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?

DevOps Tech Stack

For all Data Engineers out there, here is The State of Data Engineering 2024

Some of the highlights:

✅ More and more, data observability tools are used not just to monitor data sources, but also the infrastructure, pipelines, and systems after data is collected.

✅ Companies are now seeing data observability as essential for their AI projects. Gartner has called it a must-have for AI-ready data.

✅ Like in 2023, Monte Carlo is leading in this area, with G2 naming them the #1 Data Observability Platform. Big organizations like Cisco, American Airlines, and NASDAQ use Monte Carlo to make their AI systems more reliable.

Data Engineering Interview Questions 🔥🔥🔥

React ❤️ if you want more content like this

Learning SQL is actually a really good skill. It's not just learning SQL the language, but learning the concepts of relational algebra and how to think about data sets, designing schemas, and organizing data.

...

It is about learning the file formatting and the basics of data storage, data partitioning, and the relationship between the execution engines. All of these things will yield you to be a better DBT user, a better Snowflake user or a Databricks user.

The number one thing to do as a data engineer? Create high-quality data that people can trust.🤝

Life of a Data Engineer.....


Business user : Can we add a filter on this dashboard. This will help us track a critical metric.
me : sure this should be a quick one.

Next day :

I quickly opened the dashboard to find the column in the existing dashboard's data sources. -- column not found

Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..).

Then comes the pipeline changes , data model changes , dashboard changes , validation/testing.

Finally deploying to production and a simple email to the user that the filter has been added.

A small change in the front end but a lot of work in the backend to bring that column to life.

Never underestimate data engineers and data pipelines 💪

Data Engineering is not Excel. Not writing ML models. Not “please can you do this quick? I need it asap”

Complete Python topics required for the Data Engineer role:

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:

- Python Syntax
- Data Types
- Lists
- Tuples
- Dictionaries
- Sets
- Variables
- Operators
- Control Structures:
- if-elif-else
- Loops
- Break & Continue try-except block
- Functions
- Modules & Packages

➤ 𝗣𝗮𝗻𝗱𝗮𝘀:

- What is Pandas & imports?
- Pandas Data Structures (Series, DataFrame, Index)
- Working with DataFrames:
-> Creating DFs
-> Accessing Data in DFs Filtering & Selecting Data
-> Adding & Removing Columns
-> Merging & Joining in DFs
-> Grouping and Aggregating Data
-> Pivot Tables

- Input/Output Operations with Pandas:
-> Reading & Writing CSV Files
-> Reading & Writing Excel Files
-> Reading & Writing SQL Databases
-> Reading & Writing JSON Files
-> Reading & Writing - Text & Binary Files

➤ 𝗡𝘂𝗺𝗽𝘆:

- What is NumPy & imports?
- NumPy Arrays
- NumPy Array Operations:
- Creating Arrays
- Accessing Array Elements
- Slicing & Indexing
- Reshaping, Combining & Arrays
- Arithmetic Operations
- Broadcasting
- Mathematical Functions
- Statistical Functions

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗡𝘂𝗺𝗽𝘆 are more than enough for Data Engineer role.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Preparing for a Spark Interview? Here are 20 Key Differences You Should Know!

1️⃣ Repartition vs. Coalesce: Repartition changes the number of partitions, while coalesce reduces partitions without full shuffle.

2️⃣ Sort By vs. Order By: Sort By sorts data within each partition and may result in partially ordered final results if multiple reducers are used. Order By guarantees total order across all partitions in the final output.

3️⃣ RDD vs. Datasets vs. DataFrames: RDDs are the basic abstraction, Datasets add type safety, and DataFrames optimize for structured data.

4️⃣ Broadcast Join vs. Shuffle Join vs. Sort Merge Join: Broadcast Join is for small tables, Shuffle Join redistributes data, and Sort Merge Join sorts data before joining.

5️⃣ Spark Session vs. Spark Context: Spark Session is the entry point in Spark 2.0+, combining functionality of Spark Context and SQL Context.

6️⃣ Executor vs. Executor Core: Executor runs tasks and manages data storage, while Executor Core handles task execution.

7️⃣ DAG vs. Lineage: DAG (Directed Acyclic Graph) is the execution plan, while Lineage tracks the RDD lineage for fault tolerance.

8️⃣ Transformation vs. Action: Transformation creates RDD/Dataset/DataFrame, while Action triggers execution and returns results to driver.

9️⃣ Narrow Transformation vs. Wide Transformation: Narrow operates on single partition, while Wide involves shuffling across partitions.

🔟 Lazy Evaluation vs. Eager Evaluation: Spark delays execution until action is called (Lazy), optimizing performance.

1️⃣1️⃣ Window Functions vs. Group By: Window Functions compute over a range of rows, while Group By aggregates data into summary.

1️⃣2️⃣ Partitioning vs. Bucketing: Partitioning divides data into logical units, while Bucketing organizes data into equal-sized buckets.

1️⃣3️⃣ Avro vs. Parquet vs. ORC: Avro is row-based with schema, Parquet and ORC are columnar formats optimized for query speed.

1️⃣4️⃣ Client Mode vs. Cluster Mode: Client runs driver in client process, while Cluster deploys driver to the cluster.

1️⃣5️⃣ Serialization vs. Deserialization: Serialization converts data to byte stream, while Deserialization reconstructs data from byte stream.

1️⃣6️⃣ DAG Scheduler vs. Task Scheduler: DAG Scheduler divides job into stages, while Task Scheduler assigns tasks to workers.

1️⃣7️⃣ Accumulators vs. Broadcast Variables: Accumulators aggregate values from workers to driver, Broadcast Variables efficiently broadcast read-only variables.

1️⃣8️⃣ Cache vs. Persist: Cache stores RDD/Dataset/DataFrame in memory, Persist allows choosing storage level (memory, disk, etc.).

1️⃣9️⃣ Internal Table vs. External Table: Internal managed by Spark, External managed externally (e.g., Hive).

2️⃣0️⃣ Executor vs. Driver: Executor runs tasks on worker nodes, Driver manages job execution.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐒𝐩𝐚𝐫𝐤 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

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?

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Cisco Kafka interview questions for Data Engineers 2024.

➤ How do you create a topic in Kafka using the Confluent CLI?
➤ Explain the role of the Schema Registry in Kafka.
➤ How do you register a new schema in the Schema Registry?
➤ What is the importance of key-value messages in Kafka?
➤ Describe a scenario where using a random key for messages is beneficial.
➤ Provide an example where using a constant key for messages is necessary.
➤ Write a simple Kafka producer code that sends JSON messages to a topic.
➤ How do you serialize a custom object before sending it to a Kafka topic?
➤ Describe how you can handle serialization errors in Kafka producers.
➤ Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
➤ How do you handle deserialization errors in Kafka consumers?
➤ Explain the process of deserializing messages into custom objects.
➤ What is a consumer group in Kafka, and why is it important?
➤ Describe a scenario where multiple consumer groups are used for a single topic.
➤ How does Kafka ensure load balancing among consumers in a group?
➤ How do you send JSON data to a Kafka topic and ensure it is properly serialized?
➤ Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
➤ Explain how you can work with CSV data in Kafka, including serialization and deserialization.
➤ Write a Kafka producer code snippet that sends CSV data to a topic.
➤ Write a Kafka consumer code snippet that reads and processes CSV data from a topic.

Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍