Snowplow
Druid
Our great sponsors
Snowplow | Druid | |
---|---|---|
21 | 24 | |
6,734 | 13,188 | |
0.4% | 0.6% | |
8.7 | 9.9 | |
about 1 month ago | 4 days ago | |
Scala | Java | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Snowplow
-
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.
-
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!
-
What companies/startups are using Scala (open source projects on github)?
There are so many of them in big data, e.g. Kafka, Spark, Flink, Delta, Snowplow, Finagle, Deequ, CMAK, OpenWhisk, Snowflake, TheHive, TVM-VTA, etc.
-
We should start looking for google analytics alternatives
I added Snowplow Analytics to a site with a lot of traffic. It was a very basic implementation, where data is collected with Snowplow, stored in google big query, and visualized in google data studio. The data is collected from the caching/web server combined with a client-side tracker.
-
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 you. Want to compare to other similar companies in the portfolio to see how they are performing, same story. Data scientists will build and structure the data and store it and manipulate it and extract the value from it so that the analysts and sales people can then come in and do some selling. Show the customers what they got for their money and guarantee the renewal.
-
Click tracking solution for links and buttons on website
if you want self host, check out https://github.com/snowplow/snowplow
-
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 full CDP solution so we chose Snowplow.
- Austrian Data Protection Authority declares Google Analytics as not compliant with GDPR. Decision relevant for almost all EU websites.
-
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.
-
Cookie-based tracking is dead
I added Snowplow Analytics to a site with a lot of traffic. It was a very basic implementation, where data is collected with Snowplow, stored in google big query, and visualized in google data studio. The data is collected from the caching/web server combined with a 1st part cookie set in the user's browser.
Druid
-
How to choose the right type of database
Apache Druid: Focused on real-time analytics and interactive queries on large datasets. Druid is well-suited for high-performance applications in user-facing analytics, network monitoring, and business intelligence.
-
Choosing Between a Streaming Database and a Stream Processing Framework in Python
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in maintaining the freshness of results. The query in the streaming database focuses on recent data, making it suitable for continuous monitoring. Using streaming databases, you can run queries like finding the top 10 sold products where the “top 10 product list” might change in real-time.
-
Show HN: The simplest tiny analytics tool – storywise
https://github.com/apache/druid
It's always a question of tradeoffs.
The awesome-selfhosted project has a nice list of open-source analytics projects. It's really good inspiration to dig into these projects and find out about the technology choices that other open-source tools in the space have made.
-
Analysing Github Stars - Extracting and analyzing data from Github using Apache NiFi®, Apache Kafka® and Apache Druid®
Spencer Kimball (now CEO at CockroachDB) wrote an interesting article on this topic in 2021 where they created spencerkimball/stargazers based on a Python script. So I started thinking: could I create a data pipeline using Nifi and Kafka (two OSS tools often used with Druid) to get the API data into Druid - and then use SQL to do the analytics? The answer was yes! And I have documented the outcome below. Here’s my analytical pipeline for Github stars data using Nifi, Kafka and Druid.
-
Apache Druid® - an enterprise architect's overview
Apache Druid is part of the modern data architecture. It uses a special data format designed for analytical workloads, using extreme parallelisation to get data in and get data out. A shared-nothing, microservices architecture helps you to build highly-available, extreme scale analytics features into your applications.
-
Real Time Data Infra Stack
Apache Druid
-
When you should use columnar databases and not Postgres, MySQL, or MongoDB
But then you realize there are other databases out there focused specifically on analytical use cases with lots of data and complex queries. Newcomers like ClickHouse, Pinot, and Druid (all open source) respond to a new class of problem: The need to develop applications using endpoints published on analytical queries that were previously confined only to the data warehouse and BI tools.
-
Druids by Datadog
Datadog's product is a bit too close to Apache Druid to have named their design system so similarly.
From https://druid.apache.org/ :
> Druid unlocks new types of queries and workflows for clickstream, APM, supply chain, network telemetry, digital marketing, risk/fraud, and many other types of data. Druid is purpose built for rapid, ad-hoc queries on both real-time and historical data.
-
Mom at 54 is thinking about coding and a complete career shift. Thoughts?
Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s.
-
Building an arm64 container for Apache Druid for your Apple Silicon
Fortunately, it is super easy to build your own leveraging the binary distribution and existing docker.sh.
What are some alternatives?
PostHog - 🦔 PostHog provides open-source product analytics, session recording, feature flagging and A/B testing that you can self-host.
iced - A cross-platform GUI library for Rust, inspired by Elm
Rudderstack - Privacy and Security focused Segment-alternative, in Golang and React
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
Matomo - Empowering People Ethically with the leading open source alternative to Google Analytics that gives you full control over your data. Matomo lets you easily collect data from websites & apps and visualise this data and extract insights. Privacy is built-in. Liberating Web Analytics. Star us on Github? +1. And we love Pull Requests!
Apache Cassandra - Mirror of Apache Cassandra
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
Apache HBase - Apache HBase
jitsu - Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days
egui - egui: an easy-to-use immediate mode GUI in Rust that runs on both web and native
Countly - Countly is a product analytics platform that helps teams track, analyze and act-on their user actions and behaviour on mobile, web and desktop applications.
Scylla - NoSQL data store using the seastar framework, compatible with Apache Cassandra