Data Analysts and Data Engineers at Ecolab write SQL queries to analyze water treatment data, including pH levels, chemical dosing rates, and flow rates. It is also used to optimize industrial cleaning procedures by identifying trends in equipment performance and maintenance schedules, the reason why Ecolab asks SQL questions during job interviews.
To help you practice for the Ecolab SQL interview, here’s 10 Ecolab SQL interview questions – scroll down to start solving them!
Ecolab is a global leader in water, hygiene, and infection prevention solutions and services. Your role as a data analyst is to analyze the sales of chemicals Ecolab sold for each month. The 'sales' table maintains records of each chemical sold, its quantity and the date of sale.
You have to write a SQL query that calculates the total and average quantity of each chemical sold in each month. The resulting output should be sorted by month and then by total quantity in descending order.
sale_id | sale_date | chemical_id | quantity_sold |
---|---|---|---|
101 | 2022-01-15 | 1 | 50 |
102 | 2022-01-20 | 2 | 60 |
103 | 2022-01-25 | 1 | 70 |
104 | 2022-02-12 | 1 | 40 |
105 | 2022-02-15 | 2 | 30 |
month | chemical_id | total_quantity | average_quantity |
---|---|---|---|
1 | 1 | 120 | 60 |
1 | 2 | 60 | 60 |
2 | 1 | 40 | 40 |
2 | 2 | 30 | 30 |
In this query, we first extract the month from the column. Then, we group the data by and . The sum function calculates the total quantity sold for each medicine in each month. Then, the avg function calculates the average quantity sold. We have sorted the result by and then by in descending order to meet your requirement.
To solve a related window function SQL problem on DataLemur's free online SQL coding environment, solve this Amazon BI Engineer interview question:
Given a table of Ecolab employee salary data, write a SQL query to find the 2nd highest salary amongst all the employees.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Check your SQL query for this interview question and run your code right in DataLemur's online SQL environment:
You can find a detailed solution with hints here: 2nd Highest Salary.
In SQL, a join is used to combine rows from different tables based on a shared key or set of keys, resulting in a single merged table of data.
There are four distinct types of JOINs: , , , and .
(INNER) JOIN: Retrieves records that have matching values in both tables involved in the join.
LEFT (OUTER) JOIN: Retrieves all the records/rows from the left and the matched records/rows from the right table.
RIGHT (OUTER) JOIN: Retrieves all the records/rows from the right and the matched records/rows from the left table.
FULL (OUTER) JOIN: Retrieves all the records where there is a match in either the left or right table.
Ecolab is a company dedicated to water, hygiene and energy technologies and services. In an effort to promote water conservation, the company undertakes several water-saving projects. Often, they might want to find out the average amount of water saved per project.
Given the below two tables, and , write a SQL query that returns the average amount of water saved per project.
project_id | project_name | start_date | end_date |
---|---|---|---|
1001 | Water Json | 01/01/2021 | 31/12/2021 |
1002 | Rain Check | 01/02/2021 | 28/02/2021 |
1003 | Aqua LLC | 01/03/2021 | 31/03/2021 |
1004 | Hidro Marvin | 01/04/2021 | 30/04/2021 |
1005 | Lagoon Eagle | 01/05/2021 | 31/05/2021 |
project_id | saving_date | water_saved |
---|---|---|
1001 | 01/03/2021 | 100 |
1002 | 02/02/2021 | 400 |
1002 | 24/02/2021 | 300 |
1003 | 15/03/2021 | 200 |
1004 | 10/04/2021 | 500 |
1004 | 18/04/2021 | 300 |
1005 | 09/05/2021 | 400 |
In the above SQL query, we first join the table and table on , then we calculate the average amount of for each project, grouped by and . This will give us the average amount of water saved per project.
To practice a very similar question try this interactive Amazon Highest-Grossing Items Question which is similar for calculating highest values in categories or this Amazon Average Review Ratings Question which is similar for averaging calculations across groups.
The and operators are both used to filter data based on certain criteria, but they work in different ways. is used to select values within a range, while is used to select values that match a list.
For instance, if you have a table called that contains the salary of each employee, along with which country they reside in, you could use the operator to find all employees who make between 160k:
To find all employees that reside in France and Germany, you could use the operator:
Ecolab, as a global leader in water, hygiene, and infection prevention solutions and services, uses many pieces of equipment across various divisions. The salvage value represents the estimated worth of a piece of heavy equipment after its useful life.
In this question, we need to find the average salvage value of equipment for each division in Ecolab at the end of the year 2021.
equipment_id | division_id | purchase_date | salvage_value |
---|---|---|---|
1001 | 201 | 2018-11-29 | 1000 |
1002 | 202 | 2019-10-10 | 1500 |
1003 | 201 | 2020-09-01 | 1100 |
1004 | 203 | 2019-08-24 | 1000 |
1005 | 202 | 2019-06-15 | 1800 |
1006 | 201 | 2021-04-20 | 1200 |
1007 | 203 | 2021-05-25 | 900 |
This code block outputs the average salvage value by division. The function calculates the average salvage value for each division identified by . The clause filters out equipment purchased after 2021. The clause groups the records by .
division_id | avg_salvage_value |
---|---|
201 | 1100 |
202 | 1650 |
203 | 950 |
This output table shows the average salvage value of the equipment per division till the end of year 2021.
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes that can be used in a database:
For a concrete example, say you had a table of Ecolab customer payments with the following columns: payment_id, customer_id, payment_amount, and payment_date.
Here's what a clustered index on the column would look like:
A clustered index on the column would determine the physical order of the records in the table based on the . This means that the records with the earliest values would be stored together physically in the table, followed by records with later payment_date values.
Having a clustered index on the column can speed up queries that filter or sort the data based on the payment_date, as the records are already physically sorted in the table. For example, if you want to retrieve all the payments made in the month of January, the database can use the clustered index to quickly locate and retrieve the desired records without having to search through the entire table.
You are given a customers' table and need to find all customers whose first names begin with the letter 'A' and end in 'a' (e.g. "Amanda", "Anita", "Alicia" etc.). The customers' table has three columns: , and .
Write a SQL query to find all customers whose first names follow the above pattern and return their complete information from the table.
customer_id | first_name | |
---|---|---|
C101 | Amanda | amanda@example.com |
C102 | Carlos | carlos@example.com |
C103 | Anita | anita@example.com |
C104 | John | john@example.com |
C105 | Alicia | alicia@example.com |
customer_id | first_name | |
---|---|---|
C101 | Amanda | amanda@example.com |
C103 | Anita | anita@example.com |
C105 | Alicia | alicia@example.com |
This query selects all columns from the table where begins with 'A' and ends with 'a'. The percent symbol (%) in the LIKE clause is a wildcard character that matches any sequence of characters. Hence, 'A%A' matches any string that begins with 'A' and ends with 'a'.
Ecolab offers various products categorized into different categories to their customers. We have a table which contains detailed invoice data of each sale done by Ecolab, and a table which contains detailed data about each product. Your job is to write a SQL query that returns a list of every Ecolab customer with the total sales and total quantity of each product category they purchased from.
The table is structured as follows:
sale_id | customer_id | product_id | quantity | sale_price |
---|---|---|---|---|
100 | 20 | 300 | 2 | 100 |
101 | 20 | 301 | 1 | 150 |
102 | 30 | 300 | 5 | 100 |
103 | 40 | 302 | 3 | 200 |
104 | 30 | 301 | 1 | 150 |
The table is structured as follows:
product_id | category |
---|---|
300 | 'Cleaning' |
301 | 'Kitchen' |
302 | 'Bathroom' |
You may assume that in the table represents the price of each individual unit of product, and represents the quantity of units purchased in each sale.
This query calculates the total sales and total quantity per customer for each product category at Ecolab. It first establishes a connection between the sales and products tables using a JOIN operation on the common product_id field. Then, using a GROUP BY operation, it aggregrates rows with the same customer id and product category, and sums the quantity field and the product of sale price and quantity field for each group.
Since join questions come up routinely during SQL interviews, take a stab at this interactive Snapchat SQL Interview question using JOINS:
The function can take in multiple paramaters, and returns the first input paramater that is not null. If all arguments are null, the COALESCE function will return null too.
Suppose you have a table of Ecolab salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
sales_person | closed_deals |
---|---|
Jason Wright | NULL |
Drew Jackson | 3 |
Chris Ho | NULL |
Adam Cohen | 2 |
Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
sales_person | closed_deals |
---|---|
Jason Wright | 0 |
Drew Jackson | 3 |
Chris Ho | 0 |
Adam Cohen | 2 |
Samantha Perez | 4 |
The best way to prepare for a SQL interview, besides making sure you have strong SQL fundamentals, is to practice a ton of real SQL questions that were asked in recent job interviews. In addition to solving the earlier Ecolab SQL interview questions, you should also solve the 200+ SQL questions from real Data Science & Analytics interviews which come from companies like Microsoft, Google, and Meta.
Each DataLemur SQL question has multiple hints, step-by-step solutions and best of all, there's an online SQL coding environment so you can instantly run your SQL query and have it checked.
To prep for the Ecolab SQL interview it is also useful to practice interview questions from other chemical companies like:
Stay ahead of the curve with Ecolab's latest advancements in AI technology for the foodservice industry!
However, if your SQL coding skills are weak, don't worry about jumping right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers things like handling timestamps and filtering strings based on patterns – both of which pop up frequently in SQL interviews at Ecolab.
In addition to SQL interview questions, the other question categories to prepare for the Ecolab Data Science Interview are:
I think the best way to prep for Ecolab Data Science interviews is to read the book Ace the Data Science Interview.
It has 201 data interview questions sourced from Google, Microsoft & tech startups. The book's also got a crash course covering SQL, Product-Sense & ML. And finally it's helped a TON of people, which is why it's got over 1000+ 5-star reviews on Amazon.
While the book is more technical in nature, it's also crucial to prepare for the Ecolab behavioral interview. Start by understanding the company's values and mission.