Creating efficient data pipelines

Creating efficient data pipelines from our live environments with noSQL, JSON based data structures to an archive solution, ideally based on Microsoft Azure. Deliverable : Assessment of various Azure Solutions for achieving the desired archive and data warehousing solution (Data lake, Gen1 & Gen2, Cosmos DB, etc). If Azure is not familiar, an equivalent cloud solution could be considered but with most of the analytics and data science world moving onto Azure, we prefer to invest in a solution built on top of Azure. This assessment would consider cost, performance and other factors important for a real solution. Data pipelines allowing archive of live data with refresh rate of 12 hours at minimum. Archive solution ideally would be designed and implemented using the best Microsoft Azure solution from all the available products. Archive database, design and ideally implementation.

Assessment of available cloud based products for building a robust archiving solution

ABOUT COMPANY We are active in multiple industries and fields from logistics to data science and analytics. As a freshly founded company who has quickly grown to be active all across Canada and overseas we present a variety of opportunities which students can take part in and experience being in the fast pace and exciting technology arena while gaining knowledge and skills. We value creativity, problem solving and a genuine passion for learning. PROJECT SCOPE Creating efficient data pipelines from our live environments with noSQL, JSON based data structures to an archive solution, ideally based on Microsoft Azure. Deliverable : Assessment of various Azure Solutions for achieving the desired archive and data warehousing solution (Data lake, Gen1 & Gen2, Cosmos DB, etc). If Azure is not familiar, an equivalent cloud solution could be considered but with most of the analytics and data science world moving onto Azure, we prefer to invest in a solution built on top of Azure. This assessment would consider cost, performance and other factors important for a real solution. Data pipelines allowing archive of live data with refresh rate of 12 hours at minimum. Archive solution ideally would be designed and implemented using the best Microsoft Azure solution from all the available products. Archive database, design and ideally implementation.

Blockchain Development

About Shyftbase Inc. We are active in multiple industries and fields from logistics to data science and analytics. As a freshly founded company who has quickly grown to be active all across Canada and overseas we present a variety of opportunities which students can take part in and experience being in the fast pace and exciting technology arena while gaining knowledge and skills. We value creativity, problem solving and a genuine passion for learning as well as diversity. We come from all sort of backgrounds and welcome any individual who strives to have an impact on our daily life. Project Description Shyftbase blockchains brings transparency & trust to the supply chain. The goal of this project is to assess various options for developing blockchain-powered products based on the available cloud solutions such as Microsoft Azure Blockchain, IBM Fabric, AWS QLDB & Amazon managed blockchain. Deliverables A thorough assessment of the cloud solutions for developing blockchains An execution plan for developing one product based on one of the solutions above Minimum viable product based on the finding of the above assessment Support We will be working with the learners through the course of this project to ensure a desired level of communications is maintained. Students will have direct access to our team and can discuss issues. We will provide access to products where access is needed for developing and testing. One or two meetings a week, each with ~ 30-40 minutes length is suggested during which all related issues can be discussed.

Predictive Models for Forecasting Profitability Of Operations

Predictive Models for Forecasting Profitability Of Operations Deliverable A forecasting model for forecasting profitability of daily operations based on previous historical data. We will provide a customer's data from last October. Features included would be, data, order#, driver, product field (text, unstructured), revenue, fees & cost, driven km and possibly a few additional fields subject to further discussion. Identifying additional features beneficial in enhancing the accuracy of the model. Depending on the timeline, iterations of the model and improvements. Testing on new data and validation.