Preparing for a Spark Interview? Here are 20 Key Differences You Should Know!
1️⃣ Repartition vs. Coalesce: Repartition changes the number of partitions, while coalesce reduces partitions without full shuffle.
2️⃣ Sort By vs. Order By: Sort By sorts data within each partition and may result in partially ordered final results if multiple reducers are used. Order By guarantees total order across all partitions in the final output.
3️⃣ RDD vs. Datasets vs. DataFrames: RDDs are the basic abstraction, Datasets add type safety, and DataFrames optimize for structured data.
4️⃣ Broadcast Join vs. Shuffle Join vs. Sort Merge Join: Broadcast Join is for small tables, Shuffle Join redistributes data, and Sort Merge Join sorts data before joining.
5️⃣ Spark Session vs. Spark Context: Spark Session is the entry point in Spark 2.0+, combining functionality of Spark Context and SQL Context.
6️⃣ Executor vs. Executor Core: Executor runs tasks and manages data storage, while Executor Core handles task execution.
7️⃣ DAG vs. Lineage: DAG (Directed Acyclic Graph) is the execution plan, while Lineage tracks the RDD lineage for fault tolerance.
8️⃣ Transformation vs. Action: Transformation creates RDD/Dataset/DataFrame, while Action triggers execution and returns results to driver.
9️⃣ Narrow Transformation vs. Wide Transformation: Narrow operates on single partition, while Wide involves shuffling across partitions.
🔟 Lazy Evaluation vs. Eager Evaluation: Spark delays execution until action is called (Lazy), optimizing performance.
1️⃣1️⃣ Window Functions vs. Group By: Window Functions compute over a range of rows, while Group By aggregates data into summary.
1️⃣2️⃣ Partitioning vs. Bucketing: Partitioning divides data into logical units, while Bucketing organizes data into equal-sized buckets.
1️⃣3️⃣ Avro vs. Parquet vs. ORC: Avro is row-based with schema, Parquet and ORC are columnar formats optimized for query speed.
1️⃣4️⃣ Client Mode vs. Cluster Mode: Client runs driver in client process, while Cluster deploys driver to the cluster.
1️⃣5️⃣ Serialization vs. Deserialization: Serialization converts data to byte stream, while Deserialization reconstructs data from byte stream.
1️⃣6️⃣ DAG Scheduler vs. Task Scheduler: DAG Scheduler divides job into stages, while Task Scheduler assigns tasks to workers.
1️⃣7️⃣ Accumulators vs. Broadcast Variables: Accumulators aggregate values from workers to driver, Broadcast Variables efficiently broadcast read-only variables.
1️⃣8️⃣ Cache vs. Persist: Cache stores RDD/Dataset/DataFrame in memory, Persist allows choosing storage level (memory, disk, etc.).
1️⃣9️⃣ Internal Table vs. External Table: Internal managed by Spark, External managed externally (e.g., Hive).
2️⃣0️⃣ Executor vs. Driver: Executor runs tasks on worker nodes, Driver manages job execution.
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1️⃣ Repartition vs. Coalesce: Repartition changes the number of partitions, while coalesce reduces partitions without full shuffle.
2️⃣ Sort By vs. Order By: Sort By sorts data within each partition and may result in partially ordered final results if multiple reducers are used. Order By guarantees total order across all partitions in the final output.
3️⃣ RDD vs. Datasets vs. DataFrames: RDDs are the basic abstraction, Datasets add type safety, and DataFrames optimize for structured data.
4️⃣ Broadcast Join vs. Shuffle Join vs. Sort Merge Join: Broadcast Join is for small tables, Shuffle Join redistributes data, and Sort Merge Join sorts data before joining.
5️⃣ Spark Session vs. Spark Context: Spark Session is the entry point in Spark 2.0+, combining functionality of Spark Context and SQL Context.
6️⃣ Executor vs. Executor Core: Executor runs tasks and manages data storage, while Executor Core handles task execution.
7️⃣ DAG vs. Lineage: DAG (Directed Acyclic Graph) is the execution plan, while Lineage tracks the RDD lineage for fault tolerance.
8️⃣ Transformation vs. Action: Transformation creates RDD/Dataset/DataFrame, while Action triggers execution and returns results to driver.
9️⃣ Narrow Transformation vs. Wide Transformation: Narrow operates on single partition, while Wide involves shuffling across partitions.
🔟 Lazy Evaluation vs. Eager Evaluation: Spark delays execution until action is called (Lazy), optimizing performance.
1️⃣1️⃣ Window Functions vs. Group By: Window Functions compute over a range of rows, while Group By aggregates data into summary.
1️⃣2️⃣ Partitioning vs. Bucketing: Partitioning divides data into logical units, while Bucketing organizes data into equal-sized buckets.
1️⃣3️⃣ Avro vs. Parquet vs. ORC: Avro is row-based with schema, Parquet and ORC are columnar formats optimized for query speed.
1️⃣4️⃣ Client Mode vs. Cluster Mode: Client runs driver in client process, while Cluster deploys driver to the cluster.
1️⃣5️⃣ Serialization vs. Deserialization: Serialization converts data to byte stream, while Deserialization reconstructs data from byte stream.
1️⃣6️⃣ DAG Scheduler vs. Task Scheduler: DAG Scheduler divides job into stages, while Task Scheduler assigns tasks to workers.
1️⃣7️⃣ Accumulators vs. Broadcast Variables: Accumulators aggregate values from workers to driver, Broadcast Variables efficiently broadcast read-only variables.
1️⃣8️⃣ Cache vs. Persist: Cache stores RDD/Dataset/DataFrame in memory, Persist allows choosing storage level (memory, disk, etc.).
1️⃣9️⃣ Internal Table vs. External Table: Internal managed by Spark, External managed externally (e.g., Hive).
2️⃣0️⃣ Executor vs. Driver: Executor runs tasks on worker nodes, Driver manages job execution.
Data Engineering Interview Preparation Resources: https://whatsapp.com/channel/0029Vaovs0ZKbYMKXvKRYi3C
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👍5❤4
𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐒𝐩𝐚𝐫𝐤 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
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
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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
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❤7👍6
Cisco Kafka interview questions for Data Engineers 2024.
➤ How do you create a topic in Kafka using the Confluent CLI?
➤ Explain the role of the Schema Registry in Kafka.
➤ How do you register a new schema in the Schema Registry?
➤ What is the importance of key-value messages in Kafka?
➤ Describe a scenario where using a random key for messages is beneficial.
➤ Provide an example where using a constant key for messages is necessary.
➤ Write a simple Kafka producer code that sends JSON messages to a topic.
➤ How do you serialize a custom object before sending it to a Kafka topic?
➤ Describe how you can handle serialization errors in Kafka producers.
➤ Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
➤ How do you handle deserialization errors in Kafka consumers?
➤ Explain the process of deserializing messages into custom objects.
➤ What is a consumer group in Kafka, and why is it important?
➤ Describe a scenario where multiple consumer groups are used for a single topic.
➤ How does Kafka ensure load balancing among consumers in a group?
➤ How do you send JSON data to a Kafka topic and ensure it is properly serialized?
➤ Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
➤ Explain how you can work with CSV data in Kafka, including serialization and deserialization.
➤ Write a Kafka producer code snippet that sends CSV data to a topic.
➤ 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
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➤ How do you create a topic in Kafka using the Confluent CLI?
➤ Explain the role of the Schema Registry in Kafka.
➤ How do you register a new schema in the Schema Registry?
➤ What is the importance of key-value messages in Kafka?
➤ Describe a scenario where using a random key for messages is beneficial.
➤ Provide an example where using a constant key for messages is necessary.
➤ Write a simple Kafka producer code that sends JSON messages to a topic.
➤ How do you serialize a custom object before sending it to a Kafka topic?
➤ Describe how you can handle serialization errors in Kafka producers.
➤ Write a Kafka consumer code that reads messages from a topic and deserializes them from JSON.
➤ How do you handle deserialization errors in Kafka consumers?
➤ Explain the process of deserializing messages into custom objects.
➤ What is a consumer group in Kafka, and why is it important?
➤ Describe a scenario where multiple consumer groups are used for a single topic.
➤ How does Kafka ensure load balancing among consumers in a group?
➤ How do you send JSON data to a Kafka topic and ensure it is properly serialized?
➤ Describe the process of consuming JSON data from a Kafka topic and converting it to a usable format.
➤ Explain how you can work with CSV data in Kafka, including serialization and deserialization.
➤ Write a Kafka producer code snippet that sends CSV data to a topic.
➤ 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 👍👍
👍8❤4
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 👍👍
👍5❤2🔥1
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?
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?
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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.
➤ 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.
👍7❤2
🔺 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
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
👍6
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
🔥6👍4❤1
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.
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- 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
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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.
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.
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