Extract, Transform, Load (ETL) interview questions are a time-tested part of data engineering interviews. Typically, ETL interview questions encompass a wide range of topics including, but not limited to, ETL testing, SQL, ETL coding (typically Python), and even system design.
Typically, ETL interview questions can range in difficulty and cover the following sections:
Basic ETL Interview Questions: The questions at this level revolve around fundamental ETL concepts and definitions. Topics can include:
Intermediate ETL Interview Questions: Here, the questions typically pertain to scenarios that test the depth of your ETL knowledge. The topics often covered are:
Hard ETL Interview Questions: These questions involve advanced case studies where you are asked to design ETL solutions for specific (and often complex) business uses. Key areas of focus include:
By preparing and practicing questions from each of these categories, you can ensure a holistic understanding of ETL processes. This can significantly enhance your performance in data engineering and data science interviews.
ETL interview questions are not limited to ETL developers. ETL as a process is useful in many fields and subfields, and applicants for the following roles can expect to see ETL questions as a part of their interviews:
To be prepared for an ETL interview, you should be prepared to display the following skills:
This is especially true for roles in data, machine learning, and BI engineering, which all require advanced knowledge of ETL architecture. However, ETL questions asked of data scientists and analysts tend to be focused on the writing of SQL queries for ETL processes.
Basic ETL questions are asked early in interviews or for junior-level roles. These questions are typically definition-based or ask you to perform comparisons of different ETL tools and processes.
ETL is an integral part of data science and stands for Extract, Transform, and Load.
Extract is the process wherein one gathers and aggregates data. Often, data extraction can come from one or more data sources.
After aggregation, you Transform (clean) the data with the help of calculations and eliminations, with techniques like null handling, interpolation, and normalization.
Finally, Load refers to when the data is stored in a data warehouse or dashboard for future use. Typically, ETL questions are a core part of data engineering interview questions.
ETL (Extract, Transform, Load) plays a crucial role in data science by offering several benefits. Firstly, ETL allows for the consolidation of data from different sources into a unified format. This simplifies the data landscape and provides a comprehensive view of information, making it easier to analyze and derive insights.
Another significant advantage of ETL is its ability to transform data. By structuring, cleaning, and standardizing raw data, ETL ensures consistency and quality. This enhances the accuracy and reliability of subsequent analyses, reducing the risk of incorrect or misleading conclusions.
Moreover, ETL enables data scientists to process and manipulate large volumes of data efficiently. By automating the extraction, transformation, and loading processes, ETL tools streamline the workflow, saving time and effort.
Additionally, ETL plays a vital role in data integration. By combining data from various sources, ETL facilitates the creation of comprehensive data sets that provide a more complete picture of the subject under investigation. This integration opens up opportunities for deeper analysis, and enables the discovery of hidden patterns or correlations that may not have been apparent when considering individual data sources in isolation.
Ultimately, ETL empowers data scientists to leverage organized and meaningful data to uncover patterns, make informed decisions, and drive data-driven strategies for organizations.
Your specific tech stack may not be the sole defining factor when it comes to your appeal as an engineer. However, what truly matters is your breadth of experience with different tools, rather than being focused on a particular flavor or implementation. By sharing your experiences during an interview on a certain tech tool, such as the challenges you faced learning or using it, you can better introduce your abilities as an ETL engineer.
Start by researching the ETL tools the company already uses. Your goal should be to have a solid overview of the most common processes and uses for the tools. If you are unsure of the companies’ tools, or if they use proprietary in-house products, consider mastering SQL, a programming language of choice (preferably Python), ETL tools such as Informatica PowerCenter, and a business intelligence suite such as Power BI.
When it comes to distinguishing between ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) processes, it’s important to note that they are not mutually exclusive. Many organizations adopt variations like ETLT, choosing the approach that proves to be more efficient or cost-effective for their specific needs.
The main differentiation lies in how raw data is loaded into data warehouses. ETL traditionally involves loading data into a separate server for transformation, whereas ELT directly moves data onto the target system before performing transformations—essentially placing the load step before the transform step.
One advantage of ETL is its capability to handle certain transformations that cannot be performed within a warehouse. In contrast, ELT performs transformations inside the warehouse, necessitating the use of enhanced privacy safeguards and robust encryption measures.
The data warehouse is a culmination of the SSOT (Single Source of Truth) ideology, where an organization stores all of its data in the same place, primarily for convenience and comparability. Because businesses have their data stored in a single location, creating insights is more accessible, and one can make strategic business decisions faster. Decision makers are also not left wondering if there are separate/forgotten/hidden locations for the information they may wish to access. No more sudden discoveries of customers in a far-flung market for example.
A data warehouse, a data lake, a data mart, and a database, while all used for data management, serve different purposes and use cases. A data warehouse is a structured repository of historical data from various sources, designed for query and analysis. It’s the go-to place for deriving insights useful to strategic decision-making.
On the other hand, a data lake is a vast pool of raw data, stored in its native format until needed. It’s a more flexible solution that can store all types of data, making it perfect for big data and real-time analytics scenarios.
A data mart, on the other hand, is a subset of a data warehouse, tailored to meet the needs of a specific business unit or team. It’s more focused, so it’s quicker and easier to access relevant data.
Lastly, a database is a structured set of data. It’s the basic building block of data storage, capable of handling transactions and operations in applications, but it’s not optimized for the complex queries and analytics that data warehouses are.
Here are some keywords you can use to easily distinguish these four formats:
|Data Warehouse||Analytics, Read, Query|
|Data Lake||Raw, Big Data, Unstructured|
|Data Mart||Specific, Tailored, Subset|
|Database||Schema, CRUD, base|
The crucial part of the ETL process is the aggregation of data from various places. Most ETL pipelines extract their data from the following sources:
So, the data sources for an ETL process are quite diverse, and the choice depends largely on the business requirements and the nature of the data being handled.
This question is vague and requires some clarity, since ETL design varies by the organization’s data needs. For example, Spotify holds over 80 million songs and over 400 million users. Spotify’s ETL solutions must be built for efficiency and performance, whereas a startup might focus more on building for accuracy.
When you describe your design process, a few points to consider touching on would include:
Designing ETL solutions for big data scenarios requires careful strategizing, as the sheer volume, variety, and velocity of data can easily overwhelm traditional ETL frameworks. For instance, consider the work Google faces while crawling and indexing billions of webpages daily – tasks that require a massively scaled and highly robust ETL infrastructure.
When plotting out your strategy, there are some key stages you should go through, such as:
Let’s assume you’re tasked with pulling data from an open API for weather. You need to extract this data, convert it to a Pandas DataFrame, and perform some basic data cleaning operations. How would you go about this task?
requests library is great for making HTTP requests to APIs. Once you have the data, you can use Pandas to convert it to a DataFrame for further processing. Here’s an example of how you could do it:
import requests import pandas as pd # make a GET request to the API response = requests.get('http://api.open-weather.com/data/path') # the data is usually in the response's json, convert it to a DataFrame data = response.json() df = pd.DataFrame(data) # perform some data cleaning operations # for example, we might remove missing values df = df.dropna() print(df)
This basic workflow can be extended or modified depending on the specifics of the data and the API.
Many APIs enforce rate limits in order to control the number of requests a client can make in a given time period. If your ETL process involves fetching data from such APIs, how would you ensure your script respects these rate limits?
API rate limiting can be managed by controlling the frequency of the requests in your script. A common way to do this is by using Python’s
time.sleep() function to introduce a delay between requests. Another way is to check the response headers, as some APIs return information about the rate limits
? Note: This example presumes a simple form of rate limiting (e.g., a fixed number of requests per minute). Some APIs may use more complex forms, like progressively decreasing limits, and you’ll have to adjust your approach accordingly.
These questions require more advanced knowledge of ETL and are typically in-depth discussion-based questions. You might be presented with a scenario and asked to provide an ETL solution at this level of questioning.
These questions require more advanced knowledge of ETL and are typically in-depth discussion-based questions. You might be presented with a scenario and asked to provide an ETL solution at this level of questioning.
More context. Let’s say that you’re trying to run some data processing and cleaning on a
.csv file that’s currently 100GB large. You realize it’s too big to store in memory, so you can’t clean the file using
pandas.read_csv(). How would you get around this issue and still clean the data?
There are two main methods to handle large datasets: streaming and chunking.
Streaming: The term streaming refers to reading the data in ‘stream’, i.e., only one line at a time. It allows Pandas to limit the memory use to the current line. You can do streaming with the following syntax:
with open('large_file.csv') as file: for line in file: # do something
The downside of this approach is that you can’t use many of Pandas’ powerful data processing functions, which operate on entire DataFrames.
- Chunking: A more effective approach for utilizing Pandas’ capabilities on large datasets is to read the file in chunks.
? Note that a chunk represents a line in your CSV file.
Pandas provides the
read_csv() function, which has the keyword argument
chunksize to allow you to specify the size of the chunk.
read_csv() returns an iterable which you combine with a
for loop to iterate through the chunks. Consider the following syntax:
CHUNK_SIZE = 10000 # modify the chunk size as needed processed_data =  chunks = pd.read_csv('very_large_csv.csv', chunksize=CHUNK_SIZE) # processes all the chunks for chunk in chunks: processed = process_data(chunk) processed_data.append(processed) # combine all the data into a singular df big_dataframe = pd.concat(chunks, axis=0)
During the transformation stage, ETL testing plays a critical role in identifying any potential bugs or data errors that may arise. Its purpose is to ensure that these issues are caught and addressed before the data reaches the data warehouse. By conducting ETL testing, risks such as double records and compromised data integrity can be mitigated effectively. Most importantly, it helps prevent any loss or mishandling of data.
Without the implementation of ETL testing, incorrect data could potentially make its way into the data warehouse, leading to significant misinterpretations on subsequent analysis. This not only introduces bias into the analytical perspective of data analysts, but also has the potential to result in ill-informed business decisions.
? Note:It is essential to remember the fundamental business axiom of “Garbage In, Garbage Out”, which succinctly emphasizes how bad data can result in worse outputs. Inversely, good inputs will lead to better outputs.
Knowing the limitations and weaknesses of ETL is critical to demonstrate in ETL interviews. It allows you to assess, find workarounds, or entirely avoid specific processes that may slow the production of relevant data.
For example, staging and transformation are extremely time intensive. Moreover, if the sources are unconventional or structurally different, the transformation process might take a long time. Another bottleneck of ETL is the involvement of hardware (specifically disk-based pipelines) during transformation and staging. The hardware limitations of physical disks can create slowdowns that no efficient algorithm can solve.
Validating data transformations during ETL testing is pivotal to ensuring the integrity and accuracy of the data moving from the source system to the target data warehouse. It involves numerous checks and validations to ensure that the data has not been corrupted or altered in a way that could distort the results of data analysis.
A multi-step approach can help ensure accurate ETL testing:
1. Source to Target Count Check: This involves checking the record count in the source and then again in the target system after the ETL process. If the numbers don’t match, then there’s a high probability of data loss during the ETL process.
2. Data Integrity Check: Here, the tester confirms that the transformations didn’t compromise the data’s integrity. They need to understand thoroughly the business rules that govern these transformations, as data discrepancies can result from complex transformation logic.
3. Verification of Data Transformations: To validate the transformation rules, the tester would select a sample of data and trace its journey from the source to the destination system. They would manually calculate the expected results of transformations and compare these to the actual results.
4. Check for Data Duplication: Duplicate entries can affect the accuracy of analyses, so testers should include a check for duplicate data in their testing strategy.
5. Performance Testing: Finally, it’s crucial to evaluate the performance of the ETL process. It needs to run in a reasonable time and without straining the resources of the system. ETL testing should, therefore, include performance and load testing.
SQL is closely related to ETL work and is widely used in ETL design and development. SQL questions are asked regularly in ETL interviews, assessing your ability to write clean SQL queries based on given parameters.
Hint. The first step we need to do is to remove duplicates and retain the current salary for each user. The interviewer lets you know that there are no duplicate first and last name combinations. We can therefore remove duplicates from the
employees table by running a GROUP BY on two fields, the first and last name. This allows us to then get a unique combinational value between the two fields.
This is great, but at the same time, we are now stuck with trying to find the most recent salary from the user. How would we be able to tell which was the most recent salary without a datetime column?
The interviewer lets you know that, “instead of updating the salaries every year when doing compensation adjustments ,they did an insert instead.” This means that the current salary could be evaluated by looking at the most recent row inserted into the table.
We can assume that an insert will auto-increment the id field in the table, which means that the row we want to use would be the maximum id for the row for each given user.
SELECT first_name, last_name, MAX(id) AS max_id FROM employees GROUP BY 1,2
Now that we have the corresponding maximum id, we can re-join it to the original table in a subquery in order to then identify the correct salary associated with the id in the sub-query.
ETL failures are common, and data engineers and data scientists need to know how to handle them.
The first thing to do when checking for errors is to test whether one can duplicate the said error.
Documenting errors can be integral for creating an efficient environment and avoiding the same mistakes in messing up the pipeline repeatedly. Document the error and the actions you are taking to inform people outside the development/engineering team.
Regression testing is a critical part of maintaining the integrity of a data warehouse. After any changes are made, regression testing is there to ensure that no unintended issues have arisen as a result of these modifications. There are typically two types of changes you’ll see in a data warehouse:
When creating regression tests, it’s important to consider the following:
These questions will help guide your testing and ensure that the data warehouse is functioning as expected after changes have been implemented.
OLAP, or Online Analytical Processing, is a data structure designed for fast on-demand processing. For example, if one collates data spread over a decade (i.e., sales numbers of multiple products), it might take a lot of processing power to fully scour all those years.
Because of the heavy load required, real-time solutions and insights are almost impossible in large data sets. However, OLAP allows us to pre-process data, leading to sped up processing times and narrowing the time gap.
The question of whether to embrace OLAP cubes versus exploring alternative approaches is no small matter. The answer hinges on the specific characteristics of your data and the unique demands of your situation. In the realm of large businesses, traditional databases often struggle to generate reports with the necessary speed to address critical business challenges.
OLAP presents a compelling solution by allowing for the pre-aggregation and pre-computation of data, enabling swift query responses. As such, a great use case of OLAP is when your business environment has structured analytics needs, such as in the fields of accounting and finance.
However, it’s important to consider potential hurdles when employing OLAP. Flexibility in data analysis can pose challenges, as modifications to OLAP cubes typically require IT involvement, potentially slowing down progress. Moreover, constructing an OLAP structure that effectively satisfies analytics needs can be complex in business environments where data requirements are less clearly defined.
In a business environment that needs real-time data, OLAP is simply not the answer. While able to produce speedy queries, the data is certainly not produced real-time.
You might start with some clarifying questions about the table size and composition of the dataset. A simple process you could use would be to:
For this problem, we use the
INSERT INTO statement to add rows into the
lifetime_plays table. If we set this query to run daily, it becomes a daily ETL process. We would also want to make sure that:
song_id, and counts the columns from the
song_playstable for the current date.
GROUP BYbecause we want to have a separate count for every unique date,
When working with Big Data, it is common to encounter data sets larger than our machine’s memory. You might even be working with datasets that are double or triple your computer’s memory. The key here is to remember that we don’t have to pull all the data in one go, especially when there are robust tools designed to handle such scenarios. Enter the age-old solution: streaming.
When dealing with large datasets, it is better to use streaming in order to chunk-down your dataset into manageable pieces. Big Data frameworks such as Hadoop and Spark can help you streamline this process.
Aside from these Big Data frameworks, you can still adopt the traditional approach of fetching and processing data in smaller batches. SQL query optimizations using
OFFSET clauses can help you retrieve a portion of the records, allowing you to iterate through your data in manageable chunks instead of trying to load everything into memory all at once.
More context.You’ve been asked to create a report that aggregates sales data by both quarter and region for a multinational company using SQL. The original data is stored across multiple databases housed with each country-level operations department. What would be your approach?
As an alternative, consider leveraging data marts or Online Analytical Processing (OLAP) systems if they house the required data per region. This method is more efficient compared to resource-intensive processes such as streaming and the use of Big Data frameworks.
Furthermore, distributing the reports across several data marts can provide easy access for each region’s analytics team. The structure of these data marts can vary depending on specific business requirements. However, it’s critical to ensure that they are kept in sync with the centralized data warehouse. This synchronization can be achieved by setting up data pipelines to update the data marts whenever new data is loaded into the warehouse, ensuring consistency across all platforms.
More context. Consider a SQL database where the table logging user activity grows very rapidly. Over time, the table becomes so large that it begins to negatively impact the performance of the queries. How would you handle this scenario in the context of ETL and data warehousing?
To handle rapidly growing SQL tables in the context of ETL and data warehousing, you can employ several strategies to improve performance. For example, partitioning is a technique that involves dividing the large table into smaller partitions based on a specified criterion. Another helpful strategy is indexing, which allows queries to run faster, reducing execution time.
Archiving, or purging, older data that is no longer actively used for reporting or analysis can also help manage the table size. Moving these older records to an archival database or storage system reduces the data processed during queries, leading to improved performance.
For analytics and reporting, the use of summary tables, OLAP cubes, and data warehousing can help reduce the query times. As a last resort, scaling up or scaling out the database infrastructure can accommodate the growing load. Scaling up involves upgrading hardware resources, while scaling out involves distributing the load across multiple servers or utilizing sharding techniques.
Hard ETL questions provide more challenging scenarios and relate to more complex data types like multimedia information. Hard ETL questions are typically reserved for developer and engineer roles and assess the specialized knowledge needed for those roles.
Video data is difficult to aggregate, and the techniques used for video data differ greatly from text-based data. Three steps you might propose for working with video and multimedia data are:
The first level, primary metadata collection and indexing, refers to aggregating the video metadata and then indexing it.
The second level is user-generated content tagging. This is typically done manually but can be scaled with machine learning.
The last and most complex step is the binary-level collection. This process concerns analyzing and aggregating binary data (which often needs coding).
Dynamically changing schemas are troublesome and can create problems for an ETL developer. Changes in the schema, data, or transformation modules are both high-cost and high-risk, especially when you are dealing with massive databases or when the wrong approach is pursued.
How then do you manage pipelines involving a changing schema and active data modifications? Such a question is relatively easy to phrase but is challenging to visualize correctly. It is helpful to visualize the problem with an example:
RAWdatabase needs to be transformed through some code, most often Python. This process can include the cleaning, validation, and pushing of said processed data into our second database, which we will name
OUTis the database designed to collate all processed data from RAW. Data from
OUTwill be prepared and transported to an SQL database.
RAWdatabase, including schema changes, such as the changing of values, attributes, formats, and adding or reducing fields.
OUTdatabase to be consistent whenever a change occurs.
There are many ways to approach this problem, one of which is through CI/CD. CI/CD stands for continuous implementation and continuous development.
Knowing that changing or modifying anything in the
RAW database can make or break a whole ecosystem of data, it is imperative to use processes that come with many safety measures and are not too bold during implementation. CI/CD allows you to observe your changes and see how the changes in the
RAW database affect the
OUT database. Typically, small increments of code are pushed out into a build server, within which exist automated checking areas that scan the parameters and changes of the released code.
After the checks, the ETL developer will look at the feedback and sift through the test cases, evaluating whether the small increment succeeded or failed. This process allows you to check how the changes in the
RAW database affect those in the
OUT database. Because the changes are incremental, they will rarely be catastrophic if they do evade your checks and negatively impact the database, as they will be easily reversible.
Typically, after raw data goes through the transformation pipeline, there will be metadata left which can include a hash of the source in
RAW, the dates and time it was processed, and even the version of code you are using.
After that, use an orchestrator script that checks for new data added to the
RAW database. The orchestrator script can also check for any modifications made to the
RAW database, including additions and reductions. The script then gives a list of updates to the transformation pipeline, which processes the changes iteratively (remember: CI/CD).
Suppose you’re a data engineer at Spotify tasked with assisting the analytics team in creating reports. These reports aim to analyze the correlation between a song’s audio characteristics and the playtime of curated playlists it features in. How would you approach constructing an ETL pipeline for this scenario?
Creating an ETL pipeline for processing audio data in a context such as Spotify requires a strategic approach:
To ensure that this pipeline is reliable and can handle the constant influx of new data, we would design it to be robust, scalable, and automated. We might use tools like Apache Airflow for orchestrating these workflows, as it allows us to schedule and monitor workflows and ensure that the data pipeline is working as expected. To handle potential issues and troubleshoot effectively, efficient error handling and logging processes should be in place. Tools such as ELK (Elasticsearch, Logstash, Kibana) Stack could be useful for managing and monitoring logs.
More context. Let’s say that you’re in charge of getting payment data into your internal data warehouse.
How would you build an ETL pipeline to get Stripe payment data into the database so that analysts can build revenue dashboards and run analytics?
Context. Let’s say you work for an e-commerce company, where vendors can send products to the company’s warehouse to be listed on the website. Users are able to order any in-stock products and submit refundable returns if they’re not satisfied.
The front end of the website includes a vendor portal that provides sales data in daily, weekly, monthly, quarterly, and yearly intervals.
The company wants to expand worldwide. They put you in charge of designing its end-to-end architecture, so you need to know what significant factors to consider before you start building. What clarifying questions would you ask?
What kind of end-to-end architecture would you design for this company (both for ETL and reporting)?
You are a data engineer for Slack and are tasked to design their new product, “Slack for School”. When designing their database, you ponder upon the following questions:
The first step to design a database is to identify the critical entities, I.E., the core building blocks for the eventual success of the product. Identifying critical entities in a database allows you to build features around them, and it will be these product features that teachers and students interact with.
For a product like “Slack for School”, we can identify the following entities:
? Relations as a term can be used interchangeably with “tables”.
|attribute name||data type||constraints|
|UserID||BIGINT||PK, Auto Increment|
When answering for an interview, always explain the thought process behind your choices. Let’s consider the following explanation of the User entity:
“*While this looks like a standard user table, there are a few points of interest here. For example, we use BIGINT instead of INT to account for users above the two million mark (the max value for SQL’s INT).
In a service that’s built to handle several million users, we must design the product to scale up in the future”.*
For more comprehensive control around our users, we incorporate the inheritance technique. User is a super entity, while it has student and teacher as a sub-entity. This allows us to highlight the similarities and differences between the student and teacher entities.
We can differentiate the two sub-entities through our
UserTypeattribute. We assign 1 for students and 2 for teachers. Because we used TINYINT for this field, it becomes relatively efficient in space, while also allowing for up to 256 user types (0-255).*
Typically, we define inheritance in our ERD like this:
Other critical entities include:
When explaining these entities during an interview, follow a similar approach as with the User entity by providing insights into the design choices and their implications on product use.
We would first need to extract the data from the Interaction entity in our database. While we could theoretically assign a separate relation for each type of interaction, in order to maintain a simplified schema and reduce the need for multiple joins, we’ve decided to leverage a general ‘Interactions’ relation.
To define “participation”, we’ll focus on specific types of interactions, such as posting a message, submitting an assignment, or viewing course materials.
During the design process, it is important to acknowledge that as our service attracts an increasing number of users, the Interactions table, which has a one-to-many relationship per course, would benefit from the creation of an index.
Consider, for example, this index:
CREATE INDEX idx_interaction_course_id ON Interaction (Course_ID);
As our process might generate a substantial volume of queries (think in the thousands or even millions) to deliver these insights, ensuring our table is appropriately indexed can significantly enhance performance.
The extracted data might need some cleaning or reformatting to be useful. For example, we might want to calculate the frequency of interaction for each student by day, week, or month.
Additionally, we may want to categorize different types of interactions. This could mean a category of “communication” for message posts, or “coursework” for assignment views and submissions.
Finally, we would load this transformed data into a new analytics database table, which we will call
ParticipationMetrics. Let’s consider the following schema:
|attribute name||data type||constraints|
|User ID (FK to Users)||INTEGER||FK|
|Course ID (FK to Courses)||INTEGER||FK|
This table would provide a high-level view of participation for each student in each course, and could be queried easily to provide insights for teachers. A real-world example for this table would look like the following:
|User ID||Course ID||Date||Interaction Category||Interaction Count|
ParticipationMetrics table’s schema is not optimal. To make sure that your queries are performant as well as normalized, transfer the “Interaction Category” column as a separate table and instead, have FKs that refer to each category.
This SQL query assumes that we have a Submission table as described under critical entities above, and that we are looking at the submission date to understand trends.
SELECT User_ID, Assignment_ID, -- change to FORMAT or DATE_TRUNC depending on your SQL engine. DATE_FORMAT(Submission_Date, '%Y-%m') AS Month, COUNT(*) AS Number_of_Submissions FROM Submission WHERE Submission_Date >= CURDATE() - INTERVAL 6 MONTH GROUP BY User_ID, Assignment_ID, Month ORDER BY User_ID, Assignment_ID, Month;
This query would provide a count of the number of submissions for each student (
User_ID), under each assignment (
Assignment_ID), by month for the last six months. It should provide a strong overview of assignment submission trends. If you want more granular aggregations to observe trends in much greater detail, you can modify the
DATE_FORMAT and the
GROUP BY clause.
? Note: Please replace the column and table names in the SQL query and the ETL process with your actual database schema.
More context.You work for a video streaming company. Content creators can upload videos to the platform, and viewers can watch, like, share, and comment on the videos. Additionally, content creators can monitor their video performance metrics (views, likes, shares, comments, etc.) on a dashboard.
The company has become increasingly popular and management is expecting a surge in their worldwide user base. They assign you to design a scalable, resilient, and performant architecture to accommodate this growth.
What clarifying questions would you ask? What architectural approach would you propose for the platform, taking into consideration data ingestion, processing, storage, and content delivery?
Architectural Approach: The architecture would likely be a distributed system, incorporating various technologies for different requirements. It might include:
The architecture should be designed to handle high loads and scale smoothly as the user base and content volume grow. It should also be resilient to failures, ensuring high availability and a good user experience.
You are a data engineer for a large bank. The bank wants to develop a customer relationship management (CRM) system to streamline their operations and provide better services to their customers. You are tasked with drafting the database design and ETL processes for this CRM system.
To handle real-time updates with thousands of transactions happening concurrently, we’d need a combination of proper database design and a transactional system. For the database, we’d want to leverage ACID (Atomicity, Consistency, Isolation, Durability) compliant databases to ensure data integrity during these transactions.
For the transactional system, we could utilize something like Apache Kafka or Google Cloud Pub/Sub. These systems can handle high-volume, real-time data feeds and ensure that all transactions are processed in real-time. Also, consider partitioning and sharding strategies to distribute the load, enabling your system to handle a higher volume of concurrent transactions.
In a real-time setup, the ETL process is often referred to as the “Streaming ETL” process. Here’s how it can be applied:
Data is extracted in real-time from the source, which is typically transactional data coming from an application. As each transaction is made, it is sent to a real-time data processing system such as Apache Kafka, Amazon Kinesis, or Google Cloud Pub/Sub.
This is where the real-time processing happens. Using stream processing technologies like Apache Flink, Apache Beam, or Spark Streaming, the incoming data stream is processed in real-time. For each incoming transaction, the system would:
At this stage, the system can also check if the new total exceeds the user’s threshold for that category.
If the new total spending exceeds the threshold, the system would then load this event into a separate real-time alerting system. This might involve publishing a message to another Kafka topic, sending an event to a real-time analytics platform like Google Analytics for Firebase, or calling an API to trigger a push notification or email alert.
Meanwhile, the updated total spending is also loaded back into the cache or database, ready for when the next transaction comes in.
This kind of “streaming ETL” pipeline allows for real-time data transformation and alerting. However, it also requires careful engineering and tooling to ensure data consistency and accuracy, and to handle large volumes of data efficiently.
? Note: in a real-time setup, the “load” phase doesn’t necessarily involve loading data into a traditional, static database. Instead, it often involves sending data to other real-time systems or services, or updating real-time stores of data.
Your organization is a leading online retailer operating in over 30 countries worldwide. The company’s current relational databases are struggling to keep up with the exponential growth in data from various sources including clickstream data, customer transaction data, inventory management data, social media feeds, and third-party data. To solve this, the company plans to migrate to a big data solution by creating a data lake and moving its data warehouse operations into a distributed database.
When choosing a technology stack for a data lake and distributed data warehouse, there are several key factors to consider. Firstly, scalability is essential to handle the growing data volume, requiring horizontal scaling capabilities. Secondly, the technology should be able to handle the high variety of data typically found in a data lake, including structured, semi-structured, and unstructured data.
Additionally, it’s important to consider factors such as data processing requirements (batch processing, real-time processing, or both), security compliance protocols to protect sensitive customer data, data cataloguing and quality tools for effective data management, and the total cost of ownership, including setup, maintenance, and scaling costs. Integration with existing systems and tools within the organization is also crucial for seamless operations. Lastly, a technology stack with a strong community and good vendor support can provide valuable resources and expertise for issue resolution and faster development.
Designing the ETL process for a data lake and a distributed data warehouse at this scale requires careful planning. Here’s how it could be approached:
Given the variety of data sources, you would need different data extraction tools. Apache NiFi, for instance, can connect to many data sources and stream data in real time.
Once the data is in the data lake, it can be transformed based on use cases. Some data might need to be cleaned, some aggregated, some joined, etc. Given the scale, distributed processing frameworks like Apache Spark would be useful for transformation. Tools like Apache Beam can handle both batch and stream data.
Finally, the cleaned and transformed data needs to be loaded into the distributed data warehouse. Depending on the choice of technology, different tools will be used. If you choose a Hadoop-based ecosystem, Apache Hive or Impala could be used. If you go with a cloud solution like BigQuery or Redshift, they provide their own tools for data loading.
The data lake could also serve as a staging area before data is loaded to the data warehouse. This way, raw data is always available and can be reprocessed if necessary.
Keep in mind that monitoring the ETL process will be crucial. Given the scale, even small issues can result in big data quality problems. Tools like Apache Airflow can be used to manage and monitor the ETL workflows.
Remember, with such a scale of data, the traditional ETL may evolve into ELT (Extract, Load, Transform), where data is loaded into the system first and then transformed as required. This is because it’s often more scalable to use the distributed power of the data lake or warehouse to do the transformation.
ETL is a commonly tested data engineer interview question subject. In addition to understanding common definitions, and scenario-based questions for ETL. You should also practice a variety of data engineer SQL questions, to build your competency in writing SQL queries for ETL processes. For more SQL help, premium members also get access to our data science course, which features a full module on SQL interview prep.
If you want to learn from the pros, watch Interview Query’s mock interview with an Amazon Data Engineer.