Data Engineers

You will be 18x better at Azure Data Engineering If you cover these topics: 1. Azure Fundamentals • Cloud Computing Basics • Azure Global Infrastructure • Azure Regions and Availability Zones • Resource Groups and Management 2. Azure Storage Solutions • Azure Blob Storage • Azure Data Lake Storage (ADLS) • Azure SQL Database • Cosmos DB 3. Data Ingestion and Integration • Azure Data Factory • Azure Event Hubs • Azure Stream Analytics • Azure Logic Apps 4. Big Data Processing • Azure Databricks • Azure HDInsight • Azure Synapse Analytics • Spark on Azure 5. Serverless Compute • Azure Functions • Azure Logic Apps • Azure App Services • Durable Functions 6. Data Warehousing • Azure Synapse Analytics (formerly SQL Data Warehouse) • Dedicated SQL Pool vs. Serverless SQL Pool • Data Marts • PolyBase 7. Data Modeling • Star Schema • Snowflake Schema • Slowly Changing Dimensions • Data Partitioning Strategies 8. ETL and ELT Pipelines • Extract, Transform, Load (ETL) Patterns • Extract, Load, Transform (ELT) Patterns • Azure Data Factory Pipelines • Data Flow Activities 9. Data Security • Azure Key Vault • Role-Based Access Control (RBAC) • Data Encryption (At Rest, In Transit) • Managed Identities 10. Monitoring and Logging • Azure Monitor • Azure Log Analytics • Azure Application Insights • Metrics and Alerts 11. Scalability and Performance • Vertical vs. Horizontal Scaling • Load Balancers • Autoscaling • Caching with Azure Redis Cache 12. Cost Management • Azure Cost Management and Billing • Reserved Instances and Spot VMs • Cost Optimization Strategies • Pricing Calculators 13. Networking • Virtual Networks (VNets) • VPN Gateway • ExpressRoute • Azure Firewall and NSGs 14. CI/CD in Azure • Azure DevOps Pipelines • Infrastructure as Code (IaC) with ARM Templates • GitHub Actions • Terraform on Azure Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

20 recently asked 𝗞𝗔𝗙𝗞𝗔 interview questions. - 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. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Important Data Engineering Concepts for Interviews 1. ETL Processes: Understand the ETL (Extract, Transform, Load) process, including how to design and implement efficient pipelines to move data from various sources to a data warehouse or data lake. Familiarize yourself with tools like Apache NiFi, Talend, and AWS Glue. 2. Data Warehousing: Know the fundamentals of data warehousing, including the star schema, snowflake schema, and how to design a data warehouse that supports efficient querying and reporting. Learn about popular data warehousing solutions like Amazon Redshift, Google BigQuery, and Snowflake. 3. Data Modeling: Master data modeling concepts, including normalization and denormalization, to design databases that are optimized for both read and write operations. Understand entity-relationship (ER) diagrams and how to use them to model data relationships. 4. Big Data Technologies: Gain expertise in big data frameworks like Apache Hadoop and Apache Spark for processing large datasets. Understand the roles of HDFS, MapReduce, Hive, and Pig in the Hadoop ecosystem, and how Spark’s in-memory processing can accelerate data processing. 5. Data Lakes: Learn about data lakes as a storage solution for raw, unstructured, and semi-structured data. Understand the key differences between data lakes and data warehouses, and how to use tools like Apache Hudi and Delta Lake to manage data lakes efficiently. 6. SQL and NoSQL Databases: Be proficient in SQL for querying and managing relational databases like MySQL, PostgreSQL, and Oracle. Also, understand when and how to use NoSQL databases like MongoDB, Cassandra, and DynamoDB for storing and querying unstructured or semi-structured data. 7. Data Pipelines: Learn how to design, build, and manage data pipelines that automate the flow of data from source systems to target destinations. Familiarize yourself with orchestration tools like Apache Airflow, Luigi, and Prefect for managing complex workflows. 8. APIs and Data Integration: Understand how to integrate data from various APIs and third-party services into your data pipelines. Learn about RESTful APIs, GraphQL, and how to handle data ingestion from external sources securely and efficiently. 9. Data Streaming: Gain knowledge of real-time data processing using streaming technologies like Apache Kafka, Apache Flink, and Amazon Kinesis. Learn how to build systems that can process and analyze data in real time as it flows through the system. 10. Cloud Platforms: Get familiar with cloud-based data engineering services offered by AWS, Azure, and Google Cloud. Understand how to use services like AWS S3, Azure Data Lake, Google Cloud Storage, AWS Redshift, and BigQuery for data storage, processing, and analysis. 11. Data Governance and Security: Learn best practices for data governance, including how to implement data quality checks, lineage tracking, and metadata management. Understand data security concepts like encryption, access control, and GDPR compliance to protect sensitive data. 12. Automation and Scripting: Be proficient in scripting languages like Python, Bash, or PowerShell to automate repetitive tasks, manage data pipelines, and perform ad-hoc data processing. 13. Data Versioning and Lineage: Understand the importance of data versioning and lineage for tracking changes to data over time. Learn how to use tools like Apache Atlas or DataHub for managing metadata and ensuring traceability in your data pipelines. 14. Containerization and Orchestration: Learn how to deploy and manage data engineering workloads using containerization tools like Docker and orchestration platforms like Kubernetes. Understand the benefits of using containers for scaling and maintaining consistency across environments. 15. Monitoring and Logging: Implement logging for data pipelines to ensure they run smoothly and efficiently. Familiarize yourself with tools like Prometheus, Grafana, etc. for real-time monitoring and troubleshooting. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Pyspark Interview Questions!! Interviewer: "How would you remove duplicates from a large dataset in PySpark?" Candidate: "To remove duplicates from a large dataset in PySpark, I would follow these steps: Step 1: Load the dataset into a DataFrame

df = spark.read.csv("path/to/data.csv", header=True, inferSchema=True)

Step 2: Check for duplicates

duplicate_count = df.count() - df.dropDuplicates().count()
print(f"Number of duplicates: {duplicate_count}")

Step 3: Partition the data to optimize performance

df_repartitioned = df.repartition(100)

Step 4: Remove duplicates using the dropDuplicates() method

df_no_duplicates = df_repartitioned.dropDuplicates()

Step 5: Cache the resulting DataFrame to avoid recomputing

df_no_duplicates.cache()

Step 6: Save the cleaned dataset

df_no_duplicates.write.csv("path/to/cleaned/data.csv", header=True)

Interviewer: "That's correct! Can you explain why you partitioned the data in Step 3?" Candidate: "Yes, partitioning the data helps to distribute the computation across multiple nodes, making the process more efficient and scalable." Interviewer: "Great answer! Can you also explain why you cached the resulting DataFrame in Step 5?" Candidate: "Caching the DataFrame avoids recomputing the entire dataset when saving the cleaned data, which can significantly improve performance." Interviewer: "Excellent! You have demonstrated a clear understanding of optimizing duplicate removal in PySpark." Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Pyspark interview questions for Data Engineer 2024. 1. How do you handle data transfer between PySpark and external systems? 2. How do you deal with missing or null values in PySpark DataFrames? 3. Are there any specific strategies or functions you prefer for handling missing data? 4. What is broadcasting, and how is it useful in PySpark? 5. What is Spark and why is it preferred over MapReduce? 6. How does Spark handle fault tolerance? 7. What is the significance of caching in Spark? 8. Explain the concept of broadcast variables in Spark 9. What is the role of Spark SQL in data processing? 10. How does Spark handle memory management? 11. Discuss the significance of partitioning in Spark. 12. Explain the difference between RDDs, DataFrames, and Datasets. 13. What are the different deployment modes available in Spark? 14. What is PySpark, and how does it differ from Python Pandas? 15. Explain the difference between RDD, DataFrame, and Dataset in PySpark. 16. How do you create a DataFrame in PySpark? 17. What is lazy evaluation in PySpark and why is it important? 18. How can you handle missing or null values in PySpark DataFrames? 19. What are transformations and actions in PySpark, and can you give examples of each? 20. How do you perform joins between two DataFrames in PySpark? What are the joins available in PySpark? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Breaking in to data engineering can be 100% free and 100% project-based! Here are the steps: - find a REST API you like as a data source. Maybe stocks, sports games, Pokémon, etc. - learn Python to build a short script that reads that REST API and initially dumps to a CSV file - get a Snowflake or BigQuery free trial account. Update the Python script to dump the data there - build aggregations on top of the data in SQL using things like GROUP BY keyword - set up an Astronomer account to build an Airflow pipeline to automate this data ingestion - connect something like Tableau to your data warehouse and build a fancy chart that updates to show off your hard work! Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Data engineering interviews will be 20x easier if you learn these tools in sequence👇 ➤ 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀 - SQL is very important - Learn Python Funddamentals ➤ 𝗢𝗻-𝗣𝗿𝗲𝗺 𝘁𝗼𝗼𝗹𝘀 - Learn Pyspark - In Depth (Processing tool) - Hadoop (Distrubuted Storage) - Hive (Datawarehouse) - Airflow (Orchestration) - Kafka (Streaming platform) - CICD for production readiness ➤ 𝗖𝗹𝗼𝘂𝗱 (𝗔𝗻𝘆 𝗼𝗻𝗲) - AWS - Azure - GCP ➤ Do a couple of projects to get a good feel of it. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

20 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐬𝐜𝐞𝐧𝐚𝐫𝐢𝐨-𝐛𝐚𝐬𝐞𝐝 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 Here are few Interview questions that are often asked in PySpark interviews to evaluate if candidates have hands-on experience or not !! 𝐋𝐞𝐭𝐬 𝐝𝐢𝐯𝐢𝐝𝐞 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 4 𝐩𝐚𝐫𝐭𝐬 1. Data Processing and Transformation 2. Performance Tuning and Optimization 3. Data Pipeline Development 4. Debugging and Error Handling 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐚𝐧𝐝 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: 1. Explain how you would handle large datasets in PySpark. How do you optimize a PySpark job for performance? 2. How would you join two large datasets (say 100GB each) in PySpark efficiently? 3. Given a dataset with millions of records, how would you identify and remove duplicate rows using PySpark? 4. You are given a DataFrame with nested JSON. How would you flatten the JSON structure in PySpark? 5. How do you handle missing or null values in a DataFrame? What strategies would you use in different scenarios? 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 6. How do you debug and optimize PySpark jobs that are taking too long to complete? 7. Explain what a shuffle operation is in PySpark and how you can minimize its impact on performance. 8. Describe a situation where you had to handle data skew in PySpark. What steps did you take? 9. How do you handle and optimize PySpark jobs in a YARN cluster environment? 10. Explain the difference between repartition() and coalesce() in PySpark. When would you use each? 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 11. Describe how you would implement an ETL pipeline in PySpark for processing streaming data. 12. How do you ensure data consistency and fault tolerance in a PySpark job? 13. You need to aggregate data from multiple sources and save it as a partitioned Parquet file. How would you do this in PySpark? 14. How would you orchestrate and manage a complex PySpark job with multiple stages? 15. Explain how you would handle schema evolution in PySpark while reading and writing data. 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐚𝐧𝐝 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: 16. Have you encountered out-of-memory errors in PySpark? How did you resolve them? 17. What steps would you take if a PySpark job fails midway through execution? How do you recover from it? 18. You encounter a Spark task that fails repeatedly due to data corruption in one of the partitions. How would you handle this? 19. Explain a situation where you used custom UDFs (User Defined Functions) in PySpark. What challenges did you face, and how did you overcome them? 20. Have you had to debug a PySpark (Python + Apache Spark) job that was producing incorrect results? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Pre-Interview Checklist for Big Data Engineer Roles. ➤ SQL Essentials: - SELECT statements including WHERE, ORDER BY, GROUP BY, HAVING - Basic JOINS: INNER, LEFT, RIGHT, FULL - Aggregate functions: COUNT, SUM, AVG, MAX, MIN - Subqueries, Common Table Expressions (WITH clause) - CASE statements, advanced JOIN techniques, and Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK) ➤ Python Programming: - Basic syntax, control structures, data structures (lists, dictionaries) - Pandas & NumPy for data manipulation: DataFrames, Series, groupby ➤ Hadoop Ecosystem Proficiency: - Understanding HDFS architecture, replication, and block management. - Mastery of MapReduce for distributed data processing. - Familiarity with YARN for resource management and job scheduling. ➤ Hive Skills: - Writing efficient HiveQL queries for data retrieval and manipulation. - Optimizing table performance with partitioning and bucketing. - Working with ORC, Parquet, and Avro file formats. ➤ Apache Spark: - Spark architecture - RDD, Dataframe, Datasets, Spark SQL - Spark optimization techniques - Spark Streaming ➤ Apache HBase: - Designing effective row keys and understanding HBase’s data model. - Performing CRUD operations and integrating HBase with other big data tools. ➤ Apache Kafka: - Deep understanding of Kafka architecture, including producers, consumers, and brokers. - Implementing reliable message queuing systems and managing data streams. - Integrating Kafka with ETL pipelines. ➤ Apache Airflow: - Designing and managing DAGs for workflow scheduling. - Handling task dependencies and monitoring workflow execution. ➤ Data Warehousing and Data Modeling: - Concepts of OLAP vs. OLTP - Star and Snowflake schema designs - ETL processes: Extract, Transform, Load - Data lake vs. data warehouse - Balancing normalization and denormalization in data models. ➤ Cloud Computing for Data Engineering: - Benefits of cloud services (AWS, Azure, Google Cloud) - Data storage solutions: S3, Azure Blob Storage, Google Cloud Storage - Cloud-based data analytics tools: BigQuery, Redshift, Snowflake - Cost management and optimization strategies Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

- PySpark + DataFrame API = Data Manipulation - PySpark + RDD = Distributed Datasets - PySpark + filter() = Data Filtering - PySpark + join() = Data Integration - PySpark + groupBy() = Data Aggregation - PySpark + orderBy() = Data Sorting - PySpark + union() = Combining Datasets - PySpark + withColumn() = Data Transformation - PySpark + select() = Column Selection - PySpark + SQL Queries = SQL Integration - PySpark + createOrReplaceTempView() = Virtual Tables - PySpark + map() = Data Mapping - PySpark + reduceByKey() = Data Reduction - PySpark + partitionBy() = Data Partitioning - PySpark + broadcast() = Data Broadcasting - PySpark + accumulators = Shared Variables - PySpark + Spark SQL = Structured Data - PySpark + DataFrame Caching = Performance Optimization - PySpark + Window Functions = Advanced Analytics - PySpark + UDFs = Custom Functions - PySpark + Machine Learning = Scalable Models - PySpark + GraphX = Graph Processing - PySpark + Streaming = Real-Time Processing - PySpark + DataFrame Joins = Efficient Merging - PySpark + MLlib = Machine Learning - PySpark + Structured Streaming = Continuous Processing - PySpark + Pipeline API = Workflow Automation - PySpark + Delta Lake = Reliable Lakes - PySpark + Databricks = Cloud Platform - PySpark + ETL Pipelines = Data Extraction - PySpark + Performance Tuning = Query Efficiency - PySpark + Cluster Management = Distributed Computing Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Pyspark interview questions for Data Engineer 2024. 1. How do you deploy PySpark applications in a production environment? 2. What are some best practices for monitoring and logging PySpark jobs? 3. How do you manage resources and scheduling in a PySpark application? 4. Write a PySpark job to perform a specific data processing task (e.g., filtering data, aggregating results). 5. You have a dataset containing user activity logs with missing values and inconsistent data types. Describe how you would clean and standardize this dataset using PySpark. 6. Given a dataset with nested JSON structures, how would you flatten it into a tabular format using PySpark? 8. Your PySpark job is running slower than expected due to data skew. Explain how you would identify and address this issue. 9. You need to join two large datasets, but the join operation is causing out-of-memory errors. What strategies would you use to optimize this join? 10. Describe how you would set up a real-time data pipeline using PySpark and Kafka to process streaming data. 11. You are tasked with processing real-time sensor data to detect anomalies. Explain the steps you would take to implement this using PySpark. 12. Describe how you would design and implement an ETL pipeline in PySpark to extract data from an RDBMS, transform it, and load it into a data warehouse. 13. Given a requirement to process and transform data from multiple sources (e.g., CSV, JSON, and Parquet files), how would you handle this in a PySpark job? 14. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this. 15. Describe how you would use PySpark to join data from a Hive table and a Kafka stream. 16. You need to integrate data from an external API into your PySpark pipeline. Explain how you would achieve this. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Part 1: Basic Concepts and Architecture 1. What is a stream in Snowflake, and what are the columns present in a stream? 2. What is the architecture of Snowflake? 3. What is a Snowpipe in the context of Snowflake? 4. Can you explain the concept of a warehouse in Snowflake? 5. What is the data flow, and how many layers are in our projects? 6. How do you convert JSON to the Snowflake VARIANT data type? 7. How are task dependencies managed in Snowflake? 8. Is there a specific table for maintaining notification history in Snowflake? 9. What are alternative methods for loading data into Snowflake without using JSON functions? 10. How can you set up error notifications in Snowflake? Part 2: Data Management and ETL Processes 1. Could you explain the process of data sharing in Snowflake? 2. Explain the relationship between AWS and SF. 3. How do you move 100 GB of data into SF? Describe the steps you would follow. 4. Differentiate between a View and a Materialized View. 5. Explain the concept of a Merge statement in the context of a relational database. 6. What is the purpose of the pattern function in Snowflake? 7. Have you worked with Snowpipe? If so, describe your experience in creating and using Snowpipe. 8. How can you create a table in Oracle with a time/travel retention period to go back before 12 days? 9. What is the maximum size of a file that can be loaded into an S3 bucket? 10. What are the types of Slowly Changing Dimensions (SCD)? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

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.

Pyspark interview questions for Data Engineer 2024. 1. Describe the shuffle operation in Apache Spark and its impact on performance. 2. What are the different types of joins available in Apache Spark SQL? 3. Provide examples of when to use each. Discuss the optimizations performed by Apache Spark’s Catalyst optimizer. 4. Explain how broadcast variables work in Apache Spark and when they should be used. 5. How does Apache Spark handle memory management and garbage collection in its execution model? 6. Describe the architecture of Apache Spark and its components in a distributed environment. What are the different deployment modes available for running Apache Spark applications? When would you choose each mode? 7. Explain the role of the SparkContext in an Apache Spark application and how it differs from the SparkSession. 8. Discuss the performance tuning techniques you would employ to optimize Apache Spark jobs. 9. How does Apache Spark handle skewed data when performing aggregations or group-bys? Explain the concept of window functions in Apache Spark SQL and provide examples of their usage. 10. Discuss the role of lineage, checkpoints, and RDD persistence in ensuring fault tolerance in Apache Spark. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

- 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

Data engineering interviews will be 10x easier if you learn these tools in sequence👇 ➤ 𝗣𝗿𝗲-𝗿𝗲𝗾𝘂𝗶𝘀𝗶𝘁𝗲𝘀 - SQL is very important - Learn Python Funddamentals - Pandas and Numpy Library in Python. ➤ 𝗢𝗻-𝗣𝗿𝗲𝗺 𝘁𝗼𝗼𝗹𝘀 - Learn Pyspark - In Depth (Processing tool) - Hadoop (Distrubuted Storage) - Hive (Datawarehouse) - Hbase (NoSQL Database) - Airflow (Orchestration) - Kafka (Streaming platform) - CICD for production readiness ➤ 𝗖𝗹𝗼𝘂𝗱 (𝗔𝗻𝘆 𝗼𝗻𝗲) - AWS - Azure - GCP ➤ Do a couple of projects to get a good feel of it. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

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.