A federated catalog for all of your data

The better an organization understands and uses its data, the better it is able to make decisions and discover new opportunities. Many organizations hold massive quantities of data, but it often gets stuck inside organizational silos where its importance is invisible, origins untracked, and existence unknown to those elsewhere in the organization who could improve or derive further value from it.

Magda is a data catalog system that provides a single place where all of your organization’s data can be catalogued, enriched, searched, tracked and prioritized - whether big or small, internally or externally sourced, available as files, databases or APIs. With Magda, your data analysts, scientists and engineers can easily find useful data with powerful discovery features, properly understand what they’re using thanks to metadata enhancement and authoring tools, and make data-informed decisions with confidence as a result of history tracking and duplication detection.

Let your data mingle!

Magda is designed around the concept of federation - providing a single view across all data of interest to a user, regardless of where the data is stored or where it was sourced from. Where other data catalogs are designed around their creators’ other data products or implement federation by simply copying external datasets internally, federating over many data sources of any format is at the core of how Magda works. The system is able to quickly crawl external data sources, track changes, make automatic enhancements and push notifications when changes occur, giving your data users a one-stop shop to discover all the data that’s available to them.

Don’t neglect your small data

Investment in data often focuses on extracting value big data - big, complex datasets that are already known to be of high value. This focus comes at the expense of small data - the myriad Excel, CSV and even PDF files that are critical to the operations of every organization, but unknown outside the teams and individuals that use them.

This results in squandered opportunities as small datasets go undiscovered by other teams who could make use of or combine them, fragmentation as files are shared and modified via untracked, ad-hoc methods, and waste as datasets are collected or acquired multiple times, often at extreme expense.

Magda is designed with the flexibility to work with all of an organisation’s data assets, big or small - it can be used as a catalog for big data in a data lake, an easily-searchable repository for an organization’s small data files, an aggregator for multiple external data sources, or all at once.

Features

Discovery

The easiest way to find a dataset is by searching for it, and Magda puts its search functionality front and centre. Magda was originally developed for the Australian government’s federal open data portal data.gov.au, providing a single place for Australia’s citizens, scientists, journalists and businesses to discover and access 80,000+ datasets, from linked data APIs to small Excel files.

When users search they expect the result to be the best result for the meaning of their query, not simply the one with the most keyword matches. Magda is able to return higher-quality datasets above lower-quality ones, understand synonyms and acronyms, as well as search by time or geospatial extent.

Federation

Magda is designed from the ground-up with the ability to pull data from many different sources into one easily searchable catalog in which all datasets are first-class citizens, regardless of where they came from. Magda can accept metadata from our easy-to-use cataloging process, existing Excel or CSV-based data inventories, existing metadata APIs such as CKAN or Data.json, or have data pushed to it from your systems via its REST API.

In Magda all data is first-class, regardless of its source. Data in Magda is combined into one search index, with history tracking and even webhook notifications when metadata records are changed.

Previews

Easily determine if a dataset is useful with charting, spatial preview with TerriaJS and automatic charting of tabular data.

Metadata enhancement

Authoring of high-quality metadata has historically been difficult and time-consuming, and as a result metadata around datasets is often poorly formatted or completely absent, making them difficult to search for and hard to understand once found.

Magda is able to automatically derive and enhance metadata, without the underlying data itself ever being transmitted to a Magda server. For datasets catalogued directly, our “Add Dataset” process is able to read and derive data from files directly in the browser, without the data itself ever having to leave the user’s machine, and for both internal and external datasets our minion framework is able check for broken links, normalize formats, calculate quality, determine the best means of visualisation and more. This framework for enhancement is open and extensible, allowing to build your own enhancement processes using any language that can be deployed as a docker container.

Open architecture

Magda is designed as a set of microservices that allow extension by simply adding more services into the mix. Extensions to collect data from different data sources or enhance metadata in new ways can be written in any language and added or removed from a running deployment with little downtime and no effect on upgrades of the core product.

Easy set up and upgrades

Magda uses Kubernetes and Helm to allow for simple installation and minimal downtime upgrades with a single step. Deploy it to the cloud, your on-premises setup or even your local machine with the same set of commands.

Federated authentication

Based on PassportJS, Magda’s authentication system is able to integrate with a wide and growing range of different providers. Currently supported are:

Under Development

We’re currently finishing off these features - you can see the full roadmap here.

A better way to manually catalog datasets

Authoring a quality dataset is hard - not only does it involve a lot of manual work, but it also requires a great deal of up-front knowledge and data literacy. We’re building a guided, opinionated and heavily automated publishing process into Magda that will result in an easier time for those who publish data, and higher metadata quality to make it easier to search and use datasets for data users downstream.

Reducing duplication

Often the use of ad-hoc sharing mechanisms such as email or USB disks results in multiple copies of a dataset being modified in parallel, and poor historical visibility of an organization’s data holdings leads to external data being bought multiple times by different teams. We’re adding features to automatically identify and mitigate duplication, without the need for the data to actually be stored on Magda itself.

Authorization: Share data with confidence

We’re adding an integrated, customizable authorization system into Magda based on Open Policy Agent, which will allow:

Work with us

We’re always looking to help more organizations use their data better with Magda!

If you’d like to become a co-creation partner, want our help getting up and running, or want to sponsor specific features, we’d love to talk to you! Please get in contact with us at contact@magda.io.

Magda is also completely open-source and can be used for free - to get it running, please see the instructions below. Don’t forget to let us know you’re using it!

Current Users

See it in action

A demo site exists at demo.dev.magda.io. This is hosted on pre-emptible instances and may go down for short periods, if so wait 5-10 minutes and it should come back up again. For an example of Magda in production, see data.gov.au.

Want to get it running yourself?

Try the latest version, or build and run from source

Latest Release

v0.0.53, released at 2019-04-02 03:20:18 UTC

Open Source

Magda is fully open source, licensed under the Apache License 2.0. Thanks to all our open source contributors so far:

We welcome new contributors too! please check out our Contributor’s Guide.

The project was started by CSIRO’s Data61 and Australia’s Department of Prime Minister and Cabinet. It’s progressing thanks to Data61, the Digital Transformation Agency, the Department of Agriculture, the Department of the Environment and Energy and CSIRO Land and Water.