Two Shades of Mentoring: Cold & Warm. Which oneTruly Works?

Peggie Mishra — Fri, 07 Nov 2025 18:24:41 +0000

Mentoring shapes minds and futures. But here’s the truth I’ve learned after more than three decades on this planet, including a decade in the corporate world: not all mentoring works.

Over time, I’ve come to see mentoring in two distinct shades: Cold and Warm.

The Cold Shade of Mentoring
Cold mentoring happens when a mentor shares knowledge just to get it over with i.e., to fulfil an obligation rather than make an impact. The interaction feels transactional, distant, and mechanical. Information is passed along, but nothing truly connects.

The mentee listens, nods, maybe even takes notes. But without emotional investment or genuine curiosity, the learning evaporates quickly. No bond forms. The energy fades. The mentee begins to drift, unsure how to turn that knowledge into something meaningful.

The result? Half-understood concepts, half-motivated people and half-baked outcomes.

The Warm Shade of Mentoring
Warm mentoring, on the other hand, is alive. It’s the kind where a mentor shares knowledge with intention not just to teach, but to ensure the lesson lands. The conversation becomes a bridge rather than a barrier. There’s trust, patience, and genuine human connection.

In this environment, the protégé feels free to ask, explore, and challenge. There’s no fear of judgment, no hesitation to seek clarity. The relationship itself becomes a source of confidence and growth.

And when that happens, learning doesn’t just transfer — it transforms. Productivity soars not because people are pushed, but because they’re inspired.

My Observation
Most of us, unfortunately, have experienced the cold mentoring. The result is easy to spot: teams full of people juggling tasks with “half-knowledge,” rushing just to finish rather than to master.

We forget that the real goal of mentoring isn’t merely to pass on information but to build capability, confidence, and curiosity.

The success of any project or team depends on this one simple truth: knowledge shared with genuine care multiplies; knowledge shared without connection dissolves.

In the End
Every mentor sets the temperature of their legacy.

Are we sharing wisdom, or just words?

Are we helping people grow, or merely helping them finish?

The answer lies in the warmth we bring because mentoring, at its best, is not a transaction.

It’s a transformation.

“Become a warm mentor today, to create a legacy for tomorrow !”

How to Handle NULLs in Snowflake: A Complete Guide!

Peggie Mishra — Fri, 07 Nov 2025 16:06:24 +0000

Dear Readers,

NULL in data is like a weed, it can mess up your analysis and calculations, and ultimately bleed the value out of your analytics work. In this article, we’ll dive into what NULL is, why it exists, when it appears, and how to deal with it in Snowflake ?

So, without further ado, let’s meet this culprit.

What NULL?

In SQL, NULL is basically the database equivalent of a shrug.It is a special marker that represents a missing or unknown value. It's not the same as an empty string ('') or the number zero (0). It simply means “I don’t know” or “there’s no value here at all.”

Why NULL Exists?

NULL is essential because sometimes your data just doesn’t exist yet, or never will. NULL allows the database to acknowledge the gap without faking a value.

Eg:

A customer doesn’t have a middle name.
A product doesn’t have a discount yet.
A delivery date is unknown at the time of record insertion.

When NULL Appears?

NULL occurs anytime there is a lack of value from the user.

Common reasons include:

Optional fields with no provided value.
Data not yet available (future delivery dates, pending approvals)
Inapplicable values (someone’s “date of divorce” who never married)

How to Deal with NULL in Snowflake?

Snowflake provides multiple functions and methods to deal with NULL dataset aka unknown data similar to other databases. I have listed some of them below:

Use IS NULL / IS NOT NULL to identify the NULL data.
Use COALESCE(col, default) or IFNULL(col, default) to replace them in select , joins, aggregates etc
Use explicit filter on WHERE clauses to include NULL rows as it can can drop NULL rows unexpectedly.
Use NVL, CASE WHEN, or similar functions to handle them in calculations or strings.
Comparisons with NULL return UNKNOWN in SQL’s three-valued logic. Use COALESCE or IS NULL checks to avoid surprises.

Eg:

==Using NULL/NOT NULL

-- Finds all employees without a manager
SELECT name FROM employees WHERE manager_id IS NULL;

-- Finds all employees with a manager
SELECT name FROM employees WHERE manager_id IS NOT NULL;

==Using COALESCE

-- Calculates total compensation, treating a NULL bonus as 0

SELECT salary + COALESCE(bonus, 0) AS total_compensation FROM employees

The Trouble NULL Causes:

1. Comparison Traps

NULL comparisons are a common pitfall because standard operators like = or != don’t work as expected. Any comparison involving NULL returns UNKNOWN, not TRUE or FALSE. Use IS NULL or IS NOT NULL to handle the traps.

Eg:

--Evaluates to UNKNOWN when NULL = NULL

SELECT *
FROM employees
WHERE manager_id = NULL;

-- The correct way to find employees with a NULL manager_id is:
SELECT *
FROM employees
WHERE manager_id IS NULL;

2. Math & Concatenation Failures:

NULL acts like a virus in arithmetic and string concatenation operations. Any operation involving NULL will result in NULL.

Eg:

--Return of NULL total+compensation when bonus is NULL:

SELECT salary + bonus AS total_compensation
FROM employees;

--The correct way to add bonus is use COALESCE :

SELECT salary + COALESCE(bonus, 0) AS total_compensation
FROM employees;

3. Aggregate Surprises:

Aggregate functions like SUM() and AVG() ignore NULL values. This can lead to misleading results if you’re not aware of it.

Eg:

--Excludes NULL rows

SELECT AVG(salary) AS avg_salary, SUM(salary) AS total_salary
FROM employees;

--Use COALESCE to treat NULL salaries as 0 to get more accurate company-wide average

SELECT AVG(COALESCE(salary, 0)) FROM employees;

4. Join Issues:

NULL values in join keys never match each other. If an employee has a NULL in manager_id, that row won’t link to any id in the employees table even if other employees also have NULL as manager_id.

Use a LEFT JOIN + COALESCE to tackle the NULL issue.In following demonstrated way, you keep all employees in the output, and NULL managers are clearly labeled without risking false matches.

Eg:

--This INNER JOIN excludes employees with no manager_id

SELECT
E.name AS employee,
M.name AS manager
FROM employees AS E
INNER JOIN
employees AS M
ON E.manager_id = M.id;

--Use COALESCE to include employees without a manager

SELECT
E.name AS employee,
COALESCE(M.name, 'No Manager') AS manager
FROM employees E
LEFT JOIN employees M
ON E.manager_id = M.id;

5.Logic Surprises

In SQL’s three-valued logic (TRUE, FALSE, UNKNOWN), an UNKNOWN result in a WHERE clause acts like FALSE and discards the row. However, UNKNOWN can have different effects in other clauses such as CASE.

Eg:

--Logic Mishandling for NULL data.

SELECT
CASE
WHEN bonus > 10000 THEN 'High Bonus'
WHEN bonus > 5000 THEN 'Mid Bonus'
ELSE 'Low or No Bonus'
END AS bonus_category
FROM employees;

-- Explicitly checks for NULL first

SELECT
CASE
WHEN bonus IS NULL THEN 'No Bonus'
WHEN bonus > 10000 THEN 'High Bonus'
WHEN bonus > 5000 THEN 'Mid Bonus'
ELSE 'Low Bonus'
END AS bonus_category
FROM employees;
Closing thoughts,

NULLs aren’t a bug, they’re part of SQL’s design.NULL is tricky. The safest approach is to scan your dataset for every attribute that could be NULL, then apply handling strategies suited to your business context. In Snowflake, a few well-placed COALESCE, IS NULL, and CASE statements can save you from major data quality headaches.

Hope you enjoyed the article! If you like my writing, feel free to connect with me on Linkedin and follow for more such interesting reads.

DEV Community: Peggie Mishra

Two Shades of Mentoring: Cold & Warm. Which oneTruly Works?

How to Handle NULLs in Snowflake: A Complete Guide!