great_expectations
jitsu
| great_expectations | jitsu | |
|---|---|---|
| 15 | 13 | |
| 9,466 | 3,845 | |
| 0.9% | 1.0% | |
| 9.9 | 9.8 | |
| 5 days ago | 6 days ago | |
| Python | TypeScript | |
| Apache License 2.0 | MIT License |
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.
great_expectations
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Data Quality at Scale with Great Expectations, Spark, and Airflow on EMR
Great Expectations (GE) is an open-source data validation tool that helps ensure data quality.
- Looking for Unit Testing framework in Database Migration Process
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Soda Core (OSS) is now GA! So, why should you add checks to your data pipelines?
GE is arguably the most well known OSS alternative to Soda Core. The third option is deequ, originally developed and released in OSS by AWS. Our community has told us that Soda Core is different because it’s easy to get going and embed into data pipelines. And it also allows some of the check authoring work to be moved to other members of the data team. I'm sure there are also scenarios where Soda Core is not the best option. For example, when you only use Pandas dataframes or develop in Scala.
- Greatexpectations - Always know what to expect from your data.
- Greatexpectations – Always know what to expect from your data
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Package for drift detection
great_expectations: https://github.com/great-expectations/great_expectations
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[D] Do you use data engineering pipelines for real life projects?
For example I just found "Great Expectations" and "Kedro", "Flyte" and I was wondering at which point in time and project complexity should we choose one of these tools instead of the ancient cave man way?
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Data pipeline suggestions
Testing: GreatExpectations
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Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
- [P] Deepchecks: an open-source tool for high standards validations for ML models and data.
jitsu
- Jitsu
- Any examples of working activist, socialist, or community-organizing software?
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Lesser Known Features of ClickHouse
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.
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Reference Data Stack for Data-Driven Startups
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.
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Data pipeline suggestions
Ingestion / Extraction: Airbyte, Singer, Jitsu
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Where can I find free data engineering ( big data) projects online?
Ingestion / ETL: Airbyte, Singer, Jitsu Transformation: dbt Orchestration: Airflow, Dagster Testing: GreatExpectations Observability: Monosi Reverse ETL: Grouparoo, Castled Visualization: Lightdash, Superset
- Ask HN: Good open source alternatives to Google Analytics?
- Jitsu is a FOSS data integration platform that gathers events from several data sources (alternative to Segment)
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Launch HN: Jitsu (YC S20) – Open-Source Segment Alternative
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.
What are some alternatives?
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
kedro-great - The easiest way to integrate Kedro and Great Expectations
Snowplow - The enterprise-grade behavioral data engine (web, mobile, server-side, webhooks), running cloud-natively on AWS and GCP
deepchecks - Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
re_data - re_data - fix data issues before your users & CEO would discover them 😊
posthog-ios - PostHog iOS SDK
streamlit - Streamlit — A faster way to build and share data apps.
sqlpad - Web-based SQL editor. Legacy project in maintenance mode.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
superset - Apache Superset is a Data Visualization and Data Exploration Platform