dbt-utils
airbyte
dbt-utils | airbyte | |
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7 | 140 | |
1,213 | 14,296 | |
2.9% | 4.1% | |
6.2 | 10.0 | |
10 days ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dbt-utils
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Show HN: Nasty, a cross warehouse, type checked, unit testable analytics library
// To get around this, we can use the approach outlined by how dbt does ansi sql generate_series
// https://github.com/dbt-labs/dbt-utils/blob/main/macros/sql/generate_series.sql
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Anything one should know before going for self-hosted dbt?
I got bit by dbt-utils/deduplicate naively removing any row that contained a null in it recently, but fortunately there was a workaround for Databricks and a few other flavors of SQL.
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Managing SQL Tests
I'm used to utilising dbt and defining my tests there (along with dbt-utils or https://github.com/calogica/dbt-expectations): I simply add a list item to a column definition and can already define a great number of tests without having to copy code. I can even extend the pre-defined using generic tests. Writing custom tests also integrates nicely. Additionally it's very convenient to tag tests or define a severity. The learning curve for a business engineer is almost flat as long as they know some SQL.
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Dbt to acquire Transform to build out its semantic layer
My top three:
- Dev/stag/prod env check numbers before pushing to production.
- Unions between two sources that are not the same shape can be done without the headache. https://github.com/dbt-labs/dbt-utils#union_relations-source
- Macros for common case when statements.
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Analytics Stacks for Startups
Add tests: unit tests in SQL are still not really practical, but testing the data, before allowing users to see it, is possible. dbt has some basic tests like Non-NULL and so on. dbt_utils supports comparing data across tables. If you need more, there is Great Expectation and similar tools. dbt also supports writing SQL queries which output “bad” rows. Use this to, e.g. check a specific order against manually checked correct data. Tests give you confidence that your pipelines produce correct results: nothing is worse than waking up with a Slack message from your boss that the graphs look wrong… They are especially useful in case you have to refactor a data pipeline. Basically every query you would run during the QA phase of a change request has a high potential to become an automatic test.
- Why is Data Build Tool (DBT) is so popular? What are some other alternatives?
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Unit testing SQL in DBT
The equality test macro is also in the dbt-utils package from fishtown at https://github.com/fishtown-analytics/dbt-utils/blob/master/macros/schema_tests/equality.sql
airbyte
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Launch HN: Bracket (YC W22) – Two-Way Sync Between Salesforce and Postgres
I'l also give a shout-out to Airbyte (https://airbyte.com/), with which I've had some limited success with integrating Salesforce to a local database. The particular pull for Airbyte is that we can self-host the open source version, rather than pay Fivetran a significant sum to do this for us.
It's an immature tool, so I don't yet know that I can claim we've spent _less_ than Fivetran on the additional engineering and ops time, but it feels like it has potential to do so once stabilized.
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
- All the ways to capture changes in Postgres
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Airbyte API and Terraform Provider – available in open source
When it says "available in open source", is that under the main airbyte repo's licensing [1], hence primarily licensed under the Elastic License v2 and therefore not typically considered open source by many?
Airbyte has previous of advertising their offering as open source while not really being as per the OSD[2]. This has been raised with them previously but without response [3][4]. They've also been extending their use of ELv2, recently moving many of their existing MIT licensed connectors to be ELv2 [5].
[1] https://github.com/airbytehq/airbyte/blob/master/LICENSE
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Need help moving 16gb of mongodb data to tableau
As possible solution, I can suggest Airbyte(https://airbyte.com/). it's more performant than generic python script.
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Connecting data sources to Xata with Airbyte and Zapier integrations
Airbyte, an open-source data integration engine that offers hundreds of connectors with data warehouses and databases, has gained popularity for its seamless integration and data syncing capabilities. Xata's integration with Airbyte offers a streamlined data ingestion process from any Airbyte input source directly into your Xata database.
- Data replication from postgresql to MSSQL
- Testing
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Is it impossible to contribute to open source as a data engineer?
You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte
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airbyte VS cloudquery - a user suggested alternative
2 projects | 2 Jun 2023
What are some alternatives?
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
dagster - An orchestration platform for the development, production, and observation of data assets.
dbt-oracle - A dbt adapter for oracle db backend
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
nodejs-bigquery - Node.js client for Google Cloud BigQuery: A fast, economical and fully-managed enterprise data warehouse for large-scale data analytics.
meltano
streamlit - Streamlit — A faster way to build and share data apps.
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
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.