Tips to become a Data Engineer 👇👇
1. Data Engineering Basics: At its core, it's about efficiently moving and reshaping data from one place/format to another.
2. Be Curious: The field is vast. Dive deep, ask questions, and always be in the mode of learning and experimenting.
3. Master Data: Understand the intricacies of data types, where they originate, and how they're structured.
4. Programming: Grasping a language is crucial. If you're unsure, start with Python – it's versatile and widely used in the industry.
5. SQL: A timeless tool for querying databases. Mastering SQL will empower you to work with data across various platforms.
6. Command Line: Familiarizing yourself with command line operations can save a lot of time, especially for quick and repetitive tasks.
7. Know Computers: A basic understanding of how computers communicate and process information can guide better data engineering decisions.
8. Personal Projects: Practical experience is invaluable. Start projects, learn from them, and showcase your work on platforms like GitHub.
9. APIs and JSON: Many modern data sources are API-based. Understanding how to extract and manipulate JSON data will be a daily task.
10. Tools Mastery: Get proficient with your primary tools, but stay updated with emerging technologies and platforms.
11. Data Storage Basics: Know the difference and use-cases for Databases, Data Lakes, and Data Warehouses. Understand the distinction between OLTP (online transaction processing) and OLAP (online analytical processing).
12. Cloud Platforms: The cloud is the future. AWS, Azure, and GCP offer free tiers to start experimenting.
13. Business Acumen: A data engineer who understands business metrics and their implications can offer more value.
14. Data Grain: Dive deep into datasets to understand their finest level of detail. It aids in more precise querying and analytics.
15. Data Formats: Recognizing main data formats (like JSON, XML, CSV, SQLite, Database) will help you navigate different datasets with ease.
1. Data Engineering Basics: At its core, it's about efficiently moving and reshaping data from one place/format to another.
2. Be Curious: The field is vast. Dive deep, ask questions, and always be in the mode of learning and experimenting.
3. Master Data: Understand the intricacies of data types, where they originate, and how they're structured.
4. Programming: Grasping a language is crucial. If you're unsure, start with Python – it's versatile and widely used in the industry.
5. SQL: A timeless tool for querying databases. Mastering SQL will empower you to work with data across various platforms.
6. Command Line: Familiarizing yourself with command line operations can save a lot of time, especially for quick and repetitive tasks.
7. Know Computers: A basic understanding of how computers communicate and process information can guide better data engineering decisions.
8. Personal Projects: Practical experience is invaluable. Start projects, learn from them, and showcase your work on platforms like GitHub.
9. APIs and JSON: Many modern data sources are API-based. Understanding how to extract and manipulate JSON data will be a daily task.
10. Tools Mastery: Get proficient with your primary tools, but stay updated with emerging technologies and platforms.
11. Data Storage Basics: Know the difference and use-cases for Databases, Data Lakes, and Data Warehouses. Understand the distinction between OLTP (online transaction processing) and OLAP (online analytical processing).
12. Cloud Platforms: The cloud is the future. AWS, Azure, and GCP offer free tiers to start experimenting.
13. Business Acumen: A data engineer who understands business metrics and their implications can offer more value.
14. Data Grain: Dive deep into datasets to understand their finest level of detail. It aids in more precise querying and analytics.
15. Data Formats: Recognizing main data formats (like JSON, XML, CSV, SQLite, Database) will help you navigate different datasets with ease.
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Kavitha's Journey to become a Data Engineer 👇👇
1. Startup to Dream Job Journey:
- Started at a startup in India, transitioned to Infosys, then grabbed UK opportunity.
- Shifted from legacy Mainframe to AWS Cloud, pursued Master's from illinoisstateu, and secured dream job at Statefarm.
2. Learn Fundamentals:
- Assess skills, understand role.
- Gain proficiency in Python, SQL.
- Learn data technologies.
3. Database and Modeling Skills:
- Understand databases, gain proficiency.
- Learn data modeling principles.
4. Master ETL, Warehousing, and Visualization:
- Understand ETL, data warehousing.
- Gain experience in building warehouses.
- Familiarize with visualization tools.
- Got Certified as AWS Solutions Architect.
5. Utilize LinkedIn for Job Search:
- Network and connect with professionals.
- Showcase skills and achievements.
- Utilize job search feature, leading to dream job at Statefarm.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
1. Startup to Dream Job Journey:
- Started at a startup in India, transitioned to Infosys, then grabbed UK opportunity.
- Shifted from legacy Mainframe to AWS Cloud, pursued Master's from illinoisstateu, and secured dream job at Statefarm.
2. Learn Fundamentals:
- Assess skills, understand role.
- Gain proficiency in Python, SQL.
- Learn data technologies.
3. Database and Modeling Skills:
- Understand databases, gain proficiency.
- Learn data modeling principles.
4. Master ETL, Warehousing, and Visualization:
- Understand ETL, data warehousing.
- Gain experience in building warehouses.
- Familiarize with visualization tools.
- Got Certified as AWS Solutions Architect.
5. Utilize LinkedIn for Job Search:
- Network and connect with professionals.
- Showcase skills and achievements.
- Utilize job search feature, leading to dream job at Statefarm.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
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Here's what the average data engineering interview looks like in 2025:
- 1 hour algorithms in Python
Here you will be asked irrelevant questions about dynamic programming, linked lists, and inverting trees
- 1 hour SQL
Here you will be asked niche questions about recursive CTEs that you've used once in your ten year career
- 1 hour data architecture
Here you will be asked about CAP theorem, lambda vs kappa, and a bunch of other things that ChatGPT probably could answer in a heartbeat
- 1 hour behavioral
Here you will be asked about how to play nicely with your coworkers. This is the most relevant interview in my opinion
- 1 hour project deep dive
Here you will be asked to make up a story about something you did or did not do in the past that was a technical marvel
- 4 hour take home assignment
Here you will be asked to build their entire data engineering stack from scratch over a weekend because why hire data engineers when you can submit them to tests?
- 1 hour algorithms in Python
Here you will be asked irrelevant questions about dynamic programming, linked lists, and inverting trees
- 1 hour SQL
Here you will be asked niche questions about recursive CTEs that you've used once in your ten year career
- 1 hour data architecture
Here you will be asked about CAP theorem, lambda vs kappa, and a bunch of other things that ChatGPT probably could answer in a heartbeat
- 1 hour behavioral
Here you will be asked about how to play nicely with your coworkers. This is the most relevant interview in my opinion
- 1 hour project deep dive
Here you will be asked to make up a story about something you did or did not do in the past that was a technical marvel
- 4 hour take home assignment
Here you will be asked to build their entire data engineering stack from scratch over a weekend because why hire data engineers when you can submit them to tests?
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Data Engineering Tools:
Apache Hadoop 🗂️ – Distributed storage and processing for big data
Apache Spark ⚡ – Fast, in-memory processing for large datasets
Airflow 🦋 – Orchestrating complex data workflows
Kafka 🐦 – Real-time data streaming and messaging
ETL Tools (e.g., Talend, Fivetran) 🔄 – Extract, transform, and load data pipelines
dbt 🔧 – Data transformation and analytics engineering
Snowflake ❄️ – Cloud-based data warehousing
Google BigQuery 📊 – Managed data warehouse for big data analysis
Redshift 🔴 – Amazon’s scalable data warehouse
MongoDB Atlas 🌿 – Fully-managed NoSQL database service
Apache Hadoop 🗂️ – Distributed storage and processing for big data
Apache Spark ⚡ – Fast, in-memory processing for large datasets
Airflow 🦋 – Orchestrating complex data workflows
Kafka 🐦 – Real-time data streaming and messaging
ETL Tools (e.g., Talend, Fivetran) 🔄 – Extract, transform, and load data pipelines
dbt 🔧 – Data transformation and analytics engineering
Snowflake ❄️ – Cloud-based data warehousing
Google BigQuery 📊 – Managed data warehouse for big data analysis
Redshift 🔴 – Amazon’s scalable data warehouse
MongoDB Atlas 🌿 – Fully-managed NoSQL database service
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Forwarded from Python Projects & Resources
𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍
✅ Beginner-friendly
✅ Straight from Microsoft
✅ And yes… a badge for that resume flex
Perfect for beginners, job seekers, & Working Professionals
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4iq8QlM
Enroll for FREE & Get Certified 🎓
✅ Beginner-friendly
✅ Straight from Microsoft
✅ And yes… a badge for that resume flex
Perfect for beginners, job seekers, & Working Professionals
𝐋𝐢𝐧𝐤 👇:-
https://pdlink.in/4iq8QlM
Enroll for FREE & Get Certified 🎓
🔍 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
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
👍3
𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍
Dreaming of working at Google but not sure where to even begin?📍
Start with these FREE insider resources—from building a resume that stands out to mastering the Google interview process. 🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/441GCKF
Because if someone else can do it, so can you. Why not you? Why not now?✅️
Dreaming of working at Google but not sure where to even begin?📍
Start with these FREE insider resources—from building a resume that stands out to mastering the Google interview process. 🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/441GCKF
Because if someone else can do it, so can you. Why not you? Why not now?✅️
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20 recently asked 𝗣𝗬𝗧𝗛𝗢𝗡 questions for Data Engineers.
1. Design a Python noscript to process and transform large CSV files from multiple sources daily.
2. Write Python code to identify and handle missing values in a dataset.
3. Implement a Python solution to store large volumes of time-series data efficiently using an appropriate format.
4. Create a Python-based system to process streaming data from IoT devices in real-time.
5. Write a Python ETL noscript to extract data from a SQL database, transform it, and load it into a NoSQL database.
6. Implement error handling in a Python data pipeline when an unexpected data type is encountered.
7. Write Python code to validate incoming data for consistency and accuracy.
8. Optimize a Python noscript processing large datasets to reduce runtime.
9. Create a Python function to merge multiple large datasets without memory overflow.
10. Write a Python noscript to automate the daily backup of data stored in a cloud bucket.
11. Implement parallel processing in Python for handling large-scale data operations.
12. Write a Python program to monitor and log the performance of a data pipeline.
13. Implement a Python solution to remove duplicates from a large dataset efficiently.
14. Write a Python noscript to connect to an API, fetch data, and store it in a database.
15. Implement a Python function to generate summary statistics for a large dataset.
16. Write a Python noscript to clean and standardize a dataset with inconsistent formats.
17. Implement a Python-based incremental data load from a source system to a data warehouse.
18. Write Python code to detect and remove outliers from a dataset.
19. Implement a Python pipeline to process and analyze log files in real-time.
20. Write Python code to create and manage partitions in a large dataset for faster querying.
1. Design a Python noscript to process and transform large CSV files from multiple sources daily.
2. Write Python code to identify and handle missing values in a dataset.
3. Implement a Python solution to store large volumes of time-series data efficiently using an appropriate format.
4. Create a Python-based system to process streaming data from IoT devices in real-time.
5. Write a Python ETL noscript to extract data from a SQL database, transform it, and load it into a NoSQL database.
6. Implement error handling in a Python data pipeline when an unexpected data type is encountered.
7. Write Python code to validate incoming data for consistency and accuracy.
8. Optimize a Python noscript processing large datasets to reduce runtime.
9. Create a Python function to merge multiple large datasets without memory overflow.
10. Write a Python noscript to automate the daily backup of data stored in a cloud bucket.
11. Implement parallel processing in Python for handling large-scale data operations.
12. Write a Python program to monitor and log the performance of a data pipeline.
13. Implement a Python solution to remove duplicates from a large dataset efficiently.
14. Write a Python noscript to connect to an API, fetch data, and store it in a database.
15. Implement a Python function to generate summary statistics for a large dataset.
16. Write a Python noscript to clean and standardize a dataset with inconsistent formats.
17. Implement a Python-based incremental data load from a source system to a data warehouse.
18. Write Python code to detect and remove outliers from a dataset.
19. Implement a Python pipeline to process and analyze log files in real-time.
20. Write Python code to create and manage partitions in a large dataset for faster querying.
👍2
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𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍
Dreaming of a career in data but don’t have a degree? You don’t need one. What you do need are the right skills🔗
These 4 free/affordable certifications can get you there. 💻✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ioaJ2p
Let’s get you certified and hired!✅️
Dreaming of a career in data but don’t have a degree? You don’t need one. What you do need are the right skills🔗
These 4 free/affordable certifications can get you there. 💻✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4ioaJ2p
Let’s get you certified and hired!✅️
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 👍👍
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 👍👍
👍3
𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍
SQL seems tough, right? 😩
These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GtntaC
Master it with ease. 💡
SQL seems tough, right? 😩
These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3GtntaC
Master it with ease. 💡
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