𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜? 𝗧𝗵𝗶𝘀 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗶𝘀 𝗬𝗼𝘂𝗿 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗦𝗵𝗼𝗿𝘁𝗰𝘂𝘁!😍

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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

Best telegram channel to learn SQL: https://t.iss.one/sqlanalyst

Data Analyst Jobs👇
https://t.iss.one/jobs_SQL

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Like this post if it helps 😄❤️

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 👇
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

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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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Perfect for Beginners & Professionals Looking to Upskill!✅️

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 👍👍