      • 1
        Flexible
      CONS OF KAFKA
      • 27
        Non-Java clients are second-class citizens
      • 26
        Needs Zookeeper
      • 7
        Operational difficulties
      • 2
        Terrible Packaging

      related Kafka posts

      Eric Colson
      Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

      The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

      Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

      At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

      For more info:

      #DataScience #DataStack #Data

      See more
      John Kodumal

      As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

      We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

      See more
      Amplitude logo

      Amplitude

      721
      527
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      User analytics to fuel explosive user growth
      721
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      PROS OF AMPLITUDE
      • 9
        Great for product managers
      • 7
        Easy setup
      • 6
        Efficient analysis
      • 2
        Free for up to 10M user actions per month
      • 2
        Event streams for individual users
      • 2
        Chart edits get their own URLs
      • 1
        Engagement Matrix is super helpful
      • 1
        Fast
      • 1
        Behavioral cohorts
      • 1
        Great UI
      CONS OF AMPLITUDE
      • 4
        Super expensive once you're past the free plan

      related Amplitude posts

      Max Musing
      Founder & CEO at BaseDash · | 8 upvotes · 136.9K views

      Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

      Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

      See more
      Yonas Beshawred

      Adopting Amplitude was one of the best decisions we've made. We didn't try any of the alternatives- the free tier was really generous so it was easy to justify trying it out (via Segment). We've had Google Analytics since inception, but just for logged out traffic. We knew we'd need some sort of #FunnelAnalysisAnalytics solution, so it came down to just a few solutions.

      We had heard good things about Amplitude from friends and even had a consultant/advisor who was an Amplitude pro from using it as his company, so he kinda convinced us to splurge on the Enterprise tier for the behavioral cohorts alone. Writing the queries they provide via a few clicks in their UI would take days/weeks to craft in SQL. The behavioral cohorts allow us to create a lot of useful retention charts.

      Another really useful feature is kinda minor but kinda not. When you change a saved chart, a new URL gets generated and is visible in your browser (chartURL/edit) and that URL is immediately available to share with your team. It may sound inconsequential, but in practice, it makes it really easy to share and iterate on graphs. Only complaint is that you have to explicitly tag other team members as owners of whatever chart you're creating for them to be able to edit it and save it. I can see why this is the case, but more often than not, the people I'm sharing the chart with are the ones I want to edit it 🤷🏾‍♂️

      The Engagement Matrix feature is also really helpful (once you filter out the noisy events). Charts and dashboards are also great and make it easy for us to focus on the important metrics. We've been using Amplitude in production for about 6 months now. There's a bunch of other features we don't use regularly like Pathfinder, etc that I personally don't fully understand yet but I'm sure we'll start using them eventually.

      Again, haven't tried any of the alternatives like Heap, Mixpanel, or Kissmetrics so can't speak to those, but Amplitude works great for us.

      #analytics analyticsstack

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