jitsu VS monosi

you may check: https://github.com/jitsucom/jitsu. "Jitsu is an open-source Segment alternative. Fully-scriptable data ingestion engine for modern data teams. Set-up a real-time data pipeline in minutes, not days"

You can create an API endpoint, and send those JSON to it. In the "destination" part, it can sync to clickhouse (one of many choices, like redshift, snowflake,besides clickhouse) very quickly, and flatten the JSON into columns. If there is new key found in JSON, it will create a new column in clickhouse.

We also have telemetry set up on our Monosi product which is collected through Snowplow,. As with Airbyte, we chose Snowplow because of its open source offering and because of their scalable event ingestion framework. There are other open source options to consider including Jitsu and RudderStack or closed source options like Segment. Since we started building our product with just a CLI offering, we didn’t need a full CDP solution so we chose Snowplow.

Ingestion / Extraction: Airbyte, Singer, Jitsu

Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset

I’m just saying this is better:

We are building Jitsu, (https://github.com/jitsucom/jitsu, https://jitsu.com/) We help companies collect events from their apps, websites, and APIs and send them to databases.

Think of us as an open-source Segment alternative.

I also found https://github.com/monosidev/monosi but it seems there are no activities in the repository from last year.

I'm building an open source data observability platform - https://github.com/monosidev/monosi that visualizes metadata collected from data warehouses. Databricks is currently not supported (contributions welcome!), but it may help to take a look at how we approach the anomaly detection & visualization aspects.

Building an open source tool that lets you monitor PostgreSQL instances form anomalies in data coming in - https://github.com/monosidev/monosi

It’s open source, check out the repository here - https://github.com/monosidev/monosi

🎉 Congratulations, you've just set up and scheduled a data monitor on your Supabase instance. You can now add more monitors to other tables in your database. Find more information on how to use Monosi here.

Now that you’ve worked through an example using a public PostgreSQL instance, you can further extend this to your own data store. For more information, get started here.

Continuing to build out & stabilize Monosi (open source data observability) - https://github.com/monosidev/monosi

Observability: Monosi