Sprngy Demo Part1

This document details steps to setup new applications using Sprngy Admin UI, defining models and running workloads.

Demo Case: Finance

Example 1: Assess the Risk of a Loan

Objective - Determine whether a loan will be approved or not by the issuing bank

Overview

From source:

“The bank wants to improve their services. For instance, the bank managers have only vague idea, who is a good client (whom to offer some additional services) and who is a bad client (whom to watch carefully to minimize the bank loses). Fortunately, the bank stores data about their clients, the accounts (transactions within several months), the loans already granted, the credit cards issued The bank managers hope to improve their understanding of customers and seek specific actions to improve services. A mere application of a discovery tool will not be convincing for them.

The data about the clients and their accounts consist of following relations:

• relation account - each record describes static characteristics of an account,

• relation client - each record describes characteristics of a client,

• relation disposition - each record relates together a client with an account i.e. this relation describes the rights of clients to operate accounts,

• relation permanent order - each record describes characteristics of a payment order,

• relation transaction - each record describes one transaction on an account,

• relation loan - each record describes a loan granted for a given account,

• relation credit card - each record describes a credit card issued to an account,

• relation demographic data - each record describes demographic characteristics of a district.

Each account has both static characteristics (e.g. date of creation, address of the branch) given in relation "account" and dynamic characteristics (e.g. payments debited or credited, balances) given in relations "permanent order" and "transaction". Relation "client" describes characteristics of persons who can manipulate with the accounts. One client can have more accounts, more clients can manipulate with single account; clients and accounts are related together in relation "disposition". Relations "loan" and "credit card" describe some services which the bank offers to its clients; more credit cards can be issued to an account, at most one loan can be granted for an account. Relation "demographic data" gives some publicly available information about the districts (e.g. the unemployment rate); additional information about the clients can be deduced from this.”

Classification:

The file structure is going to look like this:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the Application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. Make sure to indicate District as the parent of Account and Client, Account as the parent of Loan, Order, Transaction, Display, and Display as the parent of Card.

We will begin first with the District Dataset:

Step 2: Meta Model, Ingest Model, and Workloads

We are going to use the Bulk Upload Meta feature to create our meta model for the District dataset. In the AdminUI menu , under the Meta Model dropdown, select the Bulk Upload Meta option. Select the Loan Application module and District entity, and import the meta model CSV file into the bulk upload. Once you hit submit, this will automatically generate the meta model for the District dataset.

Next, similar to meta model you can bulk upload ingest model in AdminUI. Or create ingest model by selecting processors. You can refer to this page to understand what each of the processors do.

Once we submit the Ingest Model, we can run the workloads under the Workload Management/Run Workloads page.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the LOANAPPLICATION/District/BDL/Fact directory in HDFS

Step 3: Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Repeating with the other Datasets

We will repeat the steps above with the remaining datasets. Note that there will be some small differences because of the parent-child relationship between the datasets.

The next dataset we will be working with is the Accounts dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Upload the data to SDL/Land.

account <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Account/account.asc',
                    sep = ';',
                    stringsAsFactors = FALSE) %>%
  rename_with(tolower) %>%
  dataops.appenddata(loanapplication.account.landdb(), 'account')

After uploading to SDL/Land, go to the Bulk Upload Meta page in AdminUI. Select the Account entity option, and upload the Meta Model csv file for account into the Bulk Upload. After it finishes uploading, the next step will be defining the ingest model.

The Ingest Model for Account will be slightly different because we have to define the Nested and Orphan processors for the parent-child quality of this dataset.

After creating the Ingest Model, run the SDL-FDL and FDL-BDL workloads, checking the respective directories to make sure that both workloads ran correctly.

Creating Data Table In Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal

The process for the other datasets will be similar to that of the Accounts dataset; the only difference will be the changes we make to the code needed to prepare our data for upload.

Client:

client <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Client/client.asc',
                   sep = ';',
                   stringsAsFactors = F) %>%
  rename_with(tolower) %>%
  mutate(as_of_date = as.character(Sys.Date()))

client %>%
  dataops.overwritedata(loanapplication.client.landdb(), 'client')

Loan:

loan <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Loan/loan.asc',
                  sep = ';',
                  stringsAsFactors = FALSE) %>%
  rename_with(tolower) %>%
  dplyr::rename(as_of_date = date) 

loan$as_of_date <- as.character(as.Date(as.character(loan$as_of_date), format = "%y%m%d"))

loan %>% 
  dataops.overwritedata(loanapplication.loan.landdb(),
                        'loan')

Order:

order <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Order/order.asc',
                        sep = ';',
                        stringsAsFactors = FALSE) %>% 
  dplyr::mutate(as_of_date = as.character(Sys.Date())) 

order %>% 
  dataops.overwritedata(loanapplication.order.landdb(),
                        'order')

Transaction:

transaction <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Transaction/trans.asc',
                     sep = ';',
                     stringsAsFactors = FALSE) %>% 
  dplyr::rename(as_of_date = date) %>% 
  dplyr::rename(transaction_type = type)
  
transaction$amount <- as.integer(transaction$amount)
transaction$balance <- as.integer(transaction$balance)  

transaction$as_of_date <- as.character(as.Date(as.character(transaction$as_of_date), format = "%y%m%d"))

nrow(transaction)
transaction[1:50000,] %>%   
dataops.overwritedata(loanapplication.transaction.landdb(),
                        'transaction')

Display:

display <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Display/disp.asc',
                    sep = ';',
                    stringsAsFactors = FALSE) %>%
  rename_with(tolower) %>%
  dplyr::mutate(as_of_date = as.character(Sys.Date())) %>%
  dataops.overwritedata(loanapplication.display.landdb(), 'display')

Card:

card <- read.csv('/home/bapdevvm/Downloads/LoanApplication/Card/card.asc',
                 sep = ';',
                 stringsAsFactors = FALSE) %>% 
  dplyr::rename(card_type = type) %>% 
  dplyr::mutate(as_of_date = as.character(Sys.Date())) 

card %>% 
  dataops.overwritedata(loanapplication.card.landdb(),
                        'card')

After uploading data to SDL/Land, follow the same steps as the Account dataset to create the Meta Model and Ingest Model, run the workloads, and create the Hive SQL statements. Once you finish this for all the data sets, we can now bring our data into Superset and create the visual models.

Step 4: Importing Dataset into Superset

After adding the datasets, you can select any one of them to bring up the Charts page. Here, you can pick which type of visualization you would like to create and the parameters for that visualization. Below are three examples of charts you can make in Superset that show the correlations between the various districts, regional statistics, loan amounts and durations, and the status of the loans.

Demo Case: Sports

Example 1: Evaluating NBA players' performances relative to their salaries

Objective - Build the most efficient team while constraining to a team’s cap space

Overview

With the NBA’s increased emphasis on data analytics, team managers have access to numerous metrics that show a player’s offensive and defensive efficiency. One such metric is FiveThirtyEight’s RAPTOR method, which highlights a player’s net efficiency and wins above replacement (WAR). In 2022, the top five players in Total RAPTOR were as following:

While it would be a manager’s dream to have a starting lineup like this, their combined salaries exceeds the cap space of $122M, leaving the team with both a hefty luxury tax bill and no bench players. The goal of this application is to build a full NBA team that gets the most value per dollar spent, according to the RAPTOR metric.

Classification:

Before defining the application in the system, it is useful to identify key entities and classify the application.

Key Entities:

The file structure is going to look like this:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Explanation of the classification: <>

Normalization: <V Structure/Star Model/Snowflake and its explanation>

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. Make sure to indicate Team as the parent of Players, and Players as the parent of Salaries and Stats.

We will begin first with the Teams dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too.

nba_teams <- read.csv("<nba_teams_file_path>", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
nba_teams$as_of_date = as.character(as.Date(Sys.Date()))

Next, we upload the data to SDL/Land using the dataops.appenddata() function:

dataops.appenddata(nba_teams, nbastatistics.teams.landdb(), 'teamsDataSpark')

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

Next, create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL. These are the SDL-FDL processors we select for our Teams data. You can refer to this page to understand what each of the processors do.

We then define the processors to use from FDL to BDL.

Once we submit the Ingest Model, we can run the workloads under the Batch Management/Run Workloads page.

Step 3: Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Repeating with the other Datasets

We will repeat the steps above with the three remaining datasets. Note that there will be some small differences because of the parent-child relationship between the datasets.

The next dataset we will be working with is the players dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too.

nba_players <- read.csv("<nba_players_file_path>", stringsAsFactors = F) %>%
  na.omit() %>%
  rename(team = Current.Team) %>%
  rename_with(tolower)
nba_players$as_of_date = as.character(as.Date(Sys.Date()))

Notice that we are renaming the Current Team column to team; this is to keep it consistent with the team column in the Teams dataset, which is necessary for the nesting processor we will use later. We are also removing all empty rows using na.omit(), since the dataset does contain some blank rows.

Before we upload the data to SDL, we still have to clean some of the data up. One discrepancy that exists between the teams dataset and players dataset is how both of them write the team ‘Philadelphia 76ers’. While the teams dataset writes the name ‘Philadelphia 76ers’, the players dataset writes it as ‘Philadelphia Sixers’. We have to fix this so that both team names match when we nest the players dataset to the team dataset.

nba_players$team[nba_players$team == "Philadelphia Sixers"] <- "Philadelphia 76ers"

Additionally, there could be differences in how the players, salaries, and statistics datasets write players' names. For example, one dataset could have the name ‘CJ McCollum’, while another could write the same name as ‘C.J. McCollum.’ Similarly, one dataset could contain the name ‘Jaren Jackson, Jr.’ while another writes it as ‘Jaren Jackson Jr’. The simplest way to fix this naming issue is to remove all periods and commas from the player_name column.

nba_players$player_name <- stringr::str_remove_all(nba_players$player_name, "[.,]")

Now, we are ready to upload data to SDL/Land:

dataops.appenddata(nba_players, nbastatistics.players.landdb(), 'playersDataSpark')

Meta Model, Ingest Model, and Workloads

The Ingest Model will be similar to the one we made for the team’s dataset, the only difference being we define two extra processors for the nested quality of the players dataset.

After creating the Ingest Model, run the SDL-FDL and FDL-BDL workloads, checking the respective directories to make sure that both workloads ran correctly.

Creating Data Table In Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal

Salaries Dataset

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too. We are going to change the unit for salary to $M, so we can rename the salary column to salary_in_millions, and for each element in that column, divide by 1000000. As we did in the players dataset, we are removing all periods and commas from the player_name column.

nba_salaries <- read.csv("/home/bapdevvm/Downloads/NBAdata/nba_salaries.csv", stringsAsFactors = F) %>%
  na.omit() %>%
  rename(salary_in_millions = salary) %>%
  rename_with(tolower)
nba_salaries$as_of_date = as.character(as.Date(Sys.Date()))
nba_salaries$salary_in_millions = nba_salaries$salary_in_millions / 1000000
nba_salaries$player_name <- stringr::str_remove_all(nba_salaries$player_name, "[.,]")

Upload the data to SDL/Land:

dataops.appenddata(nba_salaries, nbastatistics.salaries.landdb(), 'salariessDataSpark')

Meta Model, Ingest Model, and Workloads

The first step in creating the Meta Model for Salaries will be the same as that of Players and Teams - inputting column names and types. Because Salaries is nested under Players, we will have to indicate this by editing the player_name entity in the meta model and adding a parent reference to the FDL/Stage directory of the players.

After creating the meta model, we can create the ingest model for the salaries dataset. Refer to the Players dataset ingest model, as the processors for Salaries ingest model will be the same as that of Players'.

Once the ingest model is created, run both the workloads for the Salaries dataset, ensuring that they ran correctly by checking HDFS.

Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal

Statistics Dataset

Read the CSV file into a dataframe, rename the column names in lower case, add an as_of_date column, and remove all periods and commas from player names.

nba_statistics <- read.csv("<nba_statistics_file_path>", stringsAsFactors = F) %>%
  rename_with(tolower)
nba_statistics$as_of_date = as.character(as.Date(Sys.Date()))
nba_statistics$player_name <- stringr::str_remove_all(nba_statistics$player_name, "[.,]")

We also want to filter only the players who played for at least 700 minutes in the season, ensuring us a large enough sample size for our results to be accurate.

nba_statistics = nba_statistics %>%
  dataops.rdo.filterbygreaterthan("mp", 700)

Upload data to SDL/Land:

dataops.appenddata(nba_statistics, nbastatistics.statistics.landdb(), 'statisticsDataSpark')

Meta Model, Ingest Model, and Workloads

In the meta model, input the column names and data types. The statistics data set also shares the player_name column with the players data set, so we can nest those two together by editing the player_name entity and adding parent reference to FDL/Stage directory of the players dataset (see Salaries meta model for reference).

After creating the meta model, we can create the ingest model for the statistics data set.

After creating the ingest model, run the SDL-FDL and FDL-BDL workloads. Check FDL/Stage and BDL/Fact to make sure your workloads ran correctly.

Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal

Step 4: Importing Dataset into Superset

After adding the datasets, you can select any one of them to bring up the Charts page. Here, you can pick which type of visualization you would like to create and the parameters for that visualization. Below are two examples of charts you can make in Superset that show individual performance relative to salary and each team’s total offensive and defensive efficiency.

Example 2- Data Analysis of IPL 2022 match dataset.

Objective: Analyzing the IPL 2022 match by match dataset.

Overview: This is the IPL 2022 match by match dataset which includes information like match details, team details, winning team name, winning margin, player of the match etc.

IPL dataset:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure.

We will begin with the Match dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too.

match_data <- read.csv("<match_file_path>", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
match_data$as_of_date = as.character(as.Date(Sys.Date()))

Next, we upload the data to SDL/Land using the dataops.appenddata() function:

dataops.appenddata(match_data, ipl.match.landdb(), 'matchDataSpark')

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

This is how our meta model looks after adding all the columns with their respective datatypes. Note that you do not add the as_of_date column, as that will be added automatically.

Next, create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL. These are the SDL-FDL processors we select for our Teams data. You can refer to this page to understand what each of the processors do.

We then define the processors to use from FDL to BDL.

Once we submit the Ingest Model, we can run the workloads under the Batch Management/Run Workloads page.

First, run the SDL-FDL workload. This will apply the processors we selected in the Ingest Model for the SDL to FDL layer. You can see if the workload ran correctly by going to the IPL/Match/FDL/Stage directory in HDFS.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the IPL/Match/BDL/Fact directory in HDFS.

Step 3: Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 4: Importing Dataset into Superset

Once you are in Superset, select the Datasets option from the Data dropdown in the top menu. From there, select the add Dataset option. Set the Database to Apache Hive, select the IPL database from Schema, and select which table you would like to add. Superset will only allow you to add one table at a time, but you can add as many tables as you want one by one.

(see this page for further reference)

After adding the datasets, you can select any one of them to bring up the Charts page. Here, you can pick which type of visualization you would like to create and the parameters for that visualization.

Below is the example of number matches won by a team in IPL 2022.

Demo Case: Retail

Example 1- Exploratory Data Analysis of Car Sales dataset.

Objective: Car Sales dataset will give abstracts like:

• Percentage of car sales done by the car companies.

• Correlation of the car sales based on engine size and fuel capacity.

• Power performance of cars for each manufacturers based on the relationship between Horsepower and the length of the cars.

• Visualization of the number of cars manufactured and the horsepower with what they are manufactured

• The car companies that did the most sales based on the average horsepower of all their models and average price for all those models .

Overview: This is the Car sales data set from the years 2008 to 2012, which include information about different cars . Here, we have to see which features have more impact on car sales depending on the different attributes given.

Car Sales Dataset:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. Since we have just one layer file system we will have just one entity in it.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too.

data <- read.csv("<dataset_file_path>", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))

The next step is to upload the data once you are sure about the above steps. Use the below code snippet to upload the data to the Land directory of the corresponding entity:

library(CARSALES)
save_data <- data %>%
             dataops.overwritedata(carsales.CarSales.landdb(),'tempTable')

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

Add the column names and data types from the teams dataset into the Create Meta Model page and then click submit. Note that you do not add the as_of_date column, as that will be added automatically.

Next, create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL. These are the SDL-FDL processors we select for our Teams data. You can refer to this page to understand what each of the processors do.

Once we submit the Ingest Model, we can run the workloads under the Batch Management/Run Workloads page.

First, run the SDL-FDL workload. This will apply the processors we selected in the Ingest Model for the SDL to FDL layer. You can see if the workload ran correctly by going to the FDL/Stage directory in HDFS.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the BDL/Fact directory in HDFS.

Step 3: Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 4: Importing Database and Dataset into Superset

After adding the database and datasets (as shown in above screenshots) click on the dataset that you have created to bring up the Charts page. Here, you can pick which type of visualization you would like to create and the parameters for that visualization. Next step is to create a new dashboard for displaying all the charts at one place. Below are the examples of the charts you can make in Superset, that shows analysis like:

• Percentage of car sales done by the car companies.

• Correlation of the car sales based on engine size and fuel capacity.

• Power performance of cars for each manufacturers based on the relationship between Horsepower and the length of the cars.

• Visualization of the number of cars manufactured and the horsepower with what they are manufactured

• The car companies that did the most sales based on the average horsepower of all their models and average price for all those models .

Example 2- Exploratory Data Analysis of eCommerce dataset.

Objective: Analysis of eCommerce dataset will give the information about:

• Yearly spent by every individual on individual brands.

• Yearly spent based on the number of membership years.

• Number if items users bought.

• Majorly sold products on eCommerce platform.

• Comparison between time spent by users on app vs on website.

• Analysis of which category of products have the highest price.

Overview: eCommerce Dataset has two entities: Customers and Sales. Customers being the parent and Sales being the child entities. Sales dataset has all the details about what all products the user has bought and the Customers dataset has all the details about the user’s profile.

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. ce weSince we have just one layer file system we will have just one entity in it.

Step 2: Upload the dataset to the Land directory of the given entity.

• Read both the Customers and Sales csv file’s data that you have in your system using the below sample code snippet.

data <- read.csv('path/to/your/data/file.csv', stringsAsFactors = FALSE)

• After reading the data, click on the blue arrow near the dataset name in your environment of Rstudio.

  • Make sure all column names are in a small case, if not then you can use the below code snippet to convert all column names into a small case for both the Customers and Sales csv.

• data <- data %>%
          dplyr::rename_with(tolower)

• The dataset should contain one as_of_date column before you upload it to the Land directory of the respective directory.

NOTE: The format for as_of_date should be YYYY-MM-DD

data$as_of_date <- as.character(Sys.Date())

The next step is to upload the data once you are sure about the above steps. Use the below code snippet to upload the data to the Land directory of the corresponding entity.

library(ECOMMERCE)
save_data <- data %>%
             dataops.overwritedata(ecommerce.customers.landdb(),'tempTable')

save_data1 <- data1 %>%
             dataops.overwritedata(ecommerce.sales.landdb(),'tempTable')

Step 3: Creating Bulk Upload Meta Model

Bulk uploads is an option given in the UI for a user who wants to upload a CSV file of their Meta, Ingest, Analytic, or Import Models. Once uploaded, the respective models will get created in the HDFS.

Create the bulk upload meta model for customers and sales using below attached csv files through UI navigating to Meta Model → Bulk Upload Meta.

Step 4: Creating Bulk Upload Ingest Model

Bulk uploads is an option given in the UI for a user who wants to upload a CSV file of their Meta, Ingest, Analytic, or Import Models. Once uploaded, the respective models will get created in the HDFS.

Create the bulk upload ingest model for customers and sales by uploading below attached csv files through UI navigating to Ingest Model → Bulk Upload Ingest.

Step 5: Running the Workloads

First, run the SDL-FDL workload. This will apply the processors we selected in the Ingest Model for the SDL to FDL layer. You can see if the workload ran correctly by going to the FDL/Stage directory in HDFS.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the BDL/Fact directory in HDFS.

Step 6: Creating Data Table in Hive

Go to Home->BAPCode->utilityscripts->master->R, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement by adding your module name and entity name in it, which will help to create a data table in Hive. Once you run the script, a .hql file will be created in Home; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 7: Importing Database and Dataset into Superset and creating a dashboard for the charts

Once you are in Superset, select the Datasets option from the Data dropdown in the top menu. From there, select the add Dataset option. Set the Database to Apache Hive, select your database from Schema, and select which table you would like to add. Superset will only allow you to add one table at a time, but you can add as many tables as you want one by one.

(see this page for further reference)

Visualization of eCommerce dataset will give the information about:

• Yearly spent by every individual on individual brands.

• Yearly spent based on the number of membership years.

• Number if items users bought.

• Majorly sold products on eCommerce platform.

• Comparison between time spent by users on app vs on website.

• Analysis of which category of products have the highest price.

Example 3- Exploratory Data Analysis of GoSales dataset.

Objective: Analysis of GoSales dataset will give the information about product sale quantity.

Overview: GO Sales dataset from IBM contains information about daily sales, methods, retailers, and products of a fictitious outdoor equipment retail chain “Great Outdoors” (GO).

GOSales Dataset:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

This application can be classified as a Data Lake with curated, Correlated, Normalized, Analyze data.

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure.

We will begin with the Match dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. Add an as_of_date column, too. Next, we upload the data to SDL/Land using the dataops.appenddata() function:

library(GOSALES)

# go1k
data <- read.csv("/home/bapdevvm/Downloads/go_1k.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))
data <- data %>% rename(retailercode = retailer.code, productnumber = product.number, salesdate = date)

save_data <- data %>%
  dataops.overwritedata(gosales.go1k.landdb(),'tempTable')

# godailysales
data <- read.csv("/home/bapdevvm/Downloads/go_daily_sales.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))
data <- data %>% rename(retailercode = retailer.code, dailysalesproductno = product.number, ordermethodcode = order.method.code, dailysalesdate = date, dailysalesquantity = quantity, dailysalesunitprice = unit.price, dailysalesunitsaleprice = unit.sale.price)

save_data <- data %>%
  dataops.overwritedata(gosales.godailysales.landdb(),'tempTable')

# gomethods
data <- read.csv("/home/bapdevvm/Downloads/go_methods.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))
data <- data %>% rename(ordermethodcode = order.method.code, ordermethodtype = order.method.type)


save_data <- data %>%
  dataops.overwritedata(gosales.gomethods.landdb(),'tempTable')

# goproducts
data <- read.csv("/home/bapdevvm/Downloads/go_products.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))
data <- data %>% rename(productcolour = product.color, productunitprice = unit.price, producttype = product.type, productnumber = product.number, productbrand = product.brand, productline = product.line, productunitcost = unit.cost) 

save_data <- data %>%
  dataops.overwritedata(gosales.goproducts.landdb(),'tempTable')

# goretailers
data <- read.csv("/home/bapdevvm/Downloads/go_retailers.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
data$as_of_date = as.character(as.Date(Sys.Date()))
data <- data %>% rename(retailername = retailer.name, retailercountry = country, retailercode = retailer.code, retailertype = type) 

save_data <- data %>%
  dataops.overwritedata(gosales.goretailers.landdb(),'tempTable')

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

Add the column names and data types from all the datasets of GoSales into the Create Meta Model page and then click submit. Note that you do not add the as_of_date column, as that will be added automatically.

Next, create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL and then FDL to BDL. Below are the SDL-FDL and FDL to BDL processors that we select for all five datasets. You can refer to this page to understand what each of the processors do. Here we have shown for just one parent entity go1k and one child entity godailysales.

Below are the processors for child entity godailysales and for all the 3 other child entities you need to add the same processors for SDL to FDL and FDL to BDL stage.

Once we submit the Ingest Model, we can run the workloads under the Batch Management/Run Workloads page for all the 5 entities, below shown is for just 1 entity go1k and you need to follow same steps for other 4 entites separately.

First, run the SDL-FDL workload. This will apply the processors we selected in the Ingest Model for the SDL to FDL layer. You can see if the workload ran correctly by going to the GOSALES/go_1k/FDL/Stage directory in HDFS.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the GOSALES/go_1k/BDL/Fact directory in HDFS.

Step 3: Creating Data Table in Hive

Go to Home->BAPCode->utilityscripts->master->R, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement by adding your module name and entity name in it, which will help to create a data table in Hive. Once you run the script, a .hql file will be created in Home; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 6: Importing Database and Dataset into Superset and creating a dashboard for the charts

Above screenshot shows the configuration of just one entity, you can add the dataset individually for other 4 entities as well.

Below chart shows the visualization of Sales Quantity:

Demo Case: Entertainment

Example 1- Exploratory Data Analysis of a Movie Dataset (IMDB)

Objective: Given the IMDB dataset for seven different entities we will visualize:

• The popularity of a movie and it’s genres based on the movie rank given.

• Relationship between the movies and directors, which can also give the abstract of which all directors directed the most popular movies.

• Details of the directors and the genres on which they make their movies.

• Abstract of famous actors of the movies and their popularity based on the roles they played in a movie.

Overview: IMDB Dataset has total 7 different entities, which gives all the details for a particular movie like the popularity of movie, directors, actors, genre of the movies that the actors do and genre of the movies that the directors direct, below is the dataset of IMDB in sql format.

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. Since we have just one layer file system we will have just one entity in it.

Since the dataset was in SQL format, it was first imported by using the following command in terminal and then it was converted into parquet format by executing the import model and through which it was stored on SDL Land.

sudo mysql imdb < /home/bapdevvm/Downloads/imdb.sql 

Import Model:

The above is for one entity i.e., “actors“, there are total 7 entities and have to follow similar steps for other 6 entities by just changing the table names as per entities.

Entities:

Movie Genres

Movie

Actors

Roles

Movies Directors

Directors

Director Genres

The relationship between the parent-child entities is shown by below flowchart:

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

Ingest Model:

Ingest model for the only parent (root) entity:

Ingest model for all child entities:

Analytical Model:

To find top 10 movies

Run Import Workloads:

Run Analytical Workloads

One need to follow the above steps for all the other entities to run the import workload.

Step 3: Necessary manipulations to do before loading the data further from SDL to FDL

Once the data is loaded into SDL Land db by running the import workload, one need to write the below code in R script to add “as_of_date” column into the SDL Land data.

library(IMDB)
entities <- c('actors', 'directors', 'directorgenres', 
'moviesdirectors', 'movies', 'moviesgenres', 
'roles')  
module_name <- 'imdb'
write_data <- lapply(entities, function(ent) {
  today_date <- as.character(Sys.Date())
  land_db <- base::eval(parse(text = base::paste(module_name,ent,'landdb()',sep = '.')))
  data <- dataops.readparquet(land_db, "tempTable") %>%
    dataops.sdo.deselectcolumns("row_id") %>% 
    mutate(as_of_date = today_date) %>% 
    dataops.overwritedata(land_db, "tempTable")
})

Since the dataset is too big we will truncate it too 10k rows by writing the following code in R:

data1 <- dataops.readparquet(imdb.moviesgenres.ingesteddb(), "movies_genres") %>%
  head(10000) %>% 
  dataops.overwritedata(imdb.moviesgenres.landdb(), "movies_genres")

Write the above code for every other remaining entities.

Now, since there were conflicts in the column names between parent and child entities we are making some necessary changes into Land db, use the below code for required changes.

#Renaming the parent "id" to "movie_id"
data1 <- dataops.readparquet(imdb.movies.landdb(), "movies") %>% 
  rename(movie_year = year) %>%
  dataops.overwritedata(imdb.movies.landdb(), "movies")

#Renaming the parent "id" to "director_id"
data1 <- dataops.readparquet(imdb.directors.landdb(), "directors") %>% 
  rename(director_id = id) %>%
  dataops.overwritedata(imdb.directors.landdb(), "directors")

#Renaming the parent "id" to "actor_id"
data1 <- dataops.readparquet(imdb.actors.landdb(), "actors") %>% 
  rename(actor_id = id) %>%
  dataops.overwritedata(imdb.actors.landdb(), "actors")

data1 <- dataops.readparquet(imdb.directorgenres.landdb(), "directors_genres") %>% 
  rename(director_genre = genre) %>%
  dataops.overwritedata(imdb.directorgenres.landdb(), "directors_genres")

Step 4: Running the workloads

First, run the SDL-FDL workload. This will apply the processors we selected in the Ingest Model for the SDL to FDL layer. You can see if the workload ran correctly by going to the FDL/Stage directory in HDFS.

Once you confirm in HDFS that the SDL-FDL workload ran correctly, run the FDL-BDL workload next. This will apply the processors we selected in the Ingest Model for the FDL to BDL layer. You can see if the workload ran correctly by going to the BDL/Fact directory in HDFS.

Step 5: Creating Data Table in Hive

Go to Home->BAPCode->utilityscripts->master->R, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement by adding your module name and entity name in it, which will help to create a data table in Hive. Once you run the script, a .hql file will be created in Home; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 6: Importing Database and Dataset into Superset and creating a dashboard for the charts

Creating a dataset for two child entities i.e. for “actors” table and for “director_genres” table.

Click on the dataset to create charts for the visualizations. Create a dashboard to save all the charts that you have created. Below are the charts created to give the abstracts like:

• The popularity of a movie and it’s genres based on the movie rank given.

• Relationship between the movies and directors, which can also give the abstract of which all directors directed the most popular movies.

• Details of the directors and the genres on which they make their movies.

• Abstract of famous actors of the movies and their popularity based on the roles they played in a movie.

Below is the visualization of the IMDB dataset:

Demo Case: World

Example 1: Mapping World Airports

Objective - Visualize the number of airports by country and state

Overview

Source

We will plot the location of each airport that is in the airports dataset. We will also visualize which country and region/state has the greatest number of airports.

Classification:

Before defining the application in the system, it is useful to identify key entities and classify the application.

Key Entities:

The file structure is going to look like this:

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Explanation of the classification: <>

Normalization: <V Structure/Star Model/Snowflake and its explanation>

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure. Make sure to indicate Airports as the parent of Frequency, and Frequency as the parent of Runways.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. In the continents column, RStudio is reading the ‘NA’s as null (since NA is a keyword for missing values in R). We have to change wherever NA appears in the continents column to the actual string “NA”, for North America. After doing this, add an as_of_date column and upload the data to SDL/Land.

airports <- read.csv("<airports_file_path>", stringsAsFactors = FALSE) %>%
  rename_with(tolower)
airports$continent[is.na(airports$continent)] <- "NA"
airports$as_of_date = as.Date(Sys.Date())
dataops.appenddata(worldairports.airports.landdb(), "airportsDataSpark")

Step 2: Meta Model, Ingest Model, and Workloads

We can now set up the Meta Model in AdminUI:

Next, create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL. These are the SDL-FDL processors we select for our Teams data. You can refer to this page to understand what each of the processors do.

We then define the processors to use from FDL to BDL.

Once we submit the Ingest Model, we can run the workloads under the Batch Management/Run Workloads page.

Step 3: Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Repeating with the other Datasets

We will repeat the steps above with the three remaining datasets. Note that there will be some small differences because of the parent-child relationship between the datasets.

The next dataset we will be working with is the frequencies dataset.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. We will have to rename the id column to frequency_id and the airport_ref column to id; Because the airport_ref column in the frequency dataset matches the id column of the airports dataset, both column names must match in order to nest frequencies to airports.

Additionally, type is a keyword in R, so we have to rename that column to something different, in this case to frequency_type. Add an as_of_date column, and then upload data to SDL/Land.

frequencies <- read.csv("/home/bapdevvm/Downloads/airport-frequencies.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower) %>%
  rename(frequency_id = id) %>%
  rename(id = airport_ref) %>% 
  rename(frequency_type = type)
frequencies$as_of_date = as.Date(Sys.Date()) %>% as.character()

dataops.overwritedata(frequencies, worldairports.frequency.landdb(), "frequenciesDataSpark")

Meta Model, Ingest Model, and Workloads

The Ingest Model will be similar to the one we made for the airports' dataset, the only difference being we define two extra processors in the SDL layer for the nested quality of the airports dataset.

After creating the Ingest Model, run the SDL-FDL and FDL-BDL workloads, checking the respective directories to make sure that both workloads ran correctly.

Creating Data Table In Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal.

Runways Dataset

We will repeat the steps above with the runways datasets.

In RStudio, read the CSV file into a dataframe, and rename the column names in lower case. We will have to rename the id column to runway_id, airport_ident column to ident, and the airport_ref column to id; Because the airport_ref column in the frequency dataset matches the id column of the runways dataset, both column names must match in order to nest frequencies to airports.

Add an as_of_date column, and then upload data to SDL/Land.

runways <- read.csv("/home/bapdevvm/Downloads/runways.csv", stringsAsFactors = FALSE) %>%
  rename_with(tolower) %>%
  rename(runway_id = id) %>%
  rename(ident = airport_ident) %>%
  rename(id = airport_ref)
runways$as_of_date = as.Date(Sys.Date()) %>% as.character()

dataops.overwritedata(runways, worldairports.runways.landdb(), "runwaysDataSpark")

Meta Model, Ingest Model, and Workloads

We also want to nest the ident column between the runways dataset and airports dataset, since both datasets also share that column in common. We do that the same way we nested the ID columns together: Click on the edit icon next to the ident row, and when you arrive at the Key Roles page, mark yes for the nested and parent lookup options and give the directory path to the airports' FDL/Stage.

After creating the meta model, we can create the ingest model for the runways dataset. Refer to the Frequency dataset’s ingest model, as the processors for Runways ingest model will be the same as that of Frequency’s.

Once the ingest model is created, run both the SDL-FDL and FDL-BDL workloads for the Runways dataset, ensuring that they ran correctly by checking HDFS.

Creating Data Table in Hive

In the utilityscripts folder, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement needed to create a data table in Hive. Once you run the script, a .hql file will be created; open this file and copy the statement that was generated. Run it in the hive terminal

Step 4: Importing Dataset into Superset

After adding the datasets, you can select any one of them to bring up the Charts page. Here, you can pick which type of visualization you would like to create and the parameters for that visualization. Below are two examples of charts you can make in Superset:

Demo Case: Education

Example 1: To assess the role of a person in the University

Objective - To get the information about the role of a person in the university:

• Visualization of professor and students individually at each individual course level

• Relationship between Student, the phase they are in currently and for the number of years they are in a university program

• How many students are advised by each professor

• How many percentage of people teaching at each course level

Overview: The dataset has got 4 different entities which gives information about the relationship and details of the person, course and the role they have in the university, below is the dataset in sql format.

Classifying the Application:

Based on the data technology used and the business use, the application is classified as below:

Step 1: Setting up the application

Now that the business use and classification of the application is established, the application can be created using the UI. In AdminUI, set up the application by going to the Set-up Application tab, select Create New, and filling out the file structure.

Since the dataset was in SQL format, it was first imported by using the following command in terminal and then it was converted into parquet format by executing the import model and through which it was stored on SDL Land.

sudo mysql imdb < /home/bapdevvm/Downloads/UW_std.sql 

Import Model:

There are total 4 entities and have to follow the same steps as done above to create an Import model for other 3 entities i.e. “course“, “person, “taughtBy“.

Here all the entities are considered as parent and there is no relationship assigned between any of these entities.

Write below code snippet in R to make sure that landdb has correct data format.

library(dplyr)
library(UNIVERSITY)
entities <- c('advisedBy', 'taughtBy', 'person', 
              'course')  
module_name <- 'university'
write_data <- lapply(entities, function(ent) {
  today_date <- as.character(Sys.Date())
  land_db <- base::eval(parse(text = base::paste(tolower(module_name),tolower(ent),'landdb()',sep = '.')))
  data <- dataops.readparquet(land_db, "tempTable") %>%
    dataops.sdo.deselectcolumns("row_id") %>% 
    dplyr::rename_with(tolower) %>% 
    mutate(as_of_date = today_date) %>% 
    dataops.overwritedata(land_db, "tempTable")
})

Step 2: Meta Model: We can now set up the Meta Model in AdminUI. This is just for one entity “advisedBy“ and have to do the same set up for other 3 entities.

Add the column names and data types from the teams dataset into the Create Meta Model page and then click submit. Note that you do not add the as_of_date column, as that will be added automatically

Step 3: Ingest Model: This is just for one entity have to do same set up for other entities individually.

Create the ingest model in AdminUI. The first part will be defining which processors to use from SDL to FDL. These are the SDL-FDL processors we select for our Teams data. You can refer to this page to understand what each of the processors do.

Now, defining ingest model for FDL to BDL Layer

Step 4: Running Import Workloads: Batch Management → Run Import workload: This is just for one entity have to do same set up for other entities individually.

Step 5: Running Workloads SDL to FDL and FDL to BDL: Batch Management → Run Workload: This is just for one entity have to do same set up for other entities individually.

Step 6: Creating Data Table in Hive

Go to Home->BAPCode->utilityscripts->master->R, use the create_hive_ddl_using_spark_df.R script to generate the Hive SQL statement by adding your module name and entity name in it, which will help to create a data table in Hive. Once you run the script, a .hql file will be created in Home; open this file and copy the statement that was generated. Run it in the hive terminal.

Step 7: Importing Database and Dataset into Superset and creating a dashboard for the charts

Once you are in Superset, select the Datasets option from the Data dropdown in the top menu. From there, select the add Dataset option. Set the Database to Apache Hive, select your database from Schema, and select which table you would like to add. Superset will only allow you to add one table at a time, but you can add as many tables as you want one by one.

(see this page for further reference)

Below screenshot of dataset is for one entity, can create for other 3 remaining entities.

Database:

Step 8: Establishing parent child relationship.

Converting the entities into R data frames and merging (join()) data frames as per the parent-child relationship.

data <- dataops.readparquet(university.person.factdb(), "person")
data1 <- dataops.readparquet(university.advisedby.factdb(), "advisedBy")
data2 <- dataops.readparquet(university.taughtby.landdb(), "taughtBy")
data3 <- dataops.readparquet(university.course.factdb(), "course")

rdata1 <- dataops.sdo.copytordo(data)
rdata2 <- dataops.sdo.copytordo(data1)
rdata3 <- dataops.sdo.copytordo(data2)
rdata4 <- dataops.sdo.copytordo(data3)

people <- dataops.rdo.innerjoin(rdata1, coursedetails, c("p_id"))
advisor <- merge(rdata1, rdata2, by = "p_id", all = TRUE)

Step 9: Creating two directories into HDFS to store the data into it using hive queries and then can be accessed into superset.

Write below code in your terminal to create two different directories into HDFS:

hdfs dfs -mkdir -p /BigAnalytixsPlatform/UNIVERSITY/people
hdfs dfs -mkdir -p /BigAnalytixsPlatform/UNIVERSITY/advisor

Write below code in R to add data into two different HDFS directories:

data <- dataops.overwritedata(people, "/BigAnalytixsPlatform/UNIVERSITY/people/BDL/Fact", "people")
data <- dataops.overwritedata(advisor, "/BigAnalytixsPlatform/UNIVERSITY/advisor/BDL/Fact", "advisor")

Write below code in your terminal, its the hive queries to create tables which will be accessed through superset:

hive> create external table UNIVERSITY.people ( course_id int, courselevel string, hasposition string,  inphase string,  p_id int,  professor string,  student string,  yearsinprogram string,  hash_code string,  row_count int,  batch_id string,  report_date string,  year int,  year_start_date string,  year_end_date string,  month_name string,  month_number int,  month_start_date string,  month_end_date string,  quarter_number string,  quarter_start_date string,  quarter_end_date string,  days_in_month int,  days_in_quarter int,  days_in_year int,  day_of_week_long string,  day_of_week_abbr string,  day_of_month int,  day_of_quarter int,  day_of_year int,  week_day_ind string,  week_end_ind string,  week_of_month int,  week_of_year int,  week_start_date string,  week_end_date string ) STORED AS PARQUET LOCATION 'hdfs://localhost:9000/BigAnalytixsPlatform/UNIVERSITY/people/BDL/Fact' ;

hive> create external table UNIVERSITY.advisor ( p_id_dummy int, hasposition string,  inphase string,  p_id int,  professor string,  student string,  yearsinprogram string,  hash_code string,  row_count int,  batch_id string,  report_date string,  year int,  year_start_date string,  year_end_date string,  month_name string,  month_number int,  month_start_date string,  month_end_date string,  quarter_number string,  quarter_start_date string,  quarter_end_date string,  days_in_month int,  days_in_quarter int,  days_in_year int,  day_of_week_long string,  day_of_week_abbr string,  day_of_month int,  day_of_quarter int,  day_of_year int,  week_day_ind string,  week_end_ind string,  week_of_month int,  week_of_year int,  week_start_date string,  week_end_date string ) STORED AS PARQUET LOCATION 'hdfs://localhost:9000/BigAnalytixsPlatform/UNIVERSITY/advisor/BDL/Fact' ;

Now create two datasets in the superset for the two new entities “people” and “advisor” as shown above.

Step 10: Visualization

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