Snowplow VS PostgreSQL

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.

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!

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.

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.

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.

if you want self host, check out https://github.com/snowplow/snowplow

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.

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.

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.

SQL (Structured Query Language) databases are relational databases. They organize data into tables with rows and columns, and they use SQL for querying and managing data. Examples include MySQL, PostgreSQL, and SQLite.

Node.js, MySQL and PostgreSQL servers installed on your machine

Each app’s front end is built with Qwik and uses Tailwind for styling. The server-side is powered by Qwik City (Qwik’s official meta-framework) and runs on Node.js hosted on a shared Linode VPS. The apps also use PM2 for process management and Caddy as a reverse proxy and SSL provisioner. The data is stored in a PostgreSQL database that also runs on a shared Linode VPS. The apps interact with the database using Drizzle, an Object-Relational Mapper (ORM) for JavaScript. The entire infrastructure for both apps is managed with Terraform using the Terraform Linode provider, which was new to me, but made provisioning and destroying infrastructure really fast and easy (once I learned how it all worked).

I've used MySQL for years. But recently, I found myself working PostgreSQL and simple things like dumping and restoring a database are different enough that I decided to document the process. It's straightforward enough once I knew how.

A running Rails application needs a database to connect to. You may already have your database of choice installed, but if not, I recommend PostgreSQL, or Postgres for short. On a Mac, probably the easiest way to install it is with Posrgres.app. Another option, the one I prefer, is to use Homebrew. With Homebrew installed, this command will install PostgreSQL version 16 along with libpq:

PostgreSQL

PostgreSQL: Offers a robust feature set and strong compliance with SQL standards, making it suitable for a wide range of applications, from simple to complex, particularly where data integrity and extensibility are key.

FerretDB is an open source document database that adds MongoDB compatibility to other database backends, such as Postgres and SQLite. By using FerretDB, developers can access familiar MongoDB features and tools using the same syntax and commands for many of their use cases.

To better understand how SQL injection works, let's quickly create a vulnerable app using Node.js, Express, and a PostgreSQL database. The application takes user input from a form, constructs a SQL query, and executes it against the database to fetch some data.

We'll use PostgreSQL, and first of all, you need to install PostgreSQL if you haven't installed it yet. https://www.postgresql.org/