We’re curious to learn about some of the common issues users face when working with data. In our Case Study series, we are highlighting projects and organisations who are working with the Frictionless Data specifications and tooling in interesting and innovative ways.

Zegami makes information more visual and accessible, enabling intuitive exploration, search and discovery of large data sets. Zegami combines the power of machine learning and human pattern recognition to reveal hidden insights and new perspectives.

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It provides a more powerful tool for visual data than what’s possible with spreadsheets or typical business intelligence tools. By presenting data within a single field of view, Zegami enables users to easily discover patterns and correlations. Facilitating new insights and discoveries that would otherwise not be possible.

metadatasearch metadata search on Zegami {: .caption}

For Zegami to shine, our users need to be able to easily import their data so they can get actionable insight with minimal fuss. In building an analytics platform we face the unique challenge of having to support a wide variety of data sources and formats. The challenge is compounded by the fact that the data we deal with is rarely clean.

At the onset, we also faced the challenge of how best to store and transmit data between our components and micro-services. In addition to an open, extensible and simple yet powerful data format, we wanted one that can preserve data types and formatting, and be parsed by all the client applications we use, which includes server-side applications, web clients and visualisation frameworks.

We first heard about messytables1 and of the data protocols site (currently Frictionless Data Specifications2) through a lightning talk at EuroSciPy 2015. This meant when we searched for various things around jsontableschema (now tableschema3), we landed on the Frictionless Data project.

We are currently using the specifications in the following ways:

  • We use tabulator.Stream4 to parse data on our back end.
  • We use schema infer from tableschema-py5 to store an extended json table schema to represent data structures in our system. We are also developing custom json parsers using json paths and the ijson library

In the coming days, We plan on using

  • datapackage-pipelines6 as a spec for the way we treat joins and multi-step data operations in our system
  • tabulator in a polyglot persistence scenario7 - storing data in both storage buckets and either elasticsearch8 or another column store like druid.io.


Moving forward it would be interesting to see tableschema and tabulator as a communication protocol over websockets. This would allow for a really smooth experience when using handsontable9 spreadsheets with a datapackage of some kind. A socket-to-socket version of datapackage-pipelines which runs on container orchestration systems would also be interesting. There are few protocols similar to datapackage-pipelines, such as Dask10 which, although similar, is not serialisable and therefor unsuitable for applications where front end communication is necessary or where the pipelines need to be used by non-coders.

We are also keen to know more about repositories around the world that use datapackages11 so that we can import the data and show users and owners of those repositories the benefits of browsing and visualising data in Zegami.

In terms of other potential use cases, it would be useful to create a python-based alternative to the dreamfactory API server12. wq.io is one example, but it is quite hard to use and a lighter version would be great. Perhaps CKAN13 datastore could be licensed in a more open way?

In terms of the next steps for us, we are currently working on a SaaS implementation of Zegami which will dramatically reduce the effort required in order to start working with Zegami. We are then planning on developing a series of APIs so developers can create their own data transformation pipelines. One of our developers, Andrew Stretton, will be running Frictionless Data sessions at PyData London14 on Tuesday, October 3 and PyCon UK15 on Friday, October 27.

  1. Tools for parsing messy tabular data: https://github.com/okfn/messytables

  2. Frictionless Data Specifications: https://specs.frictionlessdata.io/

  3. Table Schema: http://specs.frictionlessdata.io/json-table-schema/

  4. Tabulator: library for reading and writing tabular data https://github.com/frictionlessdata/tabulator-py

  5. Table Schema Python Library: https://github.com/frictionlessdata/tableschema-py

  6. Data Package Pipelines: https://github.com/frictionlessdata/datapackage-pipelines

  7. Polyglot Persistence: https://en.wikipedia.org/wiki/Polyglot_persistence

  8. Elastic Search: https://www.elastic.co/products/elasticsearch

  9. Handsontable: Javascript spreadsheet component for web apps: https://handsontable.com

  10. Dask Custom Graphs: http://dask.pydata.org/en/latest/custom-graphs.html

  11. Data Packages: http://frictionlessdata.io/data-packages/

  12. Dream Factory: https://www.dreamfactory.com/

  13. CKAN: Open Source Data Portal Platform: https://ckan.org

  14. PyData London, October 2017 Meetup: https://www.meetup.com/PyData-London-Meetup/events/243584161/

  15. PyCon UK 2017 Schedule: http://2017.pyconuk.org/schedule/

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