A modern data stack for startups

• pgsink

  5 76 0.0 Go

  Logically replicate data out of Postgres into sinks (files, Google BigQuery, etc)

It used to be that companies would write their own hacky scripts to perform this extraction - I've had terrible incidents caused by ETL database triggers in the past, and even built a few generic ETL tools myself.

• corp

  12 413 4.6

  Assets related to the operation of Fishtown Analytics.

  

While the tool choice is obvious, how to use dbt is going to be a more controversial. There's a load of great resources on dbt best practices, but as you can see from my Slack questions, there's enough ambiguity to tie you up.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• nodejs-bigquery

  43 460 8.0 TypeScript

  Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics.

  

With data warehouse solutions (BigQuery, Snowflake, Redshift) going mainstream, modern data stacks are becoming increasingly boring - great news if you're starting from scratch!

• dbt-metabase

  1 432 8.0 Python

  dbt + Metabase integration

So how do we get this into Metabase? There's a tool called dbt-metabase that can infer Metabase semantic type information from the dbt schema and push it into Metabase- we run this whenever complete a dbt build, helping sync Metabase with whatever new fields we may have added.

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