bigquery-utils VS PostgreSQL

Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research.

Data Transformations: This phase involves modifying and integrating tables to generate new tables optimized for analytical use. Consider this example: you want to understand the purchasing behavior of customers aged between 20-30 in your online shop. This means you'll need to join product, customer, and transaction data to create a unified table for analytics. These data preparation tasks (e.g., joining fragmented data) for analysis are essentially what "Data Transformations" entail. At FINN, technologies utilized in this phase include BigQuery as a data warehouse, dbt for data transformation, and a combination of GitHub Actions and Datafold for quality assurance.

Introduction In today's data-driven world, transforming raw data into valuable insights is crucial. This process, however, often involves complex tasks that demand efficiency, scalability, and reliability. Enter dbt Cloud—a powerful tool that simplifies data transformations on Google BigQuery. In this article, we'll take you through a step-by-step guide on how to run BigQuery transformations using dbt Cloud. Let's dive in!

You'll want to evaluate what BigQuery has to offer and see if it makes sense for you to move over.

Watch the introductory videos on BigQuery on the Google Cloud Platform website (https://cloud.google.com/bigquery)

Within the audit logs you can find BigQueryAuditMetadata details in the protoPayload.metadataJson submessage in the Cloud Logging LogEntry message. GCP has offered several versions of BigQuery audit logs so there are both older “v1” and newer “v2” versions. The v1 logs report API invocations and live within the protoPayload.serviceData submessage while the v2 logs report resource interactions like which tables were read from and written to by a given query or which tables expired. The v2 data lives in a new field formatted as a JSON blob within the BigQueryAuditMetadata detail inside the protoPayload.metadataJson submessage. In v2 logs the older protoPayload.serviceData submessage does exist for backwards compatibility but the information is not set or used. We scrape details from the JobChange object instead. We referenced the GCP bigquery-utils Git repo for how to use INFORMATION_SCHEMA queries and audit logs queries.

Every time I buy a new computer, the first thing I install is the servers for MySql and Postgres, the two most common databases. This way, I can start the databases with a simple command like this:

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

PostgreSQL

The LMS Moodle Operator serves as a meta-operator, orchestrating the deployment and management of Moodle instances in Kubernetes. It handles the entire stack required to run Moodle, including components like Postgres, Keydb, NFS-Ganesha, and Moodle itself. Each of these components has its own Kubernetes Operator, ensuring seamless integration and management.

Another key feature of txtai is being able to quickly move from prototyping to production. This article will demonstrate how txtai can integrate with Postgres, a powerful, production-ready and open source object-relational database system. After txtai persists content to Postgres, we'll show it can be directly queried with SQL from any Postgres client

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).