DBT-Bon-Voyage VS nodejs-bigquery

This component of the trinity is mainly involved in processing your data according to your business needs and also storing the data. The transform component is where business logic is domiciled. This is where you perform data transformations to generate business value from data. They could be as simple as filters and as complex as joins, rolling column computation, pivoting, etc. Most transformations will be done in SQL. This is because of its ability to perform data processing and also compatibility with the storage systems. Most of the storage systems will be designed to be SQL compatible and hence, SQL as the transformation language is the most common. A common tool in this space is Data Build Tool DBT A powerful tool that supercharges SQL by introducing other programming paradigms such as jinja templating, source definitions, hooks, variables and sanity checks/tests among others. The alternative would be stored procedures and SQL scripts but you would be losing out on all the great features of DBT. I have had a go at it in my personal github which you can check over here. DBT Bon Voyage Fivetran also offers an SQL based transformation interface where one can run their SQL scripts.

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