Making data analytics easier for data scientists, developers, and DevOps
Collaboration tools in the open-source Trusted Analytics Platform (TAP) address the human side of data analytics.
Rooftop shingles (source: Lecreusois via Pixabay)
It used to be a lot harder to ingest, process, and analyze data. Technical advances in machine learning tools, the adoption of public and private clouds, and the use of optimized data center resources have vastly improved data workflows.
But there’s still work to be done. Collaboration between different data-using groups in an organization isn’t as easy as it could be. Along the path from gathering raw data to sharing a meaningful version of that data, roles can become somewhat siloed, introducing bottlenecks and roadblocks.
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The open source Trusted Analytics Platform (TAP) eliminates those silos in a way that serves data scientists, application developers, and DevOps engineers—individually and collectively. Recently, I sat down with Chuck Freedman, Chief Developer Advocate for TAP, and we talked about how TAP takes the advances that have been made from the chip set on up and provides:
- A platform that facilitates a faster time to market.
- An easier path to continuous delivery.
- Centralization of knowledge and assets.
Our full conversation is available in this embed:
During our discussion, we looked at TAP from three three distinct perspectives: data scientist, application developer, and DevOps engineer.
How TAP helps the data scientist
TAP can bring together data tools and frameworks data scientists are using today, from the many flavors of Spark to the emerging Flink platform. These all live in TAP’s marketplace, which not only makes access and discovery of a wide array of tools easy for import into TAP, but also gives you videos and tutorials to help with issues that data scientists encounter in your workflow.
More broadly, TAP brings data scientists into the same realm as application developers and DevOps engineers. Gathering these three groups together in the same platform eliminates time spent packaging up data for sharing, and it lets data scientists engage with the team to build better apps and visualizations.
How TAP helps the application developer
The core benefit of TAP for the application developer is that in-house data lives in the same cloud or datacenter where you’re building your application or visualization. If you’re using external data, APIs are available as well. All of your tools and data services, including InfluxDB and Redis, are available in the TAP marketplace. There’s also support for most languages, including Java, Node.js, and Python.
How TAP helps the DevOps engineer
As a DevOps engineer working within TAP, you’ll find yourself in an environment that is tested on both public and private clouds, such as AWS and OpenStack. Because TAP is open source, it includes the benefit of being able to share knowledge and questions through the TAP community. TAP also gives you an omniscient view of your world, from raw data ingestion to your fifth deployment. You can use a command line if you choose, but there’s also a GUI and dashboard that can help you collaborate with other team members.
Of course, a team that can’t collaborate also can’t produce. That’s why TAP’s aim is to make data analytics within and across teams easier to manage.
To learn more about TAP and become part of its community, visit community.trustedanalytics.org.
This post is part of a collaboration between O’Reilly and Intel. See our statement of editorial independence.