这是一篇关于数据建模和编写分析的SQL代写
In this assignment, you will practice Data Modeling and writing analytical SQL
statements:
- Part 1: Get started with Google BigQuery and write analytical SQL queries on Google Public datasets.
- Part 2: Create an Entity Relationship Diagram (ERD) for a set of 4 tables already defifined on Google BigQuery.
To begin this assignment
- Please download the HW #1 Assignment Template from the HW #1 folder in Blackboard. You must use the template to submit this homework assignment.
Homework Questions
Part 1
- If you have not already done so, follow the tutorial on Blackboard “Getting Started with Google BigQuery” to create a new project in Google BigQuery.
Once complete, take a screenshot of the results of any query you write. For example:2. Now, we will move onto Google’s Public Datasets in BigQuery. To do so, type “bigquery-public-data” into the “Explorer” search bar on the left-hand side of your BigQuery window and click enter. You may have to click on the blue highlighted text below the search bar “expand search to all projects” to see results. Example below:
Once you can see the “bigquery-public-data” projects, click on the grey triangle to expand the project and view all datasets. Example below:Scroll down and expand the dataset labeled “new_york_trees”. Then, click on the table “tree_census_2015”. Example below:
Finally, click on the 3 dots to the right of the “tree_census_2015” table and click on “Query”. Then, complete the query with a column name (such as “spc_common”) and click on the blue “Run” button. Examples below:Take a screenshot of your results and post them as the answer to this question.
- Continuing to use the “new_york_trees” dataset, write a query to fifind the top 5 most common trees in the “tree_census_2015” table. More specififically, you are looking for the top 5 most common “spc_common” in the table.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset, write a query to fifind the average tree diameter of trees in “Good” health by Borough in the “tree_census_2015” table.For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree health is in column “health”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset and the “tree_census_2015” table, write a query to fifind the common name of the tree with the largest tree diameter in the Borough of “Brooklyn”.
For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree common name is in column “spc_common”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset and the “tree_census_2015” table, write a query to determine which “curb_loc” has the largest average “tree_dbh”.
For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree common name is in column “spc_common”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset and the “tree_census_2015” table, write a query to determine the zip code with the most trees in “Good” health.
For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree common name is in column “spc_common”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset and the “tree_census_2015” table, write a query to help determine if “guards” improve the trees’ “health”. Be creative with this query, and explain your answer.For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree common name is in column “spc_common”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
- Continuing to use the “new_york_trees” dataset and the “tree_census_2015” table, write a query to determine the most common “user_type” for trees that are “London planetree”.
For more details, in the “tree_census_2015” table the tree diameter is in column “tree_dbh”, tree common name is in column “spc_common”, and Boroughs are in column “boroname”.
Paste a screenshot of your query and query results as your answer.
10.Now, we will move onto a difffferent Google Public Dataset. Find the “chicago_taxi_trips” dataset in the “bigquery-public-data” project.
In the table “taxi_trips”, write a query to fifind the average tip left by taxi riders that paid with a “Credit Card” for rides that were longer than 15 minutes.
For more details, the tip is in the “tips” column, the payment type is in the “payment_type” column, and the trip duration is in the “trip_seconds” column.
Paste a screenshot of your query and query results as your answer.
11.Still using the “taxi_trips” table in the “chicago_taxi_trips” dataset,Write a query to fifind the payment type that resulted in the largest average tip for rides that were longer than 10 minutes and between 5 and 10 miles.
For more details, the tip is in the “tips” column, the payment type is in the “payment_type” column, the trip duration is in the “trip_seconds” column, and the trip distance is in the “trip_miles” column.
Paste a screenshot of your query and query results as your answer.
12.Still using the “taxi_trips” table in the “chicago_taxi_trips” dataset,Use a SQL query to fifind the most expensive taxi “company”. You choose how to defifine “expensive”. Please paste a screenshot of your query, your query results, and your explanation of “expensive” as your answer.
13.Now, we will move onto a difffferent Google Public Dataset. Find the “stackoverflflow” dataset in the “bigquery-public-data” project.
Write a SQL query to fifind the “title”, “tags”, “view_count”, and “score” of the 5 posts with the highest “favorite_count” in the table “stackoverflflow_posts”.
Paste a screenshot of your query and query results as your answer.
14.Continue in the “stackoverflflow” dataset and “stackoverflflow_posts” table in the “bigquery-public-data” project.
Write a SQL query to fifind the titles of the 10 most viewed posts about BigQuery.
Hint: leverage the LIKE operator.
Paste a screenshot of your query and query results as your answer.
15.Continue in the “stackoverflflow” dataset in the “bigquery-public-data” project.
Write a SQL query that joins the “stackoverflflow_posts” and “users” tables to return the “title”, “view_count”, “owner_display_name”, and “reputation” of the 10 titles with the most comments (“comment_count”). Please add a WHERE clause to fifilter out NULLs in the “title” column.
Hint: “reputation” is from the “users” table.
Paste a screenshot of your query and query results as your answer.
Part 2
16.Look at the following 3 tables in the “stackoverflflow” datasets:
- stackoverflflow_posts● users
- badges
Use Lucidchart to create an Entity Relationship Diagram (ERD) of the above 3 tables. Please include Primary and Foreign Key information for each Entity, and Data Types of each attribute Paste a picture of your ERD as your answer.
Additional Notes:
- This is an individual assignment.
- Assemble all the responses to the questions in one MS Word or Google Docs document.
- Do not use MS Word to write your queries as MS Word will make “smart quotes” out of your text strings and SQL will not understand these.
- If the query results would take up more than 1 page, just copy the fifirst dozen rows or enough to fifill one page. Make a note of the total number of records in the full result
- Use tools like Snipping Toolon Windows for example to snip exactly what is needed to answer the question. Please do not clip the entire window, only what is needed to answer the question.
