1675736640
Marquez is an open source metadata service for the collection, aggregation, and visualization of a data ecosystem's metadata. It maintains the provenance of how datasets are consumed and produced, provides global visibility into job runtime and frequency of dataset access, centralization of dataset lifecycle management, and much more. Marquez was released and open sourced by WeWork.
Marquez is an LF AI & Data Foundation incubation project under active development, and we'd love your help!
Want to be added? Send a pull request our way!
Marquez provides a simple way to collect and view dataset, job, and run metadata using OpenLineage. The easiest way to get up and running is with Docker. From the base of the Marquez repository, run:
$ ./docker/up.sh
Tip: Use the
--build
flag to build images from source, and/or--seed
to start Marquez with sample lineage metadata. For a more complete example using the sample metadata, please follow our quickstart guide.
Note: Port 5000 is now reserved for MacOS. If running locally on MacOS, you can run
./docker/up.sh --api-port 9000
to configure the API to listen on port 9000 instead. Keep in mind that you will need to update the URLs below with the appropriate port number.
WEB UI
You can open http://localhost:3000 to begin exploring the Marquez Web UI. The UI enables you to discover dependencies between jobs and the datasets they produce and consume via the lineage graph, view run metadata of current and previous job runs, and much more!
HTTP API
The Marquez HTTP API listens on port 5000
for all calls and port 5001
for the admin interface. The admin interface exposes helpful endpoints like /healthcheck
and /metrics
. To verify the HTTP API server is running and listening on localhost
, browse to http://localhost:5001. To begin collecting lineage metadata as OpenLineage events, use the LineageAPI or an OpenLineage integration.
Note: By default, the HTTP API does not require any form of authentication or authorization.
GRAPHQL
To explore metadata via graphql, browse to http://localhost:5000/graphql-playground. The graphql endpoint is currently in beta and is located at http://localhost:5000/api/v1-beta/graphql.
We invite everyone to help us improve and keep documentation up to date. Documentation is maintained in this repository and can be found under docs/
.
Note: To begin collecting metadata with Marquez, follow our quickstart guide. Below you will find the steps to get up and running from source.
Marquez uses a multi-project structure and contains the following modules:
api
: core API used to collect metadataweb
: web UI used to view metadataclients
: clients that implement the HTTP APIchart
: helm chartNote: The
integrations
module was removed in0.21.0
, so please use an OpenLineage integration to collect lineage events easily.
Note: To connect to your running PostgreSQL instance, you will need the standard
psql
tool.
To build the entire project run:
./gradlew build
The executable can be found under api/build/libs/
To run Marquez, you will have to define marquez.yml
. The configuration file is passed to the application and used to specify your database connection. The configuration file creation steps are outlined below.
When creating your database using createdb
, we recommend calling it marquez
:
$ createdb marquez
marquez.yml
With your database created, you can now copy marquez.example.yml
:
$ cp marquez.example.yml marquez.yml
You will then need to set the following environment variables (we recommend adding them to your .bashrc
): POSTGRES_DB
, POSTGRES_USER
, and POSTGRES_PASSWORD
. The environment variables override the equivalent option in the configuration file.
By default, Marquez uses the following ports:
8080
is available for the HTTP API server.8081
is available for the admin interface.Note: All of the configuration settings in
marquez.yml
can be specified either in the configuration file or in an environment variable.
$ ./gradlew :api:runShadow
Marquez listens on port 8080
for all API calls and port 8081
for the admin interface. To verify the HTTP API server is running and listening on localhost
, browse to http://localhost:8081. We encourage you to familiarize yourself with the data model and APIs of Marquez. To run the web UI, please follow the steps outlined here.
Note: By default, the HTTP API does not require any form of authentication or authorization.
OpenLineage
: an open standard for metadata and lineage collectionSee CONTRIBUTING.md for more details about how to contribute.
If you discover a vulnerability in the project, please open an issue and attach the "security" label.
Author: MarquezProject
Source Code: https://github.com/MarquezProject/marquez
License: Apache-2.0 license
#machinelearning #metadata #data #discovery
1620466520
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1617988080
Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.
Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.
Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.
#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation
1620629020
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
1617955288
In the last two decades, many businesses have had to change their models as business operations continue to complicate. The major challenge companies face today is that a large amount of data is generated from multiple data sources. So, data analytics have introduced filters to various data sources to detect this problem. They need analytics and business intelligence to access all their data sources to make better business decisions.
It is obvious that the company needs this data to make decisions based on predicted market trends, market forecasts, customer requirements, future needs, etc. But how do you get all your company data in one place to make a proper decision? Data ingestion consolidates your data and stores it in one place.
#big data #data access #data ingestion #data collection #batch processing #data access layer #data integration platform #automate data collection
1675736640
Marquez is an open source metadata service for the collection, aggregation, and visualization of a data ecosystem's metadata. It maintains the provenance of how datasets are consumed and produced, provides global visibility into job runtime and frequency of dataset access, centralization of dataset lifecycle management, and much more. Marquez was released and open sourced by WeWork.
Marquez is an LF AI & Data Foundation incubation project under active development, and we'd love your help!
Want to be added? Send a pull request our way!
Marquez provides a simple way to collect and view dataset, job, and run metadata using OpenLineage. The easiest way to get up and running is with Docker. From the base of the Marquez repository, run:
$ ./docker/up.sh
Tip: Use the
--build
flag to build images from source, and/or--seed
to start Marquez with sample lineage metadata. For a more complete example using the sample metadata, please follow our quickstart guide.
Note: Port 5000 is now reserved for MacOS. If running locally on MacOS, you can run
./docker/up.sh --api-port 9000
to configure the API to listen on port 9000 instead. Keep in mind that you will need to update the URLs below with the appropriate port number.
WEB UI
You can open http://localhost:3000 to begin exploring the Marquez Web UI. The UI enables you to discover dependencies between jobs and the datasets they produce and consume via the lineage graph, view run metadata of current and previous job runs, and much more!
HTTP API
The Marquez HTTP API listens on port 5000
for all calls and port 5001
for the admin interface. The admin interface exposes helpful endpoints like /healthcheck
and /metrics
. To verify the HTTP API server is running and listening on localhost
, browse to http://localhost:5001. To begin collecting lineage metadata as OpenLineage events, use the LineageAPI or an OpenLineage integration.
Note: By default, the HTTP API does not require any form of authentication or authorization.
GRAPHQL
To explore metadata via graphql, browse to http://localhost:5000/graphql-playground. The graphql endpoint is currently in beta and is located at http://localhost:5000/api/v1-beta/graphql.
We invite everyone to help us improve and keep documentation up to date. Documentation is maintained in this repository and can be found under docs/
.
Note: To begin collecting metadata with Marquez, follow our quickstart guide. Below you will find the steps to get up and running from source.
Marquez uses a multi-project structure and contains the following modules:
api
: core API used to collect metadataweb
: web UI used to view metadataclients
: clients that implement the HTTP APIchart
: helm chartNote: The
integrations
module was removed in0.21.0
, so please use an OpenLineage integration to collect lineage events easily.
Note: To connect to your running PostgreSQL instance, you will need the standard
psql
tool.
To build the entire project run:
./gradlew build
The executable can be found under api/build/libs/
To run Marquez, you will have to define marquez.yml
. The configuration file is passed to the application and used to specify your database connection. The configuration file creation steps are outlined below.
When creating your database using createdb
, we recommend calling it marquez
:
$ createdb marquez
marquez.yml
With your database created, you can now copy marquez.example.yml
:
$ cp marquez.example.yml marquez.yml
You will then need to set the following environment variables (we recommend adding them to your .bashrc
): POSTGRES_DB
, POSTGRES_USER
, and POSTGRES_PASSWORD
. The environment variables override the equivalent option in the configuration file.
By default, Marquez uses the following ports:
8080
is available for the HTTP API server.8081
is available for the admin interface.Note: All of the configuration settings in
marquez.yml
can be specified either in the configuration file or in an environment variable.
$ ./gradlew :api:runShadow
Marquez listens on port 8080
for all API calls and port 8081
for the admin interface. To verify the HTTP API server is running and listening on localhost
, browse to http://localhost:8081. We encourage you to familiarize yourself with the data model and APIs of Marquez. To run the web UI, please follow the steps outlined here.
Note: By default, the HTTP API does not require any form of authentication or authorization.
OpenLineage
: an open standard for metadata and lineage collectionSee CONTRIBUTING.md for more details about how to contribute.
If you discover a vulnerability in the project, please open an issue and attach the "security" label.
Author: MarquezProject
Source Code: https://github.com/MarquezProject/marquez
License: Apache-2.0 license