Based on what you now know about data integration applications, techniques, advantages, and challenges, discuss 2 probable future advancements in data integration along with the challenges data architects and organizations can face in their design, implementation, or effects of their use on consumers.

As the need and applications for data integration continue to grow, more and more organizations will be looking to data architects to provide solutions that can enhance their operational efficiency by providing ways to organize and make their data more available and useful. This assessment invites you to examine such a scenario and to devise viable solutions based on what you have learned in this course. A major electric car company, after acquiring a rival car company, is looking to your firm to help move the data it has just acquired from a traditional DBMS into the cloud, where its data is already stored. As the lead data architect, develop a proposal for moving and merging this data. In a 4.5- to 5-page proposal, detail the following: 1. Data: Describe the specific data and how it may differ from the existing company data.2. Methods and Technologies: Explain your proposed method for moving this data into the company’s cloud-based storage. Also, propose and explain an alternative…

Explore the challenges of data integration in a data warehousing environment, especially when dealing with disparate data sources. How are these challenges typically addressed?

The processes used to complete a data science project vary in their number of steps and functions, but in the end, the objectives and outcomes are similar. Research on the Web to identify what you believe to be the set (version) of steps that you'd use to complete a Data Science project. You should find versions with 5, 7, 9, 11, 12, or more steps. If you choose a particular set of steps, cite your source and explain why you would use that approach. If you wish, you can develop your own approach, drawing on the research, if you think there may be a better way. As you get deeper into Data Science, in subsequent courses, you should learn the validity of your approach. List the steps of your approach, cite its source, and provide an explanation of why this particular approach would work best for you.