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Ask HN: What does your BI stack look like?
61–70 of 181 posts
Re: Ask HN: What does your BI stack look like?
#62[deleted]
Re: Ask HN: What does your BI stack look like?
#63A lot of these answers seem to focus on app analytics (e.g. collecting clicks, page views, etc. from Segment). How are people collecting / integrating financial data (e.g. sales, subscriptions, expenses)?
Re: Ask HN: What does your BI stack look like?
#64does anyone use ElasticSearch + Kibana?
Re: Ask HN: What does your BI stack look like?
#65does anyone uses elasticsearch + kibana?
Re: Ask HN: What does your BI stack look like?
#66MariaDB > Metabase
Stitch/Segment > BigQuery > Metabase
Stitch/Segment > BigQuery > Google Data Studio (Curmudgeonly stakeholders refuse to log in to BI tools... but require reporting anyways...)
Re: Ask HN: What does your BI stack look like?
#67ETL: Domo data connectors, Python, SQL
Warehouse: PostgreSQL
Reporting platform: Domo
Re: Ask HN: What does your BI stack look like?
#68No mention for Athena?
Right now we are heavily using it at our org.
Re: Ask HN: What does your BI stack look like?
#69how does one end up doing BI | Data Engineering ? currently a frontend | full-stack JS dev. tired of that world and want to switch to something stable ?
I'd highly recommend the Kimball books, e.g. "The Data Warehouse Toolkit".
Unless you're working with a lot of data, the technical parts of a data warehouse are relatively easy to grasp (the dbt team wrote some great posts on how they structure their projects [1]). After that, what really makes a difference is how you structure our data warehouse, how you model your data in a way that allow you to query it efficiently and ask many questions. That's where the Kimball books shine.
[1] https://community.snowflake.com/s/article/Use-Case-How-We-Co...
Re: Ask HN: What does your BI stack look like?
#70At Grove, we are: Airflow -> S3 -> DBT with Spark/EMR or Redshift/Spectrum -> Redshift data marts -> Looker At least, that’s the way we like our pipelines to work. In practice we have a couple of extractions that land directly in Redshift (we extract Zendesk data, for instance, with Stitch Data). We use Snowplow for click stream analytics. And we’ll likely move from Redshift to Snowflake (or mayybbbeee Bigtable) in Q…
Just curious, if you could highlight some of the problems you've experienced with each "generation" of looker reporting? Was there any specific issues that encouraged the re-implementation?