How to become a data engineer in 2025:

➡️ Learn SQL
➡️ Learn Python
➡️ Learn Spark
➡️ Learn ETL/ELT
➡️ Learn data modelling

Then use what you've learnt and build in public

🔍 Mastering Spark: 20 Interview Questions Demystified!

1️⃣ MapReduce vs. Spark: Learn how Spark achieves 100x faster performance compared to MapReduce.
2️⃣ RDD vs. DataFrame: Unravel the key differences between RDD and DataFrame, and discover what makes DataFrame unique.
3️⃣ DataFrame vs. Datasets: Delve into the distinctions between DataFrame and Datasets in Spark.
4️⃣ RDD Operations: Explore the various RDD operations that power Spark.
5️⃣ Narrow vs. Wide Transformations: Understand the differences between narrow and wide transformations in Spark.
6️⃣ Shared Variables: Discover the shared variables that facilitate distributed computing in Spark.
7️⃣ Persist vs. Cache: Differentiate between the persist and cache functionalities in Spark.
8️⃣ Spark Checkpointing: Learn about Spark checkpointing and how it differs from persisting to disk.
9️⃣ SparkSession vs. SparkContext: Understand the roles of SparkSession and SparkContext in Spark applications.
🔟 spark-submit Parameters: Explore the parameters to specify in the spark-submit command.
1️⃣1️⃣ Cluster Managers in Spark: Familiarize yourself with the different types of cluster managers available in Spark.
1️⃣2️⃣ Deploy Modes: Learn about the deploy modes in Spark and their significance.
1️⃣3️⃣ Executor vs. Executor Core: Distinguish between executor and executor core in the Spark ecosystem.
1️⃣4️⃣ Shuffling Concept: Gain insights into the shuffling concept in Spark and its importance.
1️⃣5️⃣ Number of Stages in Spark Job: Understand how to decide the number of stages created in a Spark job.
1️⃣6️⃣ Spark Job Execution Internals: Get a peek into how Spark internally executes a program.
1️⃣7️⃣ Direct Output Storage: Explore the possibility of directly storing output without sending it back to the driver.
1️⃣8️⃣ Coalesce and Repartition: Learn about the applications of coalesce and repartition in Spark.
1️⃣9️⃣ Physical and Logical Plan Optimization: Uncover the optimization techniques employed in Spark's physical and logical plans.
2️⃣0️⃣ Treereduce and Treeaggregate: Discover why treereduce and treeaggregate are preferred over reduceByKey and aggregateByKey in certain scenarios.

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

𝟰 𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁!😍

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One day or Day one. You decide.

Data Engineer edition.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench and write my first query.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my data pipelines.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Apache Airflow and set up my first DAG.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master big data tools.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start a Spark tutorial and process my first dataset.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn cloud data services.
𝗗𝗮𝘆 𝗢𝗻𝗲: Sign up for an Azure or AWS account and deploy my first data pipeline.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Engineer.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to data engineering job postings.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will start preparing for the interviews.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start preparing from today itself without any procrastination

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5 Pandas Functions to Handle Missing Data

🔹 fillna() – Fill missing values with a specific value or method
🔹 interpolate() – Fill NaNs with interpolated values (e.g., linear, time-based)
🔹 ffill() – Forward-fill missing values with the previous valid entry
🔹 bfill() – Backward-fill missing values with the next valid entry
🔹 dropna() – Remove rows or columns with missing values

#Pandas

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.

𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗚𝘂𝗶𝗱𝗲!😍

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Use the datasets from these FREE websites for your data projects:

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➡️ 2. Data world
➡️ 3. Open Data Blend
➡️ 4. World Bank Open Data
➡️ 5. Google Dataset Search

𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!😍

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In the Big Data world, if you need:

Distributed Storage -> Apache Hadoop
Stream Processing -> Apache Kafka
Batch Data Processing -> Apache Spark
Real-Time Data Processing -> Spark Streaming
Data Pipelines -> Apache NiFi
Data Warehousing -> Apache Hive
Data Integration -> Apache Sqoop
Job Scheduling -> Apache Airflow
NoSQL Database -> Apache HBase
Data Visualization -> Tableau

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

All the best 👍👍

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Partitioning vs. Z-Ordering in Delta Lake

Partitioning:
Purpose: Partitioning divides data into separate directories based on the distinct values of a column (e.g., date, region, country). This helps in reducing the amount of data scanned during queries by only focusing on relevant partitions.
Example: Imagine you have a table storing sales data for multiple years:

CREATE TABLE sales_data
PARTITIONED BY (year)
AS
SELECT * FROM raw_data;

This creates a separate directory for each year (e.g., /year=2021/, /year=2022/). A query filtering on year can read only the relevant partition:

SELECT * FROM sales_data WHERE year = 2022;

Benefit: By scanning only the directory for the 2022 partition, the query is faster and avoids unnecessary I/O.

Usage: Ideal for columns with high cardinality or range-based queries like year, region, product_category.

Z-Ordering:

Purpose: Z-Ordering clusters data within the same file based on specific columns, allowing for efficient data skipping. This works well with columns frequently used in filtering or joining.
Example: Suppose you have a sales table partitioned by year, and you frequently run queries filtering by customer_id:

OPTIMIZE sales_data
ZORDER BY (customer_id);
Z-Ordering rearranges data within each partition so that rows with similar customer_id values are co-located. When you run a query with a filter:

SELECT * FROM sales_data WHERE customer_id = '12345';
Delta Lake skips irrelevant data, scanning fewer files and improving query speed.

Benefit: Reduces the number of rows/files that need to be scanned for queries with filter conditions.

Usage: Best used for columns often appearing in filters or joins like customer_id, product_id, zip_code. It works well when you already have partitioning in place.

Combined Approach:

Partition Data: First, partition your table based on key columns like date, region, or year for efficient range scans.
Apply Z-Ordering: Next, apply Z-Ordering within the partitions to cluster related data and enhance data skipping, e.g., partition by year and Z-Order by customer_id.

Example: If you have sales data partitioned by year and want to optimize queries filtering on product_id:

CREATE TABLE sales_data
PARTITIONED BY (year)
AS
SELECT * FROM raw_data;

OPTIMIZE sales_data
ZORDER BY (product_id);

This combination of partitioning and Z-Ordering maximizes query performance by leveraging the strengths of both techniques. Partitioning narrows down the data to relevant directories, while Z-Ordering optimizes data retrieval within those partitions.

Summary:

Partitioning: Great for columns like year, region, product_category, where range-based queries occur.
Z-Ordering: Ideal for columns like customer_id, product_id, or any frequently filtered/joined columns.

When used together, partitioning and Z-Ordering ensure that your queries read the least amount of data necessary, significantly improving performance for large datasets.

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

All the best 👍👍

𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝘄𝗶𝘁𝗵 𝗧𝗵𝗶𝘀 𝗙𝗿𝗲𝗲 𝗢𝗿𝗮𝗰𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵!😍

Want to start a career in Data Science but don’t know where to begin?👋

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Data Engineering free courses   

Linked Data Engineering
🎬 Video Lessons
Rating ⭐️: 5 out of 5     
Students 👨‍🎓: 9,973
Duration ⏰:  8 weeks long
Source: openHPI
🔗 Course Link  

Data Engineering
Credits ⏳: 15
Duration ⏰: 4 hours
🏃‍♂️ Self paced       
Source:  Google cloud
🔗 Course Link

Data Engineering Essentials using Spark, Python and SQL  
🎬 402 video lesson
🏃‍♂️ Self paced
Teacher: itversity
Resource: Youtube
🔗 Course Link  
 
Data engineering with Azure Databricks      
Modules ⏳: 5
Duration ⏰:  4-5 hours worth of material
🏃‍♂️ Self paced       
Source:  Microsoft ignite
🔗 Course Link

Perform data engineering with Azure Synapse Apache Spark Pools      
Modules ⏳: 5
Duration ⏰:  2-3 hours worth of material
🏃‍♂️ Self paced       
Source:  Microsoft Learn
🔗 Course Link

Books
Data Engineering
The Data Engineers Guide to Apache Spark

All the best 👍👍

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Looking to boost your career with free online courses? 🎓

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Roadmap for becoming an Azure Data Engineer in 2025:

- SQL
- Basic python
- Cloud Fundamental
- ADF
- Databricks/Spark/Pyspark
- Azure Synapse
- Azure Functions, Logic Apps
- Azure Storage, Key Vault
- Dimensional Modelling
- Azure Fabric
- End-to-End Project
- Resume Preparation
- Interview Prep

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

All the best 👍👍