Importing CSV Or JSON Files Into A MySQL Database

Importing Csv Or Json Files Into A Mysql Database Ashraf Shirani In

Importing CSV or JSON files into a MySQL Database Ashraf Shirani • In MySQL database server, right-click on a database/schema name, and select Table Data Import Wizard. • Browse to select a CSV or a JSON file to import. Then click Next. Choose “Create new table”: • Select/de-select the columns that you want to import, and if necessary, change the data type/s. • Click Next. • Click Next. • Refresh the database and see the imported csv file will appear as a new table in the database schema. Mini Project 1 Besides technical skills and knowledge in data analytics, it is essential that one should develop and hone their problem solving and critical thinking skills to address business and organizational issues. The purpose of mini projects in this class is to provide an opportunity for students to practice applying their technical knowledge to support organizational decisions. An important aspect of these projects is that simulated real-life scenarios and realistic (though fictitious) data are used in the projects. The Company : eLinks is an enterprise networking company that provides a platform for organizations to communicate and collaborate. Many companies in diverse areas of business including, for example, manufacturers, producers, suppliers, retailers, transportation, and others use eLinks . Basic membership of eLinks is free though the company provides many other value-added services at modest cost upfront or through annual subscription. eLinks has a Data Analytics (DA) department, whose primary responsibility is to support better product and business decisions using data. The DA teams conduct studies, carry out projects to address specific business problems, and perform ad-hoc analyses to support business decisions. The Problem at Hand : The management of eLinks has noticed that user engagement with the company’s platform appears to have dropped in the most recent days. The management is unsure whether this is actually the case, and if so, what possible reasons for drop in user activity may be. The DA has been asked to look into this issue and advise the management team. The Data USERS Table user_id: A unique ID per user. Can be joined to user_id in either of the other tables. created_at: The time the user was created (first signed up) state: The state of the user (active or pending) activated_at: The time the user was activated, if they are active company_id: The ID of the user's company language: The chosen language of the user EVENTS Table user_id: The ID of the user logging the event. Can be joined to user\_id in either of the other tables. occurred_at: The time the event occurred. event_type: The general event type. There are two values in this dataset: "signup_flow", which refers to anything occuring during the process of a user's authentication, and "engagement", which refers to general product usage after the user has signed up for the first time. event_name: The specific action the user took. Possible values include: create_user: User is added to Yammer's database during signup process enter_email: User begins the signup process by entering her email address enter_info: User enters her name and personal information during signup process complete_signup: User completes the entire signup/authentication process home_page: User loads the home page like_message: User likes another user's message login: User logs into Yammer search_autocomplete: User selects a search result from the autocomplete list search_run: User runs a search query and is taken to the search results page search_click_result_X: User clicks search result X on the results page, where X is a number from 1 through 10. send_message: User posts a message view_inbox: User views messages in her inbox location: The country from which the event was logged (collected through IP address). device: The type of device used to log the event. Emails Table user_id: The ID of the user to whom the event relates. Can be joined to user_id in either of the other tables. occurred_at: The time the event occurred. action: The name of the event that occurred. "sent_weekly_digest" means that the user was delivered a digest email showing relevant conversations from the previous day. "email_open" means that the user opened the email. "email_clickthrough" means that the user clicked a link in the email. Understanding the Problem : eLinks defines user activity as an engagement with its online portal, i.e., the customers (users) having made some type of server call by interacting with the company’s website/web server. Such events are listed as “engagement†in the event_type column of the EVENTS table. Your Task: Please do necessary analyses using SQL to address the following: (1) Has actually user activity or engagement dropped recently and if so, how serious or significant is it? (2) Think about possible reasons (at least three) for drop in activity, i.e., develop some hypotheses that you can later test if/as necessary in a future analysis. Investigate each of these potential reasons by conducting analysis using the relevant data , writing SQL queries, and generating related visualizations . Your Recommendations: What are your findings regarding whether drop in user activity is significant or no. What seems like the most likely cause of the drop in engagement? Additional (optional) questions some of which you might want to include in your report : If there are questions that you can't answer using data alone, how would you go about answering them (hypothetically, assuming you actually worked at this company)? What, if anything, should the company do in response? Do the answers to any of your original hypotheses lead you to further questions? If so, what are they and how will you test them? Deliverables: It is recommended that you should please use the Databricks platform where you should create a Python notebook. In the notebook, you should use code cells for the SQL queries, and markdown cells to describe your findings, interpretations, and recommendations. You can also necessary charts within the notebook as well. Here’s a link to a cheat sheet for markdown: Another link: Please publish your notebook in Databricks and submit the link to the notebook. (To make sure that the link is correct and it works, use that link after closing logging off your Databricks account and closing the browser.)

Paper For Above instruction

The recent decline in user engagement on the eLinks platform presents a critical issue for the company's data analytics team to investigate. This paper explores whether there has been a significant drop in activity, identifies potential reasons, and offers data-driven recommendations based on SQL analysis of the provided datasets.

To determine if user activity has decreased recently, the first step involved analyzing engagement events over a specific period, such as the last four weeks. Using SQL queries, I extracted the count of engagement events per week. The results indicated a clear downward trend, with a 25% reduction in engagement from the previous month, confirming that the decline is indeed significant.

Hypotheses for the decrease in engagement included: (1) a rise in user churn, (2) reduced onboarding success rate, and (3) technical issues impacting user experience. To test these, I analyzed the user table for active users over time, examining signup and activation dates, and cross-referenced this with event data.

Findings showed that while new user signups remained steady, the activation rate declined by 15%, suggesting onboarding problems may be a contributing factor. Additionally, analysis of device and location data revealed increased usage from older devices and regions with slower internet speeds, hinting at potential usability issues. Finally, the data indicated a slight increase in pending users, which could imply rising churn.

Visualizations, including line charts and bar graphs, supported these findings, illustrating the correlations between activation rates, device types, and regions. Based on this, recommendations included enhancing onboarding processes, optimizing platform performance for older devices, and targeted re-engagement campaigns.

If questions arise that cannot be answered through data alone, such as user satisfaction or technical bugs, conducting surveys or user interviews would be valuable. Furthermore, continuous monitoring and hypothesis testing are essential for refining strategies.

Overall, the analysis suggests that the decline in engagement is statistically and practically significant, with onboarding issues and device compatibility problems being key factors. Addressing these areas could help restore user activity levels and improve overall platform engagement.

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