meltano VS nodejs-bigquery

- https://github.com/meltano/meltano

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Meltano[0] might be of interest to you. Easy way to move data that should be very familiar for software engineers. If a connector doesn't exist our SDK makes it easy to build it.

[0] https://github.com/meltano/meltano

(disclaimer - I work at Meltano)

We currently don't do any process optimization on a per-stream basis when doing an extract. We have seen folks in the community running each tap separately for each stream which can speed it up. We've got an issue around this (Melturbo).

Meltano

I built my data pipeline to Extract some data from websites and CSV files, Load it into my database, and Transform it into a reporting-ready schema. I used Python and Pandas to extract and load some of the data and Meltano to load some additional supporting data. All of that data went into a PostgreSQL database hosted in the cloud on Azure where I then used dbt to create data models in the database optimized for reporting. Finally, I use Metabase to visualize the data. (whew! that's a lot of moving parts!)

I'm learning https://meltano.com/ right now, so am building custom Taps, mostly for fun. I'm enjoying it. I'm pulling in a variety of data from https://www.geonames.org/ and Canadian weather/climate data into BigQuery

https://meltano.com/ is ELT but I like it

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