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 👍👍

Frequently asked SQL interview for Data Analyst/Data Engineer

1 What is SQL and what are its main features?
2 Order of writing SQL query?
3Order of execution of SQL query?
4 What are some of the most common SQL commands?
5 What’s a primary key & foreign key?
6 All types of joins and questions on their outputs?
7 Explain all window functions and difference between them?
8 What is stored procedure?
9 Difference between stored procedure & Functions in SQL?
10 What is trigger in SQL?

Interviewer: You have 2 minutes. Explain the difference between Caching and Persisting in Spark.

➤ 𝗖𝗮𝗰𝗵𝗶𝗻𝗴:

Caching in Apache Spark involves storing RDDs in memory temporarily. When an RDD is cached, its partitions are kept in memory across multiple operations, allowing for faster access and reuse of intermediate results.

➤ 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴:

Persisting in Apache Spark is similar to caching but offers more flexibility in terms of storage options. When you persist an RDD, you can specify different storage levels such as MEMORY_ONLY, MEMORY_AND_DISK, or DISK_ONLY, depending on your requirements

➤ 𝗞𝗲𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴:

- While caching stores RDDs in memory by default, persisting allows you to choose different storage levels, including disk storage. Caching is suitable for scenarios where RDDs need to be reused in subsequent operations within the same Spark job.
- whereas persisting is more versatile and can be used to store RDDs across multiple jobs or even persist them to disk for fault tolerance.

➤ 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘄𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝘃𝗲𝗿𝘀𝘂𝘀 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴

- Let's say we have an iterative algorithm where the same RDD is accessed multiple times within a loop. In this case, caching the RDD would be beneficial as it would avoid recomputation of the RDD's partitions in each iteration, resulting in significant performance gains.
- On the other hand, if we need to persist RDDs across multiple Spark jobs or need fault tolerance, persisting would be more appropriate.

➤ 𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝗦𝗽𝗮𝗿𝗸 𝗵𝗮𝗻𝗱𝗹𝗲 𝗰𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗻𝗱 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗶𝗻𝗴 𝘂𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗵𝗼𝗼𝗱

Spark employs a lazy evaluation strategy, so RDDs are not actually cached or persisted until an action is triggered. When an action is called on a cached or persisted RDD, Spark checks if the data is already in memory or on disk. If not, it calculates the RDD's partitions and stores them accordingly based on the specified storage level.

That’s the difference between Caching and Persisting in Spark.

🔺 Data engineering Free Courses

1️⃣ Data Engineering Course : Learn the basics of data engineering.

2️⃣ Data Engineer Learning Path course : a comprehensive road map to become a data engineer.

3️⃣ The Data Eng Zoomcamp course : a practical course to learn data engineering

Unlock your full potential as a Data Engineer with this detailed career path

Step 1: Fundamentals
Step 2: Data Structures & Algorithms
Step 3: Databases (SQL / NoSQL) & Data Modeling
Step 4: Data Ingestion & Data Storage Techniques
Step 5: Data warehousing tools & Data analytics techniques
Step 6: Major cloud providers and their services related to Data Engineering
Step 7: Tools required for real-time data and batch data pipelines
Step 8: Data Engineering Deployments & ops

HR: "What's your salary expectation?"
Candidate: $8,000 to 10,000 a month.

HR: You are the best-fit for the role but we can only offer $7000.
Candidate: Okay. $7,000 would be fine.

HR: How soon can you start?

Meanwhile the budget for that particular role is $15,000. HR feels like they did a great job in salary negotiation and management will be happy they cut cost for the organisation.

The new employee starts and notices the pay disparity. Guess what happens? Dissatisfaction. Disengagement. Disloyalty.

Two months later, the employee leaves the organization for a better job. The recruitment process starts all over again. Leading to further costs and performance gaps within the team and organisation.

In order to attract and retain top talent, please pay people what they are worth.

- SQL + SELECT = Querying Data
- SQL + JOIN = Data Integration
- SQL + WHERE = Data Filtering
- SQL + GROUP BY = Data Aggregation
- SQL + ORDER BY = Data Sorting
- SQL + UNION = Combining Queries
- SQL + INSERT = Data Insertion
- SQL + UPDATE = Data Modification
- SQL + DELETE = Data Removal
- SQL + CREATE TABLE = Database Design
- SQL + ALTER TABLE = Schema Modification
- SQL + DROP TABLE = Table Removal
- SQL + INDEX = Query Optimization
- SQL + VIEW = Virtual Tables
- SQL + Subqueries = Nested Queries
- SQL + Stored Procedures = Task Automation
- SQL + Triggers = Automated Responses
- SQL + CTE = Recursive Queries
- SQL + Window Functions = Advanced Analytics
- SQL + Transactions = Data Integrity
- SQL + ACID Compliance = Reliable Operations
- SQL + Data Warehousing = Large Data Management
- SQL + ETL = Data Transformation
- SQL + Partitioning = Big Data Management
- SQL + Replication = High Availability
- SQL + Sharding = Database Scaling
- SQL + JSON = Semi-Structured Data
- SQL + XML = Structured Data
- SQL + Data Security = Data Protection
- SQL + Performance Tuning = Query Efficiency
- SQL + Data Governance = Data Quality

SQL is composed of five key components:

𝐃𝐃𝐋 (𝐃𝐚𝐭𝐚 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞): Commands like CREATE, ALTER, DROP for defining and modifying database structures.
𝐃𝐐𝐋 (𝐃𝐚𝐭𝐚 𝐐𝐮𝐞𝐫𝐲 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞): Commands like SELECT for querying and retrieving data.
𝐃𝐌𝐋 (𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞): Commands like INSERT, UPDATE, DELETE for modifying data.
𝐃𝐂𝐋 (𝐃𝐚𝐭𝐚 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞): Commands like GRANT, REVOKE for managing access permissions.
𝐓𝐂𝐋 (𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞): Commands like COMMIT, ROLLBACK for managing transactions.

If you're an engineer, you'll likely need a solid understanding of all these components. If you're a data analyst, focusing on DQL will be more relevant. Tailor your learning to the topics that best fit your role.

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 👍👍

🔍 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

Pandas Data Cleaning.pdf