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Overview

Open Source Integration

The kglab package is mostly about integration. On the one hand, there are useful graph libraries, most of which don't share much common ground and can often be difficult to use together. One the other hand, there are the popular tools used for data science and data engineering, with expectation about how to repeat process, how to scale and leverage multi-cloud resources, etc.

Much of the role of kglab is to provide abstractions that make these integrations simpler, while fitting into the tools and processes that are expected by contemporary data teams in industry. The following figure shows a landscape diagram for how kglab fits into multiple technology stacks and related workflows:

Items shown in black have been implemented, while the items shown in blue are on our roadmap. We include use cases for most of what's implemented within the tutorial.

Just Enough Math, Edition 2

To be candid, kglab is partly a follow-up edition of Just Enough Math – which originally had the elevator pitch:

practical uses of advanced math for business execs (who probably didn't take +3 years of calculus) to understand big data use cases through hands-on coding experience plus case studies, histories of the key innovations and their innovators, and links to primary sources

JEM started as a book which – thanks to quick thinking by editor Ann Spencer – turned into a popular video+notebook series, followed by tutorials, and then a community focused on open source. Seven years later the field of data science has changed dramatically This time around, kglab starts as an open source Python library, with a notebook-based tutorial at its core, focused on a community and their business use cases.

The scope now is about graph-based data science, and perhaps someday this may spin-out a book or other learning materials.

How to use these materials

Following the JEM approach, throughout the tutorial you'll find a mix of topics: data science, business context, AI applications, data management, design, distributed systems – plus explorations of how to leverage relatively advanced math, where appropriate.

To addresses these topics, this documentation uses a particular structure, shown in the following figure:

To make these materials useful to a wide audience, we've provided multiple entry points, depending on what you need:

  • Introduce concepts, exploring the math behind the concepts
  • Point toward histories, primary sources, and other materials for context
  • Show use cases and linking to related case studies for grounding
  • Practice through hands-on coding, based on a progressive example
  • Clarify terminology with a glossary for shared definitions

Ideally, there should also be two other parts – stay tuned for both:

  • self-assessments for personal feedback
  • the coding examples show lead into a capstone project

In any case, the objective for these materials is to help people learn how leverage kglab effectively, gain confidence working with graph-based data science, plus have examples to repurpose for your own use cases.

Start at any point, whatever is most immediately useful for you. The material is hyper-linked together; it may be helpful to run JupyterLab for the coding examples in one browser tab, while reading this documentation in another browser tab.

Again, we're focused on a community and pay special attention to their business use cases. We're also eager to hear your feedback and suggestions for this open source project.


Last update: 2021-01-06