AWS Data Exchange

Customers working within data-driven tools such as business intelligence (BI) platforms, analytics platforms, or machine learning (ML) platforms often must find and procure third-party data from external sources. This is due to the lack of native access to third-party data. The time and resources required to prepare and integrate that data into a BI or analytics tool for use can take weeks or months, based on the size and frequency at which data is received.

In this lab, you will create a Python virtual environment and install the Discovery API Python SDK within the virtual environment. You will also execute Discovery API Search Listings API calls with a variety of search parameters.

In this lab, you will create a data set with the AWS Data Exchange for Amazon S3 delivery method.

On October 10, 2022, Amazon Web Services, Inc. launched the 2022 AWS Data Exchange for APIs Challenge and offered participants a chance to win up to $35,000 in prizes. The hackathon ran for six weeks and featured APIs from the following data providers: Gatehouse Maritime, IMDb, Inca Digital, Luminate, People Data Labs, PredictHQ, ReArc, SimilarWeb and Ursa Space.

In this three-part series, we demonstrate how to transform and prepare IMDb data to power out-of-catalog search for your media and entertainment use cases.

In this post, we discuss how to prepare IMDb data and load the data into Amazon Neptune for querying.

In this three-part series, we demonstrate how to transform and prepare IMDb data to power out-of-catalog search for your media and entertainment use cases.

In this post, we discuss the process of using Neptune to generate embeddings used to conduct our out-of-catalog search in Part 3.