Next-Gen Data Discovery and Data Observability Platform

Website • LinkedIn • Slack • Documentation • Blog • Demo

Demo

Play with our demo app!

Introduction

ODD is an open-source data discovery and observability tool for data teams that helps to efficiently democratise data, power collaboration and reduce time on data discovery through modern user-friendly environment.

Key wins

• Shorten data discovery phase
• Have transparency on how and by whom the data is used
• Foster data culture by continuous compliance and data quality monitoring
• Accelerate data insights
• Know the sources of your dashboards and ad hoc reports
• Deprecate outdated objects responsibly by assessing and mitigating the risks
• :point_right: ODD Platform is a reference implementation of Open Data Discovery Spec

Features

Data Discovery and Observability

• Accumulate scattered data insights in Federated Data catalogue
• Gain observability through E2E Data objects Lineage
• Benefit from cutting-edge E2E microservices Lineage feature in tracking your data flow through the whole data landscape
• Be warned and alerted by Pipeline Monitoring tools
• Store your metadata
• Use ODD-native modern lightweight UI

ML First citizen

• Save results of your ML Experiments by automatically logging its parameters

Data Security & Compliance

• Manage Tags to prevent any abuse of the data
• Refer to Tags to stay compliant with data security standards
• Have full transparency on how and by whom the data is used

Data Quality

• Utilize advanced Data Quality Dashboard to gain insights into data quality metrics, trends, and issues across your datasets, enabling proactive data quality management
• Simplify DQ processes by using ODD with Great Expectations and DBT tests compatibility
• Integrate ODD with any custom DQ framework

Reference Data Management (Lookup Tables) - a part of Master Data Management (MDM)

• Manage and store reference data centrally, ensuring a single source of truth for key data elements like currency codes, country names, and product categories, etc.
• Easily integrate Lookup Tables with data pipelines and transformations, enhancing data enrichment and validation processes
• Support data governance and compliance efforts by maintaining accurate and consistent reference data across all data assets

Getting Started

Running as a separate container

Setting up PostgreSQL connection details, for example:

export POSTGRES_HOST=172.17.0.1
export POSTGRES_PORT=5432
export POSTGRES_DATABASE=postgres
export POSTGRES_USER=postgres
export POSTGRES_PASSWORD=mysecretpassword

Starting new instance of the platform:

docker run -d \
  --name odd-platform \
  -e SPRING_DATASOURCE_URL=jdbc:postgresql://${POSTGRES_HOST}:${POSTGRES_PORT}/${POSTGRES_DATABASE} \
  -e SPRING_DATASOURCE_USERNAME=${POSTGRES_USER} \
  -e SPRING_DATASOURCE_PASSWORD=${POSTGRES_PASSWORD} \
  -p 8080:8080 \
  ghcr.io/opendatadiscovery/odd-platform:latest

Go to localhost:8080 in case of local environment.

Running Locally with Docker Compose

docker-compose -f docker/demo.yaml up -d odd-platform-enricher

• :point_right: QUICKSTART

Deploying to Kubernetes with Helm Charts

• :point_right: QUICKSTART

Example configurations

There are various example configurations (via docker-compose) within docker/examples directory.

Contributing

Contributing to ODD Platform is very welcome. For basic contributions, all you need is being comfortable with GitHub and Git. The best ways to contribute are:

• Work on new adapters
• Work on documentation

To ensure equal and positive communication, we adhere to our Code of Conduct. Before starting any interactions with this repository, please read it and make sure to follow.

Please before contributing check out our Contributing Guide and issues labeled "good first issue":

Integrations

OpenDataDiscovery Platform offers comprehensive data source support to meet your needs.

Existing integrations
Proxy Adapter	Airflow	Airflow 2+
Apache Druid	Cassandra	Clickhouse
Elasticsearch	Hive	Kafka
Feast	MSSQL	MySQL
Microsoft ODBC	MongoDB	Neo4j
MariaDB	Oracle	PostgreSQL
Redshift	Snowflake	Vertica
Tarantool	Athena	DynamoDB
Glue	Kinesis	Quicksight
S3	SageMaker	SageMaker Featurestore
SQS	Delta lake S3	Tableau
Cube	SuperSet	PowerBI
Trino	Presto	DBT
Redash	Spark	MLflow
Kubeflow	Databricks Unity Catalog	Great Expectations
SQLite	Couchbase	Cockroachdb
Fivetran	Airbyte	Metabase
Mode	BigQuery	Singlestore
BigTable	GoogleCloudStorage	GoogleCloudStoraDeltaTables
Blob Storage	Duckdb	ScyllaDB
CKAN

ODD Data Model

ODD operates the following high-level types of entities:

1. Datasets (collections of data: tables, topics, files, feature groups)
2. Transformers (transformers of data: ETL or ML training jobs, experiments)
3. Data Consumers (data consumers: ML models or BI dashboards)
4. Data Quality Tests (data quality tests for datasets)
5. Data Inputs (sources of data)
6. Transformer Runs (executions of ETL or ML training jobs)
7. Quality Test Runs executions of data quality tests

For more information, please check specification.md.

Community Support

Join our community if you need help, want to chat or have any other questions for us:

• GitHub - Discussion forums and issues
• Slack - Join the conversation! Get all the latest updates and chat to the devs

Contacts

If you have any questions or ideas, please don't hesitate to drop a line to any of us.

Team Member	LinkedIn	GitHub
German Osin	LinkedIn	germanosin
Nikita Dementev	LinkedIn	DementevNikita
Damir Abdullin	LinkedIn	damirabdul
Alexey Kozyurov	LinkedIn	Leshe4ka
Pavel Makarichev	LinkedIn	vixtir
Roman Zabaluev	LinkedIn	Haarolean

License

ODD Platform uses the Apache 2.0 License.