The Revature Business Intelligence interview guide, interview questions, salary data, and interview experiences.

Revature Business Intelligence Interview Guide

<p>Getting ready for an Business Intelligence interview at Revature? The Revature Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:</p>
<ul>
<li>Know what skills are necessary for Revature Business Intelligence roles.</li>
<li>Gain insights into the Business Intelligence interview process at Revature.</li>
<li>Practice real Revature Business Intelligence interview questions.</li>
</ul>
<p>Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Revature Business Intelligence interview.</p>


Getting ready for an Business Intelligence interview at Revature? The Revature Business Intelligence interview span across 10 to 12 different question topics. In preparing for the interview:

- Know what skills are necessary for Revature Business Intelligence roles.
- Gain insights into the Business Intelligence interview process at Revature.
- Practice real Revature Business Intelligence interview questions.

Interview Query regularly analyzes interview experience data, and we've used that data to produce this guide, with sample interview questions and an overview of the Revature Business Intelligence interview.
        

Overview

Revature Business Intelligence Salary

<p>We've gathered this data from parsing thousands of interview experiences sourced from members.</p>

We've gathered this data from parsing thousands of interview experiences sourced from members.

Revature Business Intelligence Interview Process

<p>We’re given a table of bank transactions with three columns, <code>user_id</code>, a deposit or withdrawal value (determined if the value is positive or negative), and <code>created_at</code> time for each transaction.</p>

<p>Write a query to get the total three-day rolling average for deposits by day.</p>

<p><em>Note: Please use the format <code>&#39;%Y-%m-%d&#39;</code> for the date in the outout</em></p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>bank_transactions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>transaction_value</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>dt</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>rolling_three_day</code></td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Rolling Bank Transactions

<p>Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.</p>

<p><strong>Example:</strong></p>

<p><strong>Input:</strong></p>

<p><code>transactions</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>user_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>created_at</code></td>
<td>DATETIME</td>
</tr>

<tr>
<td><code>product_id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>quantity</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<p><code>users</code> table</p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p> 
<strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>customer_name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>


<p>Which of the following queries will return a list of customers and the total amount they have spent on orders?</p>


Which of the following queries will return a list of customers and the total amount they have spent on orders?

Customer Orders

<p>Let’s say you’re working for a social media company trying to grow your user base. You know that people tend to use the same social network as their peers, so you want to target all your growth and marketing efforts on specific age groups so as to take advantage of network effects.</p>

<p>You wish to begin with the age group that is currently generating the most connections (fastest growing).</p>

<p>Write a SQL query to find the average number of accepted friend requests for each age group and order the results in descending order.</p>

<p><em>Note: The average number of accepted friend requests is calculated as the total number of friend requests sent by an age group that was accepted divided by the number of members in said age group.</em></p>

<p><em>We can assume the tables hold valid data in that there are no duplicate friend requests, friend requests are only accepted once, and user A can only accept a friend request from User B after B sends A a friend request.</em></p>

<p><em>The result should be rounded to two decimal places.</em></p>

<h3>Example:</h3>

<p><strong>Input:</strong></p>

<p><strong><code>friend_requests</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>receiver_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>sent_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>requests_accepted</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>accepter_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>requester_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>accepted_at</td>
<td>DATETIME</td>
</tr>
</tbody>
</table>
<p><strong><code>age_groups</code></strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>user_id</td>
<td>INTEGER</td>
</tr>

<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
<p><strong>Output:</strong></p>

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td>age_group</td>
<td>VARCHAR</td>
</tr>

<tr>
<td>average_acceptance</td>
<td>FLOAT</td>
</tr>
</tbody>
</table>


Find the average number of accepted friend requests for each age group that sent the requests.

Avg Friend Requests By Age Group

Revature Business Intelligence Interview Questions

<p><strong>About the Role</strong></p><p>We are looking for a skilled Data Engineering Analyst with expertise in PySpark, Python, Hadoop, and Azure Databricks to join our team. You will play a key role in data integration, transformation, and analysis, working with large datasets to deliver insights that drive business decisions. In this role, you will collaborate with cross-functional teams, including data scientists, data engineers, and business analysts, to develop and maintain data pipelines, and ensure data quality and consistency across systems.</p><p><strong>Key Responsibilities</strong></p><ul><li>Design, develop, and optimize ETL pipelines for data ingestion, transformation, and loading, using PySpark and Azure Databricks.</li><li>Implement data processing and transformation logic in Python and PySpark to handle large datasets efficiently.</li><li>Work with Hadoop-based data lakes and Azure cloud storage solutions to manage and retrieve data as required.</li><li>Collaborate with data scientists and analysts to provide data-driven insights and solutions.</li><li>Perform data profiling, validation, and quality assurance to ensure data accuracy and consistency.</li><li>Optimize data models and pipelines for performance, scalability, and reliability.</li><li>Utilize Azure Databricks for data processing workflows, managing resources, and ensuring job efficiency.</li><li>Monitor, troubleshoot, and resolve data pipeline issues to ensure minimal downtime and data accuracy.</li><li>Contribute to documentation and best practices for data engineering processes and standards.</li></ul><p><strong>Required Skills and Qualifications</strong></p><ul><li><strong>Education</strong>: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related field.</li><li><strong>Experience</strong>: 3+ years of experience in data engineering, with hands-on experience in PySpark, Python, Hadoop, and Azure Databricks.</li><li><strong>Technical Skills</strong>:</li><li>Strong experience with <strong>Python</strong> and <strong>PySpark</strong> for data transformation and analysis.</li><li>Proficiency in <strong>Hadoop</strong> ecosystems (HDFS, Hive, HBase) for data storage and retrieval.</li><li>Experience with <strong>Azure Databricks</strong> and other Azure data services, including Azure Data Lake, Azure Blob Storage, and Azure Synapse.</li><li>Strong SQL skills for data manipulation, querying, and validation.</li><li>Knowledge of ETL processes and tools, including performance optimization techniques.</li><li><strong>Analytical Skills</strong>:</li><li>Strong problem-solving skills with the ability to troubleshoot complex data issues.</li><li>Proficiency in handling large datasets and performing data profiling and cleansing.</li><li><strong>Soft Skills</strong>:</li><li>Excellent communication and teamwork abilities to collaborate with cross-functional teams.</li><li>Strong attention to detail and ability to work independently and manage multiple tasks effectively.</li></ul><p></p>

Revature Business Intelligence Interview Guide

Overview

Revature Business Intelligence Salary

Revature Business Intelligence Interview Process

Revature Business Intelligence Interview Questions

Revature Business Intelligence Jobs