API Design Patterns: a guide to better APIs

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API Design Patterns provides best practices for developing application programming interfaces (APIs) that are flexible, reliable, and easy for developers to use. It presents design patterns that have been proven at Google to create shared standards for API development. The book teaches readers to build adaptive APIs that meet customers' needs by drawing on lessons from experts in the API community like the author, JJ Geewax, a software engineer at Google focused on API design.

Software

API Design Best
Practices
With API Design Patterns. Take 42%
off by entering slgeewax3 into the
discount code box at checkout at
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The API development guide
As a developer, your job is to
make sure these APIs are stable,
reliable, and easy to use for
other developers.
API Design Patterns provides
you with a unique catalog of
design standards and best
practices to ensure your APIs
are flexible and user-friendly.
A simple API

Patterns are important
API design patterns are a useful
set of best practice specifications
and common solutions to API
design challenges.
Using accepted design patterns
creates a shared language
amongst developers who create
and consume APIs, which is
especially critical given the
explosion of mission-critical
public-facing web APIs

Learn the best practices
API Design Patterns draws on the
collected wisdom of the API
community, including the internal
developer knowledge base at
Google, laying out an innovative set
of design patterns for developing
both internal and public-facing APIs.
Learn to make adaptive APIs that
your customers will love using.

Learn from an API expert
JJ Geewax is a software engineer
at Google, focusing on Google
Cloud Platform and API design.
He is also the author of Google
Cloud Platform in Action.
Rest assured that your journey
of API discovery is being led by
an expert guide.

If you want to learn more
about the book, check it
out on liveBook here.
Take 42% off API Design Patterns by
entering slgeewax3 into the discount
code box at checkout at
manning.com.

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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