AI & DS HUE EXPERIMENT

 

 STEP 1 — Create Sample Dataset (On Linux)

Create emp1.csv:

vi emp1.csv

Paste:

employee_id,name,department,salary
1,John Doe,Engineering,70000
2,Jane Smith,Marketing,60000
3,Jim Brown,Engineering,80000
4,Jake White,Sales,50000
5,Emily Davis,Marketing,75000

Save and exit.

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start hue

http://localhost:8088

training

training

 STEP 2 — Upload File to HDFS

Option A (Using Command Line)

hadoop fs -mkdir -p /user/hue
hadoop fs -put emp1.csv /user/hue/
hadoop fs -ls /user/hue

You should see emp1.csv listed.

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Option B (Using Hue UI)

1. Open Hue

2. Go to File Browser

3. Click Upload

4. Select employees.csv

5. Upload to:

/user/hue/

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✅ STEP 3 — Create Hive Table in Hue

Go to:

Hue → Query Editors → Hive

CREATE TABLE emp1 (
   employee_id INT,
   name STRING,
   department STRING,
   salary FLOAT
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;

Then load data:

LOAD DATA INPATH '/user/hue/emp1.csv'
INTO TABLE emp1;

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✅ STEP 4 — Verify Data

Run:

SELECT * FROM emp1;

You should see 5 rows.

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✅ STEP 5 — Analysis Queries

1️⃣ Count Employees by Department

SELECT department, COUNT(*) AS employee_count
FROM emp1
GROUP BY department;

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2️⃣ Average Salary by Department

SELECT department, AVG(salary) AS average_salary
FROM emp1
GROUP BY department;

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3️⃣ Salary Greater Than 60000

SELECT name, salary
FROM emp1
WHERE salary > 60000;

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✅ STEP 6 — Generate Reports

After running query in Hue:

1. Results appear below

2. Click Download

3. Export as:

   • CSV

   • Excel

This becomes your report.

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✅ STEP 7 — Create Dashboard in Hue

1. Go to Dashboards

2. Click New Dashboard

3. Add a widget

4. Select your saved Hive query

5. Choose:

   • Bar Chart → Employee count by department

   • Pie Chart → Salary distribution

Save dashboard.

Now you have visual report.

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✅ OPTIONAL — Using Impala (Faster Queries)

In Hue:

Query Editors → Impala

Run same queries (faster performance).