Wizualne budowanie aplikacji na Sparku przy pomocy narzędzia Seahorse
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Seahorse is a collaborative platform designed to enhance Apache Spark's accessibility for non-technical users and improve the development of Spark applications. The document outlines its evolution since its first release in February 2016, highlighting features such as R and Scala support, custom cluster connectivity, and reporting capabilities. It also includes information on how to access Seahorse and participate in its feedback process.
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Wizualne budowanie aplikacji na Sparku przy pomocy narzędzia Seahorse
- 1. ● Bring Apache Spark capabilities closer to non code-savvy users ○ interactive Big Data analytics ○ speed up building Apache Spark applications ● Provide collaboration platform for data scientists and software engineers Seahorse Motivations
- 3. ● Development since mid 2015 ● First release in Feb 16 ● 3 releases so far ● Shown on multiple conferences: ○ Strata (NYC, London) ○ Spark Summit (SF, Amsterdam) ○ Hadoop Summit (San Jose, Dublin) ○ AI World (SF) ○ ODSC (London) ● Included in platforms: ○ IBM’s Big Data University ○ Intel’s Trusted Analytics Platform ● Group of early adopters Seahorse Timeline
- 4. Seahorse 1.3 (released in Sep 16): ● R support: build custom operations in R and use R notebooks within Seahorse ● Custom cluster connectivity: run Seahorse in Client Mode and connect to any cluster ● Spark 2.0 Seahorse Roadmap Highlights Seahorse 1.4 (Jan 17): ● Scala SDK: create custom operations in Scala and use them in multiple workflows from the operation palette ● Workflow scheduler ● Email reports: generate custom visual reports using Notebooks and get results sent via email
- 5. 1. Go to deepsense.io -> Seahorse -> Standalone 2. Fill out a short form and choose between: a. Docker (set of containers with Docker Compose) b. Vagrant (virtual machine image) 3. See examples and docs at seahorse.deepsense.io 4. Post feedback to our board or directly to [email protected] How to Get Seahorse