bigquery-utils VS nodejs-bigquery

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.

If you've ever wondered what it's like to manage a BigQuery instance at Reddit scale, know that it's exactly like smaller systems just with much, much bigger numbers in the logs. Database management fundamentals are eerily similar regardless of scale or platform; BigQuery handles just about anything we throw at it, and we do indeed throw it the whole book. Our BigQuery platform is more than 100 petabytes of data that supports data science, machine learning, and analytics workloads that drive experiments, analytics, advertising, revenue, safety, and more. As Reddit grew, so did the workload velocity and complexity within BigQuery and thus the need for more elegant and fine-tuned workload management.

Now I know I've got some data I could use, I now need to find a platform that I can use to analyse the data coming from the Forem API. I did consider some other pieces of software, such as Google BigQuery (with looker studio) and ElasticSearch (with Kibana), I ultimately went with OpenSearch which is essentially a forked version of ElasticSearch maintained by AWS. The main reasons are that I could host it locally for free (unlike BigQuery). I do have some prior experience with both elastic (back when it was called ELK) and OpenSearch, but my work with OpenSearch was far more recent, so I decided to go with that.

First, in one of our recent projects, we helped our client to run the cloud-based infrastructure of their entirely automated, real-time SEO platform. The solution rested in the safe familiarity of Google’s popular cloud-based data centres (i.e. Google Cloud Platform), whilst also making use of BigQuery — a serverless, multi-cloud data warehouse.

Bigquery as our data warehouse

This post in github thinks it may be an error in bigquery's backend.

A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of data-warehouse products include Snowflake, Google BigQuery, Azure Synapse Analytics, and Amazon Redshift — all of which, incidentally, are easily managed with Pulumi.

Google BigQuery