elementary-ext VS nodejs-bigquery

- name: elementary namespace: elementary pip_url: elementary-data[platform]==0.6.3 git+https://github.com/potloc/elementary-ext.git executable: elementary_invoker settings: - name: project_dir kind: string value: ${MELTANO_PROJECT_ROOT}/transform/ - name: profiles_dir kind: string value: ${MELTANO_PROJECT_ROOT}/transform/profiles/platform/ - name: file_path kind: string value: ${MELTANO_PROJECT_ROOT}/path/to/report.html - name: skip_pre_invoke env: ELEMENTARY_EXT_SKIP_PRE_INVOKE kind: boolean value: true description: Whether to skip pre-invoke hooks which automatically run dbt clean and deps - name: slack-token kind: password - name: slack-channel-name kind: string value: elementary-notifs config: profiles-dir: ${MELTANO_PROJECT_ROOT}/transform/profiles/platform/ file-path: ${MELTANO_PROJECT_ROOT}/path/to/report.html slack-channel-name: your_channel_name skip_pre_invoke: true commands: initialize: args: initialize executable: elementary_extension describe: args: describe executable: elementary_extension monitor-report: args: monitor-report executable: elementary_extension monitor-send-report: args: monitor-send-report executable: elementary_extension

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