Data Analyst Interview Resources
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Channel Data Analyst Interview Resources (@dataanalystinterview) in the English language segment is an active participant. Currently, the community unites 52 285 subscribers, ranking 3 330 in the Education category and 7 186 in the India region.
๐ Audience metrics and dynamics
Since its creation on ะฝะตะฒัะดะพะผะพ, the project has demonstrated rapid growth, gathering an audience of 52 285 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 247 over the last 30 days and by 13 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.55%. Within the first 24 hours after publication, content typically collects 0.92% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 332 views. Within the first day, a publication typically gains 479 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as sql, row, |--, dataset, visualization.
๐ Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
โJoin our telegram channel to learn how data analysis can reveal fascinating patterns, trends, and stories hidden within the numbers! ๐
For ads & suggestions: @love_dataโ
Thanks to the high frequency of updates (latest data received on 12 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
WITH RECURSIVE EmployeeHierarchy AS (
SELECT employee_id, employee_name, manager_id
FROM employees
WHERE manager_id IS NULL
UNION ALL
SELECT e.employee_id, e.employee_name, e.manager_id
FROM employees e
JOIN EmployeeHierarchy eh ON e.manager_id = eh.employee_id
)
SELECT *
FROM EmployeeHierarchy;
2. Pivoting Data
Turn row data into columns (e.g., show product categories as separate columns).
SELECT *
FROM (
SELECT TO_CHAR(order_date, 'YYYY-MM') AS month, product_category, sales_amount
FROM sales
) AS pivot_data
PIVOT (
SUM(sales_amount)
FOR product_category IN ('Electronics', 'Clothing', 'Books')
) AS pivoted_sales;
3. Window Functions
Calculate a running total of sales based on order date.
SELECT
order_date,
sales_amount,
SUM(sales_amount) OVER (ORDER BY order_date) AS running_total
FROM sales;
4. Ranking with Window Functions
Rank employeesโ salaries within each department.
SELECT
department,
employee_name,
salary,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS salary_rank
FROM employees;
5. Finding Gaps in Sequences
Identify missing values in a sequential dataset (e.g., order numbers).
WITH Sequences AS (
SELECT MIN(order_number) AS start_seq, MAX(order_number) AS end_seq
FROM orders
)
SELECT start_seq + 1 AS missing_sequence
FROM Sequences
WHERE NOT EXISTS (
SELECT 1
FROM orders o
WHERE o.order_number = Sequences.start_seq + 1
);
6. Unpivoting Data
Convert columns into rows to simplify analysis of multiple attributes.
SELECT
product_id,
attribute_name,
attribute_value
FROM products
UNPIVOT (
attribute_value FOR attribute_name IN (color, size, weight)
) AS unpivoted_data;
7. Finding Consecutive Events
Check for consecutive days/orders for the same product using LAG().
WITH ConsecutiveOrders AS (
SELECT
product_id,
order_date,
LAG(order_date) OVER (PARTITION BY product_id ORDER BY order_date) AS prev_order_date
FROM orders
)
SELECT product_id, order_date, prev_order_date
FROM ConsecutiveOrders
WHERE order_date - prev_order_date = 1;
8. Aggregation with the FILTER Clause
Calculate selective averages (e.g., only for the Sales department).
SELECT
department,
AVG(salary) FILTER (WHERE department = 'Sales') AS avg_salary_sales
FROM employees
GROUP BY department;
9. JSON Data Extraction
Extract values from JSON columns directly in SQL.
SELECT
order_id,
customer_id,
order_details ->> 'product' AS product_name,
CAST(order_details ->> 'quantity' AS INTEGER) AS quantity
FROM orders;
10. Using Temporary Tables
Create a temporary table for intermediate results, then join it with other tables.
-- Create a temporary table
CREATE TEMPORARY TABLE temp_product_sales AS
SELECT product_id, SUM(sales_amount) AS total_sales
FROM sales
GROUP BY product_id;
-- Use the temp table
SELECT p.product_name, t.total_sales
FROM products p
JOIN temp_product_sales t ON p.product_id = t.product_id;
Why These Matter
Advanced SQL queries let you handle complex data manipulation and analysis tasks with ease. From traversing hierarchical relationships to reshaping data (pivot/unpivot) and working with JSON, these techniques expand your ability to derive insights from relational databases.
Keep practicing these queries to solidify your SQL expertise and make more data-driven decisions!
Here you can find essential SQL Interview Resources๐
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
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