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SQL From Basic to Advanced level

Basic SQL is ONLY 7 commands:
- SELECT
- FROM
- WHERE (also use SQL comparison operators such as =, <=, >=, <> etc.)
- ORDER BY
- Aggregate functions such as SUM, AVERAGE, COUNT etc.
- GROUP BY
- CREATE, INSERT, DELETE, etc.
You can do all this in just one morning.

Once you know these, take the next step and learn commands like:
- LEFT JOIN
- INNER JOIN
- LIKE
- IN
- CASE WHEN
- HAVING (undertstand how it's different from GROUP BY)
- UNION ALL
This should take another day.

Once both basic and intermediate are done, start learning more advanced SQL concepts such as:
- Subqueries (when to use subqueries vs CTE?)
- CTEs (WITH AS)
- Stored Procedures
- Triggers
- Window functions (LEAD, LAG, PARTITION BY, RANK, DENSE RANK)
These can be done in a couple of days.
Learning these concepts is NOT hard at all

- what takes time is practice and knowing what command to use when. How do you master that?
- First, create a basic SQL project
- Then, work on an intermediate SQL project (search online) -

Lastly, create something advanced on SQL with many CTEs, subqueries, stored procedures and triggers etc.

This is ALL you need to become a badass in SQL, and trust me when I say this, it is not rocket science. It's just logic.

Remember that practice is the key here. It will be more clear and perfect with the continous practice

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ENJOY LEARNING 👍👍

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- 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 👇
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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

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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 👇
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All the best 👍👍

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➤ 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.

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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) 

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- Introduction to Database & SQL Querying 

- SQL for Beginners – Microsoft SQL Server

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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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