๐๐๐ซ๐ ๐๐ซ๐ 20 ๐ซ๐๐๐ฅ-๐ญ๐ข๐ฆ๐ ๐๐ฉ๐๐ซ๐ค ๐ฌ๐๐๐ง๐๐ซ๐ข๐จ-๐๐๐ฌ๐๐ ๐ช๐ฎ๐๐ฌ๐ญ๐ข๐จ๐ง๐ฌ
1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?
2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?
3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.
4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?
5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?
6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.
7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.
8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?
9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?
10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?
11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.
12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?
13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?
14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?
15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.
16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?
17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?
18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?
19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.
20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
1. Data Processing Optimization: How would you optimize a Spark job that processes 1 TB of data daily to reduce execution time and cost?
2. Handling Skewed Data: In a Spark job, one partition is taking significantly longer to process due to skewed data. How would you handle this situation?
3. Streaming Data Pipeline: Describe how you would set up a real-time data pipeline using Spark Structured Streaming to process and analyze clickstream data from a website.
4. Fault Tolerance: How does Spark handle node failures during a job, and what strategies would you use to ensure data processing continues smoothly?
5. Data Join Strategies: You need to join two large datasets in Spark, but you encounter memory issues. What strategies would you employ to handle this?
6. Checkpointing: Explain the role of checkpointing in Spark Streaming and how you would implement it in a real-time application.
7. Stateful Processing: Describe a scenario where you would use stateful processing in Spark Streaming and how you would implement it.
8. Performance Tuning: What are the key parameters you would tune in Spark to improve the performance of a real-time analytics application?
9. Window Operations: How would you use window operations in Spark Streaming to compute rolling averages over a sliding window of events?
10. Handling Late Data: In a Spark Streaming job, how would you handle late-arriving data to ensure accurate results?
11. Integration with Kafka: Describe how you would integrate Spark Streaming with Apache Kafka to process real-time data streams.
12. Backpressure Handling: How does Spark handle backpressure in a streaming application, and what configurations can you use to manage it?
13. Data Deduplication: How would you implement data deduplication in a Spark Streaming job to ensure unique records?
14. Cluster Resource Management: How would you manage cluster resources effectively to run multiple concurrent Spark jobs without contention?
15. Real-Time ETL: Explain how you would design a real-time ETL pipeline using Spark to ingest, transform, and load data into a data warehouse.
16. Handling Large Files: You have a #Spark job that needs to process very large files (e.g., 100 GB). How would you optimize the job to handle such files efficiently?
17. Monitoring and Debugging: What tools and techniques would you use to monitor and debug a Spark job running in production?
18. Delta Lake: How would you use Delta Lake with Spark to manage real-time data lakes and ensure data consistency?
19. Partitioning Strategy: How you would design an effective partitioning strategy for a large dataset.
20. Data Serialization: What serialization formats would you use in Spark for real-time data processing, and why?
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
โค7๐6
Cisco Kafka interview questions for Data Engineers 2024.
โค How do you create a topic in Kafka using the Confluent CLI?
โค Explain the role of the Schema Registry in Kafka.
โค How do you register a new schema in the Schema Registry?
โค What is the importance of key-value messages in Kafka?
โค Describe a scenario where using a random key for messages is beneficial.
โค Provide an example where using a constant key for messages is necessary.
โค Write a simple Kafka producer code that sends JSON messages to a topic.
โค How do you serialize a custom object before sending it to a Kafka topic?
โค Describe how you can handle serialization errors in Kafka producers.
โค Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
โค How do you handle deserialization errors in Kafka consumers?
โค Explain the process of deserializing messages into custom objects.
โค What is a consumer group in Kafka, and why is it important?
โค Describe a scenario where multiple consumer groups are used for a single topic.
โค How does Kafka ensure load balancing among consumers in a group?
โค How do you send JSON data to a Kafka topic and ensure it is properly serialized?
โค Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
โค Explain how you can work with CSV data in Kafka, including serialization and deserialization.
โค Write a Kafka producer code snippet that sends CSV data to a topic.
โค Write a Kafka consumer code snippet that reads and processes CSV data from a topic.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
โค How do you create a topic in Kafka using the Confluent CLI?
โค Explain the role of the Schema Registry in Kafka.
โค How do you register a new schema in the Schema Registry?
โค What is the importance of key-value messages in Kafka?
โค Describe a scenario where using a random key for messages is beneficial.
โค Provide an example where using a constant key for messages is necessary.
โค Write a simple Kafka producer code that sends JSON messages to a topic.
โค How do you serialize a custom object before sending it to a Kafka topic?
โค Describe how you can handle serialization errors in Kafka producers.
โค Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
โค How do you handle deserialization errors in Kafka consumers?
โค Explain the process of deserializing messages into custom objects.
โค What is a consumer group in Kafka, and why is it important?
โค Describe a scenario where multiple consumer groups are used for a single topic.
โค How does Kafka ensure load balancing among consumers in a group?
โค How do you send JSON data to a Kafka topic and ensure it is properly serialized?
โค Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
โค Explain how you can work with CSV data in Kafka, including serialization and deserialization.
โค Write a Kafka producer code snippet that sends CSV data to a topic.
โค Write a Kafka consumer code snippet that reads and processes CSV data from a topic.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐8โค4
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 ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
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 ๐
https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
All the best ๐๐
๐5โค2๐ฅ1
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 Whatโs 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?
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 Whatโs 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?
๐4
Interviewer: You have 2 minutes. Explain the difference between Caching and Persisting in Spark.
โค ๐๐ฎ๐ฐ๐ต๐ถ๐ป๐ด:
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.
โค ๐ฃ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด:
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
โค ๐๐ฒ๐ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐ ๐ฏ๐ฒ๐๐๐ฒ๐ฒ๐ป ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด:
- 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.
โค ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ๐ป ๐๐ผ๐ ๐๐ผ๐๐น๐ฑ ๐๐๐ฒ ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐๐ฒ๐ฟ๐๐๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด
- 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.
โค ๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐ต๐ฎ๐ป๐ฑ๐น๐ฒ ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด ๐๐ป๐ฑ๐ฒ๐ฟ ๐๐ต๐ฒ ๐ต๐ผ๐ผ๐ฑ
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.
Thatโs the difference between Caching and Persisting in Spark.
โค ๐๐ฎ๐ฐ๐ต๐ถ๐ป๐ด:
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.
โค ๐ฃ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด:
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
โค ๐๐ฒ๐ ๐ฑ๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐ ๐ฏ๐ฒ๐๐๐ฒ๐ฒ๐ป ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด:
- 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.
โค ๐๐ ๐ฎ๐บ๐ฝ๐น๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ๐ป ๐๐ผ๐ ๐๐ผ๐๐น๐ฑ ๐๐๐ฒ ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐๐ฒ๐ฟ๐๐๐ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด
- 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.
โค ๐๐ผ๐ ๐ฑ๐ผ๐ฒ๐ ๐ฆ๐ฝ๐ฎ๐ฟ๐ธ ๐ต๐ฎ๐ป๐ฑ๐น๐ฒ ๐ฐ๐ฎ๐ฐ๐ต๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐ฝ๐ฒ๐ฟ๐๐ถ๐๐๐ถ๐ป๐ด ๐๐ป๐ฑ๐ฒ๐ฟ ๐๐ต๐ฒ ๐ต๐ผ๐ผ๐ฑ
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.
Thatโs the difference between Caching and Persisting in Spark.
๐7โค2
๐บ Data engineering Free Courses
1๏ธโฃ Data Engineering Course : Learn the basics of data engineering.
2๏ธโฃ Data Engineer Learning Path course : a comprehensive road map to become a data engineer.
3๏ธโฃ The Data Eng Zoomcamp course : a practical course to learn data engineering
1๏ธโฃ Data Engineering Course : Learn the basics of data engineering.
2๏ธโฃ Data Engineer Learning Path course : a comprehensive road map to become a data engineer.
3๏ธโฃ The Data Eng Zoomcamp course : a practical course to learn data engineering
๐6
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
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
๐ฅ6๐4โค1
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.
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.
๐22๐1
- 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 + 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
๐15โค6๐ฅฐ1
SQL is composed of five key components:
๐๐๐ (๐๐๐ญ๐ ๐๐๐๐ข๐ง๐ข๐ญ๐ข๐จ๐ง ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like CREATE, ALTER, DROP for defining and modifying database structures.
๐๐๐ (๐๐๐ญ๐ ๐๐ฎ๐๐ซ๐ฒ ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like SELECT for querying and retrieving data.
๐๐๐ (๐๐๐ญ๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like INSERT, UPDATE, DELETE for modifying data.
๐๐๐ (๐๐๐ญ๐ ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like GRANT, REVOKE for managing access permissions.
๐๐๐ (๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐): 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.
๐๐๐ (๐๐๐ญ๐ ๐๐๐๐ข๐ง๐ข๐ญ๐ข๐จ๐ง ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like CREATE, ALTER, DROP for defining and modifying database structures.
๐๐๐ (๐๐๐ญ๐ ๐๐ฎ๐๐ซ๐ฒ ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like SELECT for querying and retrieving data.
๐๐๐ (๐๐๐ญ๐ ๐๐๐ง๐ข๐ฉ๐ฎ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like INSERT, UPDATE, DELETE for modifying data.
๐๐๐ (๐๐๐ญ๐ ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐): Commands like GRANT, REVOKE for managing access permissions.
๐๐๐ (๐๐ซ๐๐ง๐ฌ๐๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐ซ๐จ๐ฅ ๐๐๐ง๐ ๐ฎ๐๐ ๐): 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.
๐5๐ฅ4
Data Engineering free courses
Linked Data Engineering
๐ฌ Video Lessons
Rating โญ๏ธ: 5 out of 5
Students ๐จโ๐: 9,973
Duration โฐ: 8 weeks long
Source: openHPI
๐ Course Link
Data Engineering
Credits โณ: 15
Duration โฐ: 4 hours
๐โโ๏ธ Self paced
Source: Google cloud
๐ Course Link
Data Engineering Essentials using Spark, Python and SQL
๐ฌ 402 video lesson
๐โโ๏ธ Self paced
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Data engineering with Azure Databricks
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Perform data engineering with Azure Synapse Apache Spark Pools
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๐ Course Link
Books
Data Engineering
The Data Engineers Guide to Apache Spark
All the best ๐๐
Linked Data Engineering
๐ฌ Video Lessons
Rating โญ๏ธ: 5 out of 5
Students ๐จโ๐: 9,973
Duration โฐ: 8 weeks long
Source: openHPI
๐ Course Link
Data Engineering
Credits โณ: 15
Duration โฐ: 4 hours
๐โโ๏ธ Self paced
Source: Google cloud
๐ Course Link
Data Engineering Essentials using Spark, Python and SQL
๐ฌ 402 video lesson
๐โโ๏ธ Self paced
Teacher: itversity
Resource: Youtube
๐ Course Link
Data engineering with Azure Databricks
Modules โณ: 5
Duration โฐ: 4-5 hours worth of material
๐โโ๏ธ Self paced
Source: Microsoft ignite
๐ Course Link
Perform data engineering with Azure Synapse Apache Spark Pools
Modules โณ: 5
Duration โฐ: 2-3 hours worth of material
๐โโ๏ธ Self paced
Source: Microsoft Learn
๐ Course Link
Books
Data Engineering
The Data Engineers Guide to Apache Spark
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