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Snowplow
BySnowplow AnalyticsSnowplow Analytics

Snowplow

#22in Analytics
Discussions1
Followers174
OverviewDiscussions1

What is Snowplow?

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 Analytics category of a tech stack.

Key Features

Track rich events from your websites, mobile apps, server-side systems, third party systems and any type of connected device, so that you have a record of what happened, when, and to whomLoad your data into your data warehouse of choice to power sophisticated analyticsProcess your data including validating, enriching and modeling itYour data is available in real-time via Amazon Kinesis, Google Pub/Sub and BigQuery to power real-time applications and reportsYour data pipeline is running in your cloud environment giving you full ownership and control of your data

Snowplow Pros & Cons

Pros of Snowplow

  • ✓Can track any type of digital event
  • ✓Data quality
  • ✓First-party tracking
  • ✓Completely open source
  • ✓Real-time streams
  • ✓Redshift integration
  • ✓BigQuery integration
  • ✓Snowflake integration

Cons of Snowplow

No cons listed yet.

Snowplow Alternatives & Comparisons

What are some alternatives to Snowplow?

Keen

Keen

Keen is a powerful set of API's that allow you to stream, store, query, and visualize event-based data. Customer-facing metrics bring SaaS products to the next level with acquiring, engaging, and retaining customers.

Ahoy

Ahoy

Ahoy provides a solid foundation to track visits and events in Ruby, JavaScript, and native apps.

Bitdeli

Bitdeli

Build dashboards and reports with exactly the metrics you need using plain Python scripts. There is nothing new to learn. Bitdeli keeps your results up to date, no matter how much data you have or how complex your metrics are. Get started in minutes with our growing library of open-source analytics, created by experienced data hackers.

Iteratively

Iteratively

Iteratively helps teams capture reliable product analytics they can trust. It eliminates the most common causes of error during the definition and implementation of tracking plans, and cuts down on the time it takes to correctly instrument the product. As a result, folks that consume product analytics get exactly what they spec'd out and can rely on the incoming data knowing it is trustworthy and accurate.

Quickmetrics

Quickmetrics

It is a service for collecting, analyzing and visualizing custom metrics. It can be used to track anything from signups to server response times. Sending events is super simple.

Objectiv

Objectiv

Open-source product analytics infrastructure with a generic event taxonomy, so models built on one company's data set can be deployed and run on another.

Snowplow Integrations

Groonga, Iteratively, Indicative, Elasticsearch, Microsoft Azure and 7 more are some of the popular tools that integrate with Snowplow. Here's a list of all 12 tools that integrate with Snowplow.

Groonga
Groonga
Iteratively
Iteratively
Indicative
Indicative
Elasticsearch
Elasticsearch
Microsoft Azure
Microsoft Azure
Amazon S3
Amazon S3
PostgreSQL
PostgreSQL
Amazon Redshift
Amazon Redshift
AzureDataStudio
AzureDataStudio
Google Cloud Storage
Google Cloud Storage
Kafka
Kafka
Google BigQuery
Google BigQuery

Snowplow Discussions

Discover why developers choose Snowplow. Read real-world technical decisions and stack choices from the StackShare community.

kew44
kew44

Nov 10, 2022

Needs adviceonAmazon S3Amazon S3AWS GlueAWS GlueAzure Data FactoryAzure Data Factory

Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:

  1. Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
  2. Storage->@{Amazon S3}|tool:25| seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
  3. Data Catalog-> @{AWS Glue}|tool:8906|? @{Azure Data Factory}|tool:6356|? @{Snowplow}|tool:3097|? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
  4. Partitions-> I've seen @{Cassandra}|tool:1032| and YARN mentioned, but have no experience with either
  5. Processing-> We want to use SAS if at all possible. What will work with SAS code?
  6. Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
  7. I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
  8. An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, @{JavaScript}|tool:1209|, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!
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