Welcome to the technical user journey 

A technical user in DataBench is someone that would like to search for big data benchmarks to test some specific big data tools, apps, or Machine Learning methods. There are a number of existing benchmarks out there, but unless you are an expert on benchmarking (and even so), it is hard to decide which is the best benchmark, or even if there are benchmarks that suit your needs.

You can browse and search in the benchmark catalogue from the Toolbox without registering, but if you want to have full access to DataBench resources you should register to the Toolbox. It is easy and painless.

Beginner

User journey

As a beginner, you have a set of Knowledge Nuggets (knowledge pieces) to understand what big data benchmarking is and what it can do for you. We recommend you click on the FAQs link in the main page or type “beginner” in the search box and enjoy!

Searching for existing technical benchmarks: Users have the possibility of searching in several ways:

  • Search box in the top right corner of the Toolbox. This box allows you to introduce any of the metadata fields.
  • Browse the entire catalogue . A user can search for benchmarks guided by the  BDVA Reference model  just by clicking on any of the boxes.
  • Guided search by selecting some of the most used metadata fields

Once located, you could select one technical benchmark and navigate to their own page to look for more content. Benchmarks are marked with metadata indicating their main features.

You may be interested in looking at different architectural blueprints. Type “blueprint” in the search box to find them.

You can also have a look to the Knowledge Nuggets Catalogue to understand the context

Advanced

User journey

 As an advanced technical user, you are supposed to have experience setting and execution big data benchmarks.

 We recommend you type “advanced” or “intermediate” in the search box.

It is important to understand the DataBench Framework and it relation to the BDV Reference Model , and some of the elements we have generated to define the main 4 steps of a Generic Data Pipeline and the Generic Big Data Analytics Blueprint. This links will provide you an overview of the main aspects related to the DataBench Framework and how you can make use of it for searching benchmarking information and potentially map your own technology to existing efforts in the big data and AI communities.

Other technical user journeys an advanced user might be interested in:

If you would like to suggest new content or benchmarks to the Toolbox you can do the following:

The DataBench Toolbox is open to the community. If you would like to suggest new content or benchmarks to the Toolbox you can do the following:

  • Suggest a new benchmark or update an existing one: Use the Suggest Benchmark option under the Benchmark menu. A form to provide information about the benchmark will be prompted. If you would like to update an existing benchmark, please explain the update and provide supporting info (i.e. links to existing web resources and papers).
  • Suggest a new big data/AI pipeline: Use the Suggest blueprint/pipeline option under the Knowledge Nuggets menu. If you are interested to contribute to the efforts in the big data community to map your big data/AI architecture to existing pipelines and blueprints based on standardization efforts (i.e. the BDV Reference Model), you can use this option to report it and gain visibility. Examples of how other have done so can be found in this mapping from the Telecom industry.
  • Suggest a new Knowledge Nugget: Use the Suggest Knowledge Nugget option under the Knowledge Nuggets menu. You will be prompted with a form to make your suggestion. Note that this is intended to provide knowledge about the use of big data technologies and AI, so any piece of information, examples, best practices, ways to derive metrics or KPIs, or anything related to benchmarking might be of interest to the community.

But there is more. Eventually, the tool will provide further technical and business insights based on previous knowledge, best practices from existing projects, etc.