Data Engineering Flashcards
(38 cards)
What makes you the best candidate for this position?
If the hiring manager selects you for a phone interview, they must have seen something they liked in your profile. Approach this question with confidence and talk about your experience and career growth.
It is important to review the company’s profile and job description before the interview. Doing so will help you understand what the hiring manager is looking for and tailor your response accordingly.
Focus on specific skills and experiences aligning with the job requirements, such as designing and managing data pipelines, modeling, and ETL processes. Highlight how your unique combination of skills, experience, and knowledge makes you stand out.
What are the daily responsibilities of a data engineer?
While there is no absolute answer, sharing your experiences from previous jobs and referring to the job description can provide a comprehensive response. Generally, the daily responsibilities of data engineers include:
Developing, testing, and maintaining databases.
Creating data solutions based on business requirements.
Data acquisition and integration.
Developing, validating, and maintaining data pipelines for ETL processes, modeling, transformation, and serving.
Deploying and managing machine learning models in some cases.
Maintaining data quality by cleaning, validating, and monitoring data streams.
Improving system reliability, performance, and quality.
Following data governance and security guidelines to ensure compliance and data integrity.
What is the toughest thing you find about being a data engineer?
This question will vary based on individual experiences, but common challenges include:
Keeping up with the rapid pace of technological advancements and integrating new tools to enhance the performance, security, reliability, and ROI of data systems.
Understanding and implementing complex data governance and security protocols.
Managing disaster recovery plans and ensuring data availability and integrity during unforeseen events.
Balancing business requirements with technical constraints and predicting future data demands.
Handling large volumes of data efficiently and ensuring data quality and consistency.
What data tools or frameworks do you have experience with? Are there any you prefer over others?
Your answer will be based on your experiences. Being familiar with modern tools and third-party integrations will help you confidently respond to this question. Discuss tools related to:
Database management (e.g., MySQL, PostgreSQL, MongoDB)
Data warehousing (e.g., Amazon Redshift, Google BigQuery, Snowflake)
Data orchestration (e.g., Apache Airflow, Prefect)
Data pipelines (e.g., Apache Kafka, Apache NiFi)
Cloud management (e.g., AWS, Google Cloud Platform, Microsoft Azure)
Data cleaning, modeling, and transformation (e.g., pandas, dbt, Spark)
Batch and real-time processing (e.g., Apache Spark, Apache Flink)
Remember, there is no wrong answer to this question. The interviewer is assessing your skills and experience.
How do you stay updated with the latest trends and advancements in data engineering?
This question evaluates your commitment to continuous learning and staying current in your field.
You can mention subscribing to industry newsletters, following influential blogs, participating in online forums and communities, attending webinars and conferences, and taking online courses. Highlight specific sources or platforms you use to stay informed.
Can you describe a time when you had to collaborate with a cross-functional team to complete a project?
Data engineering often involves working with various teams, including data scientists, analysts, and IT staff.
Share a specific example where you successfully collaborated with others, emphasizing your communication skills, ability to understand different perspectives, and how you contributed to the project’s success. Explain the challenges you faced and how you overcame them to achieve the desired outcome.
Can you explain the design schemas relevant to data modeling?
There are three primary data modeling design schemas: star, snowflake, and galaxy.
Star schema: This schema contains various dimension tables connected to a central fact table. It is simple and easy to understand, making it suitable for straightforward queries.
Snowflake schema: An extension of the star schema, the snowflake schema consists of a fact table and multiple dimension tables with additional layers of normalization, forming a snowflake-like structure. It reduces redundancy and improves data integrity.
Galaxy schema: Also known as a fact constellation schema, it contains two or more fact tables that share dimension tables. This schema is suitable for complex database systems that require multiple fact tables.
Which ETL tools have you worked with? What is your favorite, and why?
When answering this question, mention the ETL tools you have mastered and explain why you chose specific tools for certain projects. Discuss the pros and cons of each tool and how they fit into your workflow. Popular open-source tools include:
dbt (data build tool): Great for transforming data in your warehouse using SQL.
Apache Spark: Excellent for large-scale data processing and batch processing.
Apache Kafka: Used for real-time data pipelines and streaming.
Airbyte: An open-source data integration tool that helps in data extraction and loading.
If you need to refresh your ETL knowledge, consider taking the Introduction to Data Engineering course.
What is data orchestration, and what tools can you use to perform it?
Data orchestration is an automated process for accessing raw data from multiple sources, performing data cleaning, transformation, and modeling techniques, and serving it for analytical tasks. It ensures that data flows smoothly between different systems and stages of processing.
Popular tools for data orchestration include:
Apache Airflow: Widely used for scheduling and monitoring workflows.
Prefect: A modern orchestration tool with a focus on data flow.
Dagster: An orchestration tool designed for data-intensive workloads.
AWS Glue: A managed ETL service that simplifies data preparation for analytics.
What tools do you use for analytics engineering?
Analytics engineering involves transforming processed data, applying statistical models, and visualizing it through reports and dashboards.
Popular tools for analytics engineering include:
dbt (data build tool): This is used to transform data in your warehouse using SQL.
BigQuery: A fully managed, serverless data warehouse for large-scale data analytics.
Postgres: A powerful, open-source relational database system.
Metabase: An open-source tool that lets you ask questions about your data and display the answers in understandable formats.
Google Data Studio: This is used to create dashboards and visual reports.
Tableau: A leading platform for data visualization.
These tools help access, transform, and visualize data to derive meaningful insights and support decision-making processes.
What is the difference between OLAP and OLTP systems?
OLAP (Online Analytical Processing) analyzes historical data and supports complex queries. It’s optimized for read-heavy workloads and is often used in data warehouses for business intelligence tasks. OLTP (Online Transaction Processing) is designed for managing real-time transactional data. It’s optimized for write-heavy workloads and is used in operational databases for day-to-day business operations.
The main difference lies in their purpose: OLAP supports decision-making, while OLTP supports daily operations.
If you still have doubts, I recommend reading the OLTP vs OLAP blog post.
Which Python libraries are most efficient for data processing?
The most popular data processing libraries in Python include:
pandas: Ideal for data manipulation and analysis, providing data structures like DataFrames.
NumPy: Essential for numerical computations, supporting large multi-dimensional arrays and matrices.
Dask: Facilitates parallel computing and can handle larger-than-memory computations using a familiar pandas-like syntax.
PySpark: A Python API for Apache Spark, useful for large-scale data processing and real-time analytics.
Each of these libraries has pros and cons, and the choice depends on the specific data requirements and the scale of the data processing tasks.
How do you perform web scraping in Python?
Web scraping in Python typically involves the following steps:
- Access the webpage using the requests library:
import requests
from bs4 import BeautifulSoup
url = ‘http://example.com’
response = requests.get(url)
soup = BeautifulSoup(response.text, ‘html.parser’)
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2. Extract tables and information using BeautifulSoup:
tables = soup.find_all(‘table’)
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3. Convert it into a structured format using pandas:
import pandas as pd
data = []
for table in tables:
rows = table.find_all(‘tr’)
for row in rows:
cols = row.find_all(‘td’)
cols = [ele.text.strip() for ele in cols]
data.append(cols)
df = pd.DataFrame(data)
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4. Clean the data using pandas and NumPy:
df.dropna(inplace=True) # Drop missing values
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5. Save the data in the form of a CSV file:
df.to_csv(‘scraped_data.csv’, index=False)
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In some cases, pandas.read_html can simplify the process:
df_list = pd.read_html(‘http://example.com’)
df = df_list[0] # Assuming the table of interest is the first one
How do you handle large datasets in Python that do not fit into memory?
Handling large datasets that do not fit into memory requires using tools and techniques designed for out-of-core computation:
Dask: Allows for parallel computing and works with larger-than-memory datasets using a pandas-like syntax.
import dask.dataframe as dd
df = dd.read_csv(‘large_dataset.csv’)
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PySpark: Enables distributed data processing, which is useful for handling large-scale data.
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName(‘data_processing’).getOrCreate()
df = spark.read.csv(‘large_dataset.csv’, header=True, inferSchema=True)
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Chunking with pandas: Read large datasets in chunks.
import pandas as pd
chunk_size = 10000
for chunk in pd.read_csv(‘large_dataset.csv’, chunksize=chunk_size):
process(chunk) # Replace with your processing function
How do you ensure your Python code is efficient and optimized for performance?
To ensure Python code is efficient and optimized for performance, consider the following practices:
Profiling: Use profiling tools like cProfile, line_profiler, or memory_profiler to identify bottlenecks in your code.
import cProfile
cProfile.run(‘your_function()’)
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Vectorization: Use numpy or pandas for vectorized operations instead of loops.
import numpy as np
data = np.array([1, 2, 3, 4, 5])
result = data * 2 # Vectorized operation
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Efficient data structures: Choose appropriate data structures (e.g., lists, sets, dictionaries) based on your use case.
data_dict = {‘key1’: ‘value1’, ‘key2’: ‘value2’} # Faster lookups compared to lists
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Parallel processing: Utilize multi-threading or multi-processing for tasks that can be parallelized.
from multiprocessing import Pool
def process_data(data_chunk):
# Your processing logic here
return processed_chunk
with Pool(processes=4) as pool:
results = pool.map(process_data, data_chunks)
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Avoiding redundant computations: Cache results of expensive operations if they need to be reused.
from functools import lru_cache
@lru_cache(maxsize=None)
def expensive_computation(x):
# Perform expensive computation
return result
How do you ensure data integrity and quality in your data pipelines?
Data integrity and quality are important for reliable data engineering. Best practices include:
Data validation: Implement checks at various stages of the data pipeline to validate data formats, ranges, and consistency.
def validate_data(df):
assert df[‘age’].min() >= 0, “Age cannot be negative”
assert df[‘salary’].dtype == ‘float64’, “Salary should be a float”
# Additional checks...
Data cleaning: Use libraries like pandas to clean and preprocess data by handling missing values, removing duplicates, and correcting errors.
df.dropna(inplace=True) # Drop missing values
df.drop_duplicates(inplace=True) # Remove duplicates
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Automated testing: Develop unit tests for data processing functions using frameworks like pytest.
import pytest
def test_clean_data():
raw_data = pd.DataFrame({‘age’: [25, -3], ‘salary’: [‘50k’, ‘60k’]})
clean_data = clean_data_function(raw_data)
assert clean_data[‘age’].min() >= 0
assert clean_data[‘salary’].dtype == ‘float64’
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Monitoring and alerts: Set up monitoring for your data pipelines to detect anomalies and send alerts when data quality issues arise.
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from airflow.operators.email_operator import EmailOperator
# Define your DAG and tasks…
How do you handle missing data in your datasets?
Handling missing data is a common task in data engineering. Approaches include:
Removal: Simply remove rows or columns with missing data if they are not significant.
df.dropna(inplace=True)
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Imputation: Fill missing values with statistical measures (mean, median) or use more sophisticated methods like KNN imputation.
df[‘column’].fillna(df[‘column’].mean(), inplace=True)
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Indicator variable: Add an indicator variable to specify which values were missing.
df[‘column_missing’] = df[‘column’].isnull().astype(int)
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Model-based imputation: Use predictive modeling to estimate missing values.
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=5)
df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
How do you handle API rate limits when fetching data in Python?
To handle API rate limits, there are strategies such as:
Backoff and retry: Implementing exponential backoff when rate limits are reached.
Pagination: Fetching data in smaller chunks using the API’s pagination options.
Caching: Storing responses to avoid redundant API calls.
Example using Python’s time library and the requests module:
import time
import requests
def fetch_data_with_rate_limit(url):
for attempt in range(5): # Retry up to 5 times
response = requests.get(url)
if response.status_code == 429: # Too many requests
time.sleep(2 ** attempt) # Exponential backoff
else:
return response.json()
raise Exception(“Rate limit exceeded”)
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Python is undoubtedly one of the most important languages in data engineering. You can hone your skills by taking our Data Engineer in Python track, which covers a comprehensive curriculum to equip you with modern data engineering concepts, programming languages, tools, and frameworks.
What are Common Table Expressions (CTEs) in SQL?
CTEs are used to simplify complex joins and run subqueries. They help make SQL queries more readable and maintainable. Here’s an example of a CTE that displays all students with Science majors and grade A:
SELECT *
FROM class
WHERE id IN (
SELECT DISTINCT id
FROM students
WHERE grade = “A”
AND major = “Science”
);
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Using a CTE, the query becomes:
WITH temp AS (
SELECT id
FROM students
WHERE grade = “A”
AND major = “Science”
)
SELECT *
FROM class
WHERE id IN (SELECT id FROM temp);
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CTEs can be used for more complex problems and multiple CTEs can be chained together.
How do you rank the data in SQL?
Data engineers commonly rank values based on parameters such as sales and profit. The RANK() function is used to rank data based on a specific column:
SELECT
id,
sales,
RANK() OVER (ORDER BY sales DESC) AS rank
FROM bill;
Can you create a simple temporary function and use it in an SQL query?
Like in Python, you can create functions in SQL to make your queries more elegant and avoid repetitive case statements. Here’s an example of a temporary function get_gender:
CREATE TEMPORARY FUNCTION get_gender(type VARCHAR) RETURNS VARCHAR AS (
CASE
WHEN type = “M” THEN “male”
WHEN type = “F” THEN “female”
ELSE “n/a”
END
);
SELECT
name,
get_gender(type) AS gender
FROM class;
How do you add subtotals in SQL?
Adding subtotals can be achieved using the GROUP BY and ROLLUP() functions. Here’s an example:
SELECT
department,
product,
SUM(sales) AS total_sales
FROM sales_data
GROUP BY ROLLUP(department, product);
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This query will give you a subtotal for each department and a grand total at the end.
How do you handle missing data in SQL?
Handling missing data is essential for maintaining data integrity. Common approaches include:
Using COALESCE(): This function returns the first non-null value in the list.
SELECT id, COALESCE(salary, 0) AS salary FROM employees;
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Using CASE statements: To handle missing values conditionally.
SELECT id,
CASE
WHEN salary IS NULL THEN 0
ELSE salary
END AS salary
FROM employees;
How do you perform data aggregation in SQL?
Data aggregation involves using aggregate functions like SUM(), AVG(), COUNT(), MIN(), and MAX(). Here’s an example:
SELECT department,
SUM(salary) AS total_salary,
AVG(salary) AS average_salary,
COUNT(*) AS employee_count
FROM employees
GROUP BY department;
