- PySpark + DataFrame API = Data Manipulation
- PySpark + RDD = Distributed Datasets
- PySpark + filter() = Data Filtering
- PySpark + join() = Data Integration
- PySpark + groupBy() = Data Aggregation
- PySpark + orderBy() = Data Sorting
- PySpark + union() = Combining Datasets
- PySpark + withColumn() = Data Transformation
- PySpark + select() = Column Selection
- PySpark + SQL Queries = SQL Integration
- PySpark + createOrReplaceTempView() = Virtual Tables
- PySpark + map() = Data Mapping
- PySpark + reduceByKey() = Data Reduction
- PySpark + partitionBy() = Data Partitioning
- PySpark + broadcast() = Data Broadcasting
- PySpark + accumulators = Shared Variables
- PySpark + Spark SQL = Structured Data
- PySpark + DataFrame Caching = Performance Optimization
- PySpark + Window Functions = Advanced Analytics
- PySpark + UDFs = Custom Functions
- PySpark + Machine Learning = Scalable Models
- PySpark + GraphX = Graph Processing
- PySpark + Streaming = Real-Time Processing
- PySpark + DataFrame Joins = Efficient Merging
- PySpark + MLlib = Machine Learning
- PySpark + Structured Streaming = Continuous Processing
- PySpark + Pipeline API = Workflow Automation
- PySpark + Delta Lake = Reliable Lakes
- PySpark + Databricks = Cloud Platform
- PySpark + ETL Pipelines = Data Extraction
- PySpark + Performance Tuning = Query Efficiency
- PySpark + Cluster Management = Distributed Computing

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

🚀 SQL Essentials for Data Engineers:

Joins & Subqueries – Master INNER, LEFT, RIGHT, CROSS joins.

Window Functions – Use ROW_NUMBER(), RANK(), LAG() for analytics.

CTEs & Temp Tables – Write cleaner queries with WITH.

Performance Tuning – Optimize with indexes & execution plans.

ACID Transactions – Ensure consistency with COMMIT & ROLLBACK.

Normalization – Balance efficiency with normal vs. denormal forms.

Master these, and you're golden! 💡

#SQL #DataEngineering

𝟱 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Whether you’re a complete beginner or looking to level up, these courses cover Excel, Power BI, Data Science, and Real-World Analytics Projects to make you job-ready.

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Part 1: Basic Concepts and Architecture

1. What is a stream in Snowflake, and what are the columns present in a stream?
2. What is the architecture of Snowflake?
3. What is a Snowpipe in the context of Snowflake?
4. Can you explain the concept of a warehouse in Snowflake?
5. What is the data flow, and how many layers are in our projects?
6. How do you convert JSON to the Snowflake VARIANT data type?
7. How are task dependencies managed in Snowflake?
8. Is there a specific table for maintaining notification history in Snowflake?
9. What are alternative methods for loading data into Snowflake without using JSON functions?
10. How can you set up error notifications in Snowflake?

Part 2: Data Management and ETL Processes

1. Could you explain the process of data sharing in Snowflake?
2. Explain the relationship between AWS and SF.
3. How do you move 100 GB of data into SF? Describe the steps you would follow.
4. Differentiate between a View and a Materialized View.
5. Explain the concept of a Merge statement in the context of a relational database.
6. What is the purpose of the pattern function in Snowflake?
7. Have you worked with Snowpipe? If so, describe your experience in creating and using Snowpipe.
8. How can you create a table in Oracle with a time/travel retention period to go back before 12 days?
9. What is the maximum size of a file that can be loaded into an S3 bucket?
10. What are the types of Slowly Changing Dimensions (SCD)?

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

𝟱 𝗙𝗿𝗲𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗹𝗮𝗻𝘀 𝘁𝗼 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝗧𝗲𝗰𝗵 & 𝗔𝗜!😍

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Data engineering interviews will be 10x easier if you learn these tools in sequence👇

➤ 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀
- SQL is very important
- Learn Python Funddamentals
- Pandas and Numpy Library in Python.

➤ 𝗢𝗻-𝗣𝗿𝗲𝗺 𝘁𝗼𝗼𝗹𝘀
- Learn Pyspark - In Depth (Processing tool)
- Hadoop (Distrubuted Storage)
- Hive (Datawarehouse)
- Hbase (NoSQL Database)
- Airflow (Orchestration)
- Kafka (Streaming platform)
- CICD for production readiness

➤ 𝗖𝗹𝗼𝘂𝗱 (𝗔𝗻𝘆 𝗼𝗻𝗲)
- AWS
- Azure
- GCP

➤ Do a couple of projects to get a good feel of it.

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

🎓 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗢𝗽𝗲𝗻 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 – 𝗟𝗲𝗮𝗿𝗻, 𝗚𝗿𝗼𝘄 & 𝗨𝗽𝘀𝗸𝗶𝗹𝗹!😍

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⏳ Don’t miss out—bookmark this for later!

Top Interview Questions for Apache Airflow 👇👇

1. What is Apache Airflow?
2. Is Apache Airflow an ETL tool?
3. How do we define workflows in Apache Airflow?
4. What are the components of the Apache Airflow architecture?
5. What are Local Executors and their types in Airflow?
6. What is a Celery Executor?
7. How is Kubernetes Executor different from Celery Executor?
8. What are Variables (Variable Class) in Apache Airflow?
9. What is the purpose of Airflow XComs?
10. What are the states a Task can be in? Define an ideal task flow.
11. What is the role of Airflow Operators?
12. How does airflow communicate with a third party (S3, Postgres, MySQL)?
13. What are the basic steps to create a DAG?
14. What is Branching in Directed Acyclic Graphs (DAGs)?
15. What are ways to Control Airflow Workflow?
16. Explain the External task Sensor.
17. What are the ways to monitor Apache Airflow?
18. What is TaskFlow API? and how is it helpful?
19. How are Connections used in Apache Airflow?
20. Explain Dynamic DAGs.
21. What are some of the most useful Airflow CLI commands?
22. How to control the parallelism or concurrency of tasks in Apache Airflow configuration?
23. What do you understand by Jinja Templating?
24. What are Macros in Airflow?
25. What are the limitations of TaskFlow API?
26. How is the Executor involved in the Airflow Life cycle?
27. List the types of Trigger rules.
28. What are SLAs?
29. What is Data Lineage?
30.What is a Spark Submit Operator?
31. What is a Spark JDBC Operator?
32. What is the SparkSQL operator?
33. Difference between Client mode and Cluster mode while deploying to a Spark Job.
34. How would you approach if you wanted to queue up multiple dags with order dependencies?
35. What if your Apache Airflow DAG failed for the last ten days, and now you want to backfill those last ten days' data, but you don't need to run all the tasks of the dag to backfill the data?
36. What will happen if you set 'catchup=False' in the dag and 'latest_only = True' for some of the dag tasks?
37. What if you need to use a set of functions to be used in a directed acyclic graph?
38. How would you handle a task which has no dependencies on any other tasks?
39. How can you use a set or a subset of parameters in some of the dags tasks without explicitly defining them in each task?
40. Is there any way to restrict the number of variables to be used in your directed acyclic graph, and why would we need to do that?

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

Like if you need similar content 😄👍

Hope this helps you 😊

𝟰 𝗙𝗥𝗘𝗘 𝗦𝗤𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

- Introduction to SQL (Simplilearn) 

- Intro to SQL (Kaggle) 

- Introduction to Database & SQL Querying 

- SQL for Beginners – Microsoft SQL Server

 Start Learning Today – 4 Free SQL Courses

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Git commands for Data Engineers

𝟭. 𝗴𝗶𝘁 𝗱𝗶𝗳𝗳: Show file differences not yet staged.
𝟮. 𝗴𝗶𝘁 𝗰𝗼𝗺𝗺𝗶𝘁 -𝗮 -𝗺 "𝗰𝗼𝗺𝗺𝗶𝘁 𝗺𝗲𝘀𝘀𝗮𝗴𝗲": Commit all tracked changes with a message.
𝟯. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘁𝘂𝘀: Show the state of your working directory.
𝟰. 𝗴𝗶𝘁 𝗮𝗱𝗱 𝗳𝗶𝗹𝗲_𝗽𝗮𝘁𝗵:Add file(s) to the staging area.
𝟱. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁 -𝗯 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Create and switch to a new branch.
𝟲. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗰𝗸𝗼𝘂𝘁 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Switch to an existing branch.
𝟳. 𝗴𝗶𝘁 𝗰𝗼𝗺𝗺𝗶𝘁 --𝗮𝗺𝗲𝗻𝗱:Modify the last commit.
𝟴. 𝗴𝗶𝘁 𝗽𝘂𝘀𝗵 𝗼𝗿𝗶𝗴𝗶𝗻 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Push a branch to a remote.
𝟵. 𝗴𝗶𝘁 𝗽𝘂𝗹𝗹: Fetch and merge remote changes.
𝟭𝟬. 𝗴𝗶𝘁 𝗿𝗲𝗯𝗮𝘀𝗲 -𝗶: Rebase interactively, rewrite commit history.
𝟭𝟭. 𝗴𝗶𝘁 𝗰𝗹𝗼𝗻𝗲: Create a local copy of a remote repo.
𝟭𝟮. 𝗴𝗶𝘁 𝗺𝗲𝗿𝗴𝗲: Merge branches together.
𝟭𝟯. 𝗴𝗶𝘁 𝗹𝗼𝗴 --𝘀𝘁𝗮𝘁: Show commit logs with stats.
𝟭𝟰. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘀𝗵: Stash changes for later.
𝟭𝟱. 𝗴𝗶𝘁 𝘀𝘁𝗮𝘀𝗵 𝗽𝗼𝗽: Apply and remove stashed changes.
𝟭𝟲. 𝗴𝗶𝘁 𝘀𝗵𝗼𝘄 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Show details about a commit.
𝟭𝟳. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁 𝗛𝗘𝗔𝗗~𝟭: Undo the last commit, preserving changes locally.
𝟭𝟴. 𝗴𝗶𝘁 𝗳𝗼𝗿𝗺𝗮𝘁-𝗽𝗮𝘁𝗰𝗵 -𝟭 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Create a patch file for a specific commit.
𝟭𝟵. 𝗴𝗶𝘁 𝗮𝗽𝗽𝗹𝘆 𝗽𝗮𝘁𝗰𝗵_𝗳𝗶𝗹𝗲_𝗻𝗮𝗺𝗲: Apply changes from a patch file.
𝟮𝟬. 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵 -𝗗 𝗯𝗿𝗮𝗻𝗰𝗵_𝗻𝗮𝗺𝗲: Delete a branch forcefully.
𝟮𝟭. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁: Undo commits by moving branch reference.
𝟮𝟮. 𝗴𝗶𝘁 𝗿𝗲𝘃𝗲𝗿𝘁: Undo commits by creating a new commit.
𝟮𝟯. 𝗴𝗶𝘁 𝗰𝗵𝗲𝗿𝗿𝘆-𝗽𝗶𝗰𝗸 𝗰𝗼𝗺𝗺𝗶𝘁_𝗶𝗱: Apply changes from a specific commit.
𝟮𝟰. 𝗴𝗶𝘁 𝗯𝗿𝗮𝗻𝗰𝗵: Lists branches.
𝟮𝟱. 𝗴𝗶𝘁 𝗿𝗲𝘀𝗲𝘁 --𝗵𝗮𝗿𝗱: Resets everything to a previous commit, erasing all uncommitted changes.

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

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SNOWFLAKES AND DATABRICKS

Snowflake and Databricks are leading cloud data platforms, but how do you choose the right one for your needs?

🌐 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞

❄️ 𝐍𝐚𝐭𝐮𝐫𝐞: Snowflake operates as a cloud-native data warehouse-as-a-service, streamlining data storage and management without the need for complex infrastructure setup.

❄️ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: It provides robust ELT (Extract, Load, Transform) capabilities primarily through its COPY command, enabling efficient data loading.
❄️ Snowflake offers dedicated schema and file object definitions, enhancing data organization and accessibility.

❄️ 𝐅𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲: One of its standout features is the ability to create multiple independent compute clusters that can operate on a single data copy. This flexibility allows for enhanced resource allocation based on varying workloads.

❄️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: While Snowflake primarily adopts an ELT approach, it seamlessly integrates with popular third-party ETL tools such as Fivetran, Talend, and supports DBT installation. This integration makes it a versatile choice for organizations looking to leverage existing tools.

🌐 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬

❄️ 𝐂𝐨𝐫𝐞: Databricks is fundamentally built around processing power, with native support for Apache Spark, making it an exceptional platform for ETL tasks. This integration allows users to perform complex data transformations efficiently.

❄️ 𝐒𝐭𝐨𝐫𝐚𝐠𝐞: It utilizes a 'data lakehouse' architecture, which combines the features of a data lake with the ability to run SQL queries. This model is gaining traction as organizations seek to leverage both structured and unstructured data in a unified framework.

🌐 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬

❄️ 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭 𝐍𝐞𝐞𝐝𝐬: Both Snowflake and Databricks excel in their respective areas, addressing different data management requirements.

❄️ 𝐒𝐧𝐨𝐰𝐟𝐥𝐚𝐤𝐞’𝐬 𝐈𝐝𝐞𝐚𝐥 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: If you are equipped with established ETL tools like Fivetran, Talend, or Tibco, Snowflake could be the perfect choice. It efficiently manages the complexities of database infrastructure, including partitioning, scalability, and indexing.

❄️ 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐥𝐞𝐱 𝐋𝐚𝐧𝐝𝐬𝐜𝐚𝐩𝐞𝐬: Conversely, if your organization deals with a complex data landscape characterized by unpredictable sources and schemas, Databricks—with its schema-on-read technique—may be more advantageous.

🌐 𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:

Ultimately, the decision between Snowflake and Databricks should align with your specific data needs and organizational goals. Both platforms have established their niches, and understanding their strengths will guide you in selecting the right tool for your data strategy.

Roadmap to crack product-based companies for Big Data Engineer role:

1. Master Python, Scala/Java
2. Ace Apache Spark, Hadoop ecosystem
3. Learn data storage (SQL, NoSQL), warehousing
4. Expertise in data streaming (Kafka, Flink/Storm)
5. Master workflow management (Airflow)
6. Cloud skills (AWS, Azure or GCP)
7. Data modeling, ETL/ELT processes
8. Data viz tools (Tableau, Power BI)
9. Problem-solving, communication, attention to detail
10. Projects, certifications (AWS, Azure, GCP)
11. Practice coding, system design interviews

Here, you can find Data Engineering Resources 👇
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Most asked Python interview questions for Data Engineer jobs with answers!

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝘁𝗵𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗹𝗶𝘀𝘁𝘀 𝗮𝗻𝗱 𝘁𝘂𝗽𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻.
Lists are mutable, meaning their elements can be changed but Tuples are immutable.

𝟮. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲 𝗶𝗻 𝗽𝗮𝗻𝗱𝗮𝘀?
A DataFrame is a 2-dimensional labelled data structure, similar to a spreadsheet.

𝟯. 𝗥𝗲𝘃𝗲𝗿𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗼𝗿𝗱𝘀 𝗶𝗻 𝗮 𝘀𝘁𝗿𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻
def reverse_words(s: str) -> str:
words = s.split()
reversed_words = reversed(words)
return ' '.join(reversed_words)

𝟰. 𝗪𝗿𝗶𝘁𝗲 𝗮 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗰𝗼𝘂𝗻𝘁 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝘃𝗼𝘄𝗲𝗹𝘀 𝗶𝗻 𝗮 𝗴𝗶𝘃𝗲𝗻 𝘀𝘁𝗿𝗶𝗻𝗴?
def count_vowels(string: str) -> int:
vowels = "aeiouAEIOU"
vowel_count = 0
for char in string:
if char in vowels:
vowel_count += 1
return vowel_count

I’ve listed 4 but there are many questions you’d need to prepare to succeed in interviews.

Here, you can find Data Engineering Interview Resources 👇 https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C

All the best 👍👍

Here are top 40 commonly asked pyspark questions that you can prepare for interviews.

𝗥𝗗𝗗𝘀 -
1. What is an RDD in Apache Spark? Explain its characteristics.
2. How are RDDs fault-tolerant in Apache Spark?
3. What are the different ways to create RDDs in Spark?
4. Explain the difference between transformations and actions in RDDs.
5. How does Spark handle data partitioning in RDDs?
6. Can you explain the lineage graph in RDDs and its significance?
7. What is lazy evaluation in Apache Spark RDDs?
8. How can you persist RDDs in memory for faster access?
9. Explain the concept of narrow and wide transformations in RDDs.
10. What are the limitations of RDDs compared to DataFrames and Datasets?

𝗗𝗮𝘁𝗮𝗳𝗿𝗮𝗺𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀 -
1. What are DataFrames and Datasets in Apache Spark?
2. What are the differences between DataFrame and RDD?
3. Explain the concept of a schema in a DataFrame.
4. How are DataFrames and Datasets fault-tolerant in Spark?
5. What are the advantages of using DataFrames over RDDs?
6. Explain the Catalyst optimizer in Apache Spark.
7. How can you create DataFrames in Apache Spark?
8. What is the significance of Encoders in Datasets?
9. How does Spark SQL optimize the execution plan for DataFrames?
10. Can you explain the benefits of using Datasets over DataFrames?

𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 -
1. What is Spark SQL, and how does it relate to Apache Spark?
2. How does Spark SQL leverage DataFrame and Dataset APIs?
3. Explain the role of the Catalyst optimizer in Spark SQL.
4. How can you run SQL queries on DataFrames in Spark SQL?
5. What are the benefits of using Spark SQL over traditional SQL queries?

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 -
1. What are some common performance bottlenecks in Apache Spark applications?
2. How can you optimize the shuffle operations in Spark?
3. Explain the significance of data skew and techniques to handle it in Spark.
4. What are some techniques to optimize Spark job execution time?
5. How can you tune memory configurations for better performance in Spark?
6. What is dynamic allocation, and how does it optimize resource usage in Spark?
7. How can you optimize joins in Spark?
8. What are the benefits of partitioning data in Spark?
9. How does Spark leverage data locality for optimization?

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

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5 most asked SQL Interview Questions for Data Engineer jobs.

𝟭. 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗲𝗰𝗼𝗻𝗱 𝗛𝗶𝗴𝗵𝗲𝘀𝘁 𝗦𝗮𝗹𝗮𝗿𝘆 𝗶𝗻 𝗮 𝗧𝗮𝗯𝗹𝗲

SELECT MAX(salary) AS SecondHighestSalary
FROM Employee
WHERE salary < (SELECT MAX(salary) FROM Employee);

𝟮 . 𝗙𝗶𝗻𝗱 𝗼𝘂𝘁 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘁𝗵𝗲𝗶𝗿 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀

SELECT e2.name as Employee
FROM employee e1
INNER JOIN employee e2
ON e1.id = e2.managerID
WHERE e1.salary < e2.salary

𝟯. 𝗙𝗶𝗻𝗱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘄𝗵𝗼 𝗻𝗲𝘃𝗲𝗿 𝗼𝗿𝗱𝗲𝗿

SELECT name as Customers
FROM Customers
WHERE id not in (
SELECT customerId
FROM Orders);

𝟰. 𝗗𝗲𝗹𝗲𝘁𝗲 𝗱𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗲𝗺𝗮𝗶𝗹𝘀

DELETE p1
FROM Person p1, Person p2
WHERE p1.Email = p2.Email AND
p1.Id > p2.Id

𝟱. 𝗖𝗼𝘂𝗻𝘁 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗼𝗿𝗱𝗲𝗿𝘀 𝗽𝗹𝗮𝗰𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝘆𝗲𝗮𝗿 𝗮𝗻𝗱 𝗺𝗼𝗻𝘁𝗵.

SELECT COUNT(*) AS order_count
FROM orders WHERE EXTRACT(YEAR_MONTH FROM order_date) = EXTRACT(YEAR_MONTH FROM CURDATE() - INTERVAL 1 MONTH);

💡 Note: SQL interview questions vary widely based on the specific role and company. So you also need to practice questions your target companies ask.

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

All the best 👍👍

𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

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Thinking about becoming a Data Engineer? Here's the roadmap to avoid pitfalls & master the essential skills for a successful career.

📊Introduction to Data Engineering

✅Overview of Data Engineering & its importance
✅Key responsibilities & skills of a Data Engineer
✅Difference between Data Engineer, Data Scientist & Data Analyst
✅Data Engineering tools & technologies

📊Programming for Data Engineering

✅Python
✅SQL
✅Java/Scala
✅Shell scripting

📊Database System & Data Modeling

✅Relational Databases: design, normalization & indexing
✅NoSQL Databases: key-value stores, document stores, column-family stores & graph database
✅Data Modeling: conceptual, logical & physical data model
✅Database Management Systems & their administration

📊Data Warehousing and ETL Processes

✅Data Warehousing concepts: OLAP vs. OLTP, star schema & snowflake schema
✅ETL: designing, developing & managing ETL processe
✅Tools & technologies: Apache Airflow, Talend, Informatica, AWS Glue
✅Data lakes & modern data warehousing solution

📊Big Data Technologies

✅Hadoop ecosystem: HDFS, MapReduce, YARN
✅Apache Spark: core concepts, RDDs, DataFrames & SparkSQL
✅Kafka and real-time data processing
✅Data storage solutions: HBase, Cassandra, Amazon S3

📊Cloud Platforms & Services

✅Introduction to cloud platforms: AWS, Google Cloud Platform, Microsoft Azure
✅Cloud data services: Amazon Redshift, Google BigQuery, Azure Data Lake
✅Data storage & management on the cloud
✅Serverless computing & its applications in data engineering

📊Data Pipeline Orchestration

✅Workflow orchestration: Apache Airflow, Luigi, Prefect
✅Building & scheduling data pipelines
✅Monitoring & troubleshooting data pipelines
✅Ensuring data quality & consistency

📊Data Integration & API Development

✅Data integration techniques & best practices
✅API development: RESTful APIs, GraphQL
✅Tools for API development: Flask, FastAPI, Django
✅Consuming APIs & data from external sources

📊Data Governance & Security

✅Data governance frameworks & policies
✅Data security best practices
✅Compliance with data protection regulations
✅Implementing data auditing & lineage

📊Performance Optimization & Troubleshooting

✅Query optimization techniques
✅Database tuning & indexing
✅Managing & scaling data infrastructure
✅Troubleshooting common data engineering issues

📊Project Management & Collaboration

✅Agile methodologies & best practices
✅Version control systems: Git & GitHub
✅Collaboration tools: Jira, Confluence, Slack
✅Documentation & reporting

Resources for Data Engineering
1️⃣Python: https://t.me/pythonanalyst

2️⃣SQL: https://t.me/sqlanalyst

3️⃣Excel: https://t.me/excel_analyst

4️⃣Free DE Courses: https://t.me/free4unow_backup/569

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

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