Frequently asked SQL interview questions for Data Analyst/Data Engineer role-

1 - What is SQL and what are its main features?
2 - Order of writing SQL query?
3- Order of execution of SQL query?
4- What are some of the most common SQL commands?
5- What’s a primary key & foreign key?
6 - All types of joins and questions on their outputs?
7 - Explain all window functions and difference between them?
8 - What is stored procedure?
9 - Difference between stored procedure & Functions in SQL?
10 - What is trigger in SQL?
11 - Difference between where and having?

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1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?

2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?

3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.

4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?

5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?

6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.

7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.

8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?

9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?

10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?

11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.

12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?

13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?

14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?

15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.

16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?

17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?

18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?

19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.

20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?

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Data Analyst vs Data Engineer: Must-Know Differences

Data Analyst:
- Role: Focuses on analyzing, interpreting, and visualizing data to extract insights that inform business decisions.
- Best For: Those who enjoy working directly with data to find patterns, trends, and actionable insights.
- Key Responsibilities:
- Collecting, cleaning, and organizing data.
- Using tools like Excel, Power BI, Tableau, and SQL to analyze data.
- Creating reports and dashboards to communicate insights to stakeholders.
- Collaborating with business teams to provide data-driven recommendations.
- Skills Required:
- Strong analytical skills and proficiency with data visualization tools.
- Expertise in SQL, Excel, and reporting tools.
- Familiarity with statistical analysis and business intelligence.
- Outcome: Data analysts focus on making sense of data to guide decision-making processes in business, marketing, finance, etc.

Data Engineer:
- Role: Focuses on designing, building, and maintaining the infrastructure that allows data to be stored, processed, and analyzed efficiently.
- Best For: Those who enjoy working with the technical aspects of data management and creating the architecture that supports large-scale data analysis.
- Key Responsibilities:
- Building and managing databases, data warehouses, and data pipelines.
- Developing and maintaining ETL (Extract, Transform, Load) processes to move data between systems.
- Ensuring data quality, accessibility, and security.
- Working with big data technologies like Hadoop, Spark, and cloud platforms (AWS, Azure, Google Cloud).
- Skills Required:
- Proficiency in programming languages like Python, Java, or Scala.
- Expertise in database management and big data tools.
- Strong understanding of data architecture and cloud technologies.
- Outcome: Data engineers focus on creating the infrastructure and pipelines that allow data to flow efficiently into systems where it can be analyzed by data analysts or data scientists.

Data analysts work with the data to extract insights and help make data-driven decisions, while data engineers build the systems and infrastructure that allow data to be stored, processed, and analyzed. Data analysts focus more on business outcomes, while data engineers are more involved with the technical foundation that supports data analysis.

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Azure Data Engineer interview questions:

Project Questions:

1) Tell me about your project. Explain it end to end.
2) On what measure you have mentioned about the storage cost decreased and cost optimization has been done?

ADF and ADLS

1) There are 10 million CSV files in ADLS Gen2. How will you read them and process using Azure Data Factory? Explain in detail what and all would be used.
2) What is Integration Runtime?
3) What is variable and parameter in ADF?
4) Explain different activities and brief about what each activity does.
5) The pipeline is scheduled and it got failed. I need to automate it by sending the mail when it gets failed. How would you implement it?
6) How do you handle the exceptions in ADF?
7) If the ADF pipeline is running very slow, how would you approach and fix it?
8) What is the difference between Blob storage and ADLS Gen2? Why ADLS Gen2 is required?

Azure Databricks

9) How do you connect ADLS Gen 2 with databricks? In where we mention the role assignments?
10) If you are using Service Principal to connect with ADLS from Azure Databricks explain the steps and how would you code it?
11) Why using service principal? How would you create it?
12) What is Databricks runtime? Why we need it?
13) What are Workflows?
14) Explain about the Medallion Architecture in brief.
15) Explain about Delta file format briefly.
16) Consider you are working in Facebook. User is writing data for each record. Since each record is been written, a new json transaction log will get created for each write. But we can use Datalake only right. Why do we require delta file format? It can decrease the performance every now and then right?
17) Consider a job is running very slow in Azure Databricks. How would you approach the issue and make it faster?
18) What are the optimization techniques you have worked on? Explain them in brief.
19) How would you optimize the job with respect to memory management in azure databricks?

DevOps Tech Stack

Data Lake vs Data Warehouse

Data Science Techniques

Few topics that you need to cover for Kafka interview:

1. Topic
- Partition
- Message ordering
- Replication
- Offset
- Compression

2. Producer
- Serialization
- Batching
- Compaction
- Intervals
- Sync & Async
- Idempotence
- Some important properties

3. Broker
- Kafka cluster
- Replication
- Retention
- Cleanup
- Graceful shut down

4. Consumer
- Deserialization
- Consumer group
- Consumption types
- Sync & Async
- Failure handling
- Some important properties

5. Zookeeper
6. Schema registry
7. Admin client API, MakeMirror
8. Kafka Streams
9. Kafka Connect

🚦Top 10 Data Science Tools🚦

Here we will examine the top best Data Science tools that are utilized generally by data researchers and analysts. But prior to beginning let us discuss about what is Data Science.

🛰What is Data Science ?

Data science is a quickly developing field that includes the utilization of logical strategies, calculations, and frameworks to extract experiences and information from organized and unstructured data .

🗽Top Data Science Tools that are normally utilized :

1.) Jupyter Notebook : Jupyter Notebook is an open-source web application that permits clients to make and share archives that contain live code, conditions, representations, and narrative text .

2.) Keras : Keras is a famous open-source brain network library utilized in data science. It is known for its usability and adaptability.
Keras provides a range of tools and techniques for dealing with common data science problems, such as overfitting, underfitting, and regularization.

3.) PyTorch : PyTorch is one more famous open-source AI library utilized in information science. PyTorch also offers easy-to-use interfaces for various tasks such as data loading, model building, training, and deployment, making it accessible to beginners as well as experts in the field of machine learning.

4.) TensorFlow : TensorFlow allows data researchers to play out an extensive variety of AI errands, for example, image recognition , natural language processing , and deep learning.

5.) Spark : Spark allows data researchers to perform data processing tasks like data control, investigation, and machine learning , rapidly and effectively.

6.) Hadoop : Hadoop provides a distributed file system (HDFS) and a distributed processing framework (MapReduce) that permits data researchers to handle enormous datasets rapidly.

7.) Tableau : Tableau is a strong data representation tool that permits data researchers to make intuitive dashboards and perceptions. Tableau allows users to combine multiple charts.

8.) SQL : SQL (Structured Query Language) SQL permits data researchers to perform complex queries , join tables, and aggregate data, making it simple to extricate bits of knowledge from enormous datasets. It is a powerful tool for data management, especially for large datasets.

9.) Power BI : Power BI is a business examination tool that conveys experiences and permits clients to make intuitive representations and reports without any problem.

10.) Excel : Excel is a spreadsheet program that broadly utilized in data science. It is an amazing asset for information the board, examination, and visualization .Excel can be used to explore the data by creating pivot tables, histograms, scatterplots, and other types of visualizations.

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1. Startup to Dream Job Journey:
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- Shifted from legacy Mainframe to AWS Cloud, pursued Master's from illinoisstateu, and secured dream job at Statefarm.
2. Learn Fundamentals:
- Assess skills, understand role.
- Gain proficiency in Python, SQL.
- Learn data technologies.
3. Database and Modeling Skills:
- Understand databases, gain proficiency.
- Learn data modeling principles.
4. Master ETL, Warehousing, and Visualization:
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- Gain experience in building warehouses.
- Familiarize with visualization tools.
- Got Certified as AWS Solutions Architect.
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- Showcase skills and achievements.
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SQL Interview Questions for 0-1 year of Experience (Asked in Top Product-Based Companies).

Sharpen your SQL skills with these real interview questions!

Q1. Customer Purchase Patterns -
You have two tables, Customers and Purchases: CREATE TABLE Customers ( customer_id INT PRIMARY KEY, customer_name VARCHAR(255) ); CREATE TABLE Purchases ( purchase_id INT PRIMARY KEY, customer_id INT, product_id INT, purchase_date DATE );
Assume necessary INSERT statements are already executed.
Write an SQL query to find the names of customers who have purchased more than 5 different products within the last month. Order the result by customer_name.

Q2. Call Log Analysis -
Suppose you have a CallLogs table: CREATE TABLE CallLogs ( log_id INT PRIMARY KEY, caller_id INT, receiver_id INT, call_start_time TIMESTAMP, call_end_time TIMESTAMP );
Assume necessary INSERT statements are already executed.
Write a query to find the average call duration per user. Include only users who have made more than 10 calls in total. Order the result by average duration descending.

Q3. Employee Project Allocation - Consider two tables, Employees and Projects:
CREATE TABLE Employees ( employee_id INT PRIMARY KEY, employee_name VARCHAR(255), department VARCHAR(255) ); CREATE TABLE Projects ( project_id INT PRIMARY KEY, lead_employee_id INT, project_name VARCHAR(255), start_date DATE, end_date DATE );
Assume necessary INSERT statements are already executed.
The goal is to write an SQL query to find the names of employees who have led more than 3 projects in the last year. The result should be ordered by the number of projects led.

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