Data Engineers

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. Data Engineering Interview Preparation Resources: https://topmate.io/analyst/910180 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://topmate.io/analyst/910180

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Data Warehousing interview questions 𝗣𝗛𝗔𝗦𝗘 𝟭 - 𝗕𝗮𝘀𝗶𝗰𝘀 • What is a data warehouse, and why is it important? • Explain the difference between OLTP and OLAP systems. • What are the key components of a data warehouse architecture? • What is ETL, and how does it work in data warehousing? • What are facts and dimensions in a data warehouse? 𝗣𝗛𝗔𝗦𝗘 𝟮 - 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 • Explain the different types of slowly changing dimensions (SCD). • What is a star schema, and how does it differ from a snowflake schema? • How do you handle data quality issues in a data warehouse? • What is a surrogate key, and why is it used in data warehousing? • How do you optimize query performance in a data warehouse? 𝗣𝗛𝗔𝗦𝗘 𝟯 - 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 • How would you design a data warehouse for real-time analytics? • Explain the process of building a data warehouse using dimensional modeling. • How would you handle the challenge of maintaining data integrity across multiple sources in a data warehouse? • What are the best practices for data warehouse maintenance and performance tuning? • How do you ensure data security and privacy in a data warehouse? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

- Azure + Data Factory = Data Pipelines - Azure + Synapse Analytics = Data Warehousing & Analytics - Azure + Databricks = Collaborative Analytics - Azure + Cosmos DB = NoSQL Databases - Azure + HDInsight = Hadoop & Big Data - Azure + Blob Storage = Scalable Storage - Azure + Event Hubs = Streaming Data - Azure + Stream Analytics = Real-Time Analytics - Azure + Virtual Network = Network Management - Azure + Monitor = Cloud Monitoring - AWS + Glue = ETL and Data Integration - AWS + Redshift = Data Warehousing - AWS + EMR = Big Data Processing - AWS + S3 = Object Storage - AWS + Kinesis = Real-Time Data Streaming - AWS + RDS = Managed Databases - AWS + DynamoDB = NoSQL Databases - AWS + Data Pipeline = Data Workflow Orchestration - AWS + IAM = Identity Management - AWS + CloudWatch = Monitoring and Logging - GCP + Dataflow = Stream & Batch Processing - GCP + BigQuery = Data Warehousing - GCP + Dataproc = Managed Hadoop & Spark - GCP + Cloud Storage = Object Storage - GCP + Pub/Sub = Messaging & Event Ingestion - GCP + Dataprep = Data Preparation - GCP + Bigtable = NoSQL Databases - GCP + IAM = Access Management - GCP + VPC = Virtual Private Cloud - GCP + Composer = Workflow Orchestration Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

𝐒𝐞𝐜𝐨𝐧𝐝 𝐫𝐨𝐮𝐧𝐝 𝐨𝐟 𝐂𝐚𝐩𝐠𝐞𝐦𝐢𝐧𝐢 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 : : : 1. Describe your work experience. 2. Provide a detailed explanation of a project, including the data sources, file formats, and methods for file reading. 3. Discuss the transformation techniques you have utilized, offering an example and explanation. 4. Explain the process of reading web API data in Spark, including detailed code explanation. 5. How do you convert lists into data frames? 6. What is the method for reading JSON files in Spark? 7. How do you handle complex data? When is it appropriate to use the "explode" function? 8. How do you determine the continuation of a process and identify necessary transformations for complex data? 9. What actions do you take if a Spark job fails? How do you troubleshoot and find a solution? 10. How do you address performance issues? Explain a scenario where a job is slow and how you would diagnose and resolve it. 11. Given a dataframe with a "department" column, explain how you would add a new employee to a department, specifying their salary and increment. 12. Explain the scenario for finding the highest salary using SQL. 13. If you have three data frames, write SQL queries to join them based on a common column. 14. When is it appropriate to use partitioning or bucketing in Spark? How do you determine when to use each technique? How do you assess cardinality? 15. How do you check for improper memory allocation? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Interview questions for Data Architect and Data Engineer positions: Design and Architecture 1.⁠ ⁠Design a data warehouse architecture for a retail company. 2.⁠ ⁠How would you approach data governance in a large organization? 3.⁠ ⁠Describe a data lake architecture and its benefits. 4.⁠ ⁠How do you ensure data quality and integrity in a data warehouse? 5.⁠ ⁠Design a data mart for a specific business domain (e.g., finance, healthcare). Data Modeling and Database Design 1.⁠ ⁠Explain the differences between relational and NoSQL databases. 2.⁠ ⁠Design a database schema for a specific use case (e.g., e-commerce, social media). 3.⁠ ⁠How do you approach data normalization and denormalization? 4.⁠ ⁠Describe entity-relationship modeling and its importance. 5.⁠ ⁠How do you optimize database performance? Data Security and Compliance 1.⁠ ⁠Describe data encryption methods and their applications. 2.⁠ ⁠How do you ensure data privacy and confidentiality? 3.⁠ ⁠Explain GDPR and its implications on data architecture. 4.⁠ ⁠Describe access control mechanisms for data systems. 5.⁠ ⁠How do you handle data breaches and incidents? Data Engineer Interview Questions!! Data Processing and Pipelines 1.⁠ ⁠Explain the concepts of batch processing and stream processing. 2.⁠ ⁠Design a data pipeline using Apache Beam or Apache Spark. 3.⁠ ⁠How do you handle data integration from multiple sources? 4.⁠ ⁠Describe data transformation techniques (e.g., ETL, ELT). 5.⁠ ⁠How do you optimize data processing performance? Big Data Technologies 1.⁠ ⁠Explain Hadoop ecosystem and its components. 2.⁠ ⁠Describe Spark RDD, DataFrame, and Dataset. 3.⁠ ⁠How do you use NoSQL databases (e.g., MongoDB, Cassandra)? 4.⁠ ⁠Explain cloud-based big data platforms (e.g., AWS, GCP, Azure). 5.⁠ ⁠Describe containerization using Docker. Data Storage and Retrieval 1.⁠ ⁠Explain data warehousing concepts (e.g., fact tables, dimension tables). 2.⁠ ⁠Describe column-store and row-store databases. 3.⁠ ⁠How do you optimize data storage for query performance? 4.⁠ ⁠Explain data caching mechanisms. 5.⁠ ⁠Describe graph databases and their applications. Behavioral and Soft Skills 1.⁠ ⁠Can you describe a project you led and the challenges you faced? 2.⁠ ⁠How do you collaborate with cross-functional teams? 3.⁠ ⁠Explain your experience with Agile development methodologies. 4.⁠ ⁠Describe your approach to troubleshooting complex data issues. 5.⁠ ⁠How do you stay up-to-date with industry trends and technologies? Additional Tips 1.⁠ ⁠Review the company's technology stack and be prepared to discuss relevant tools and technologies. 2.⁠ ⁠Practice whiteboarding exercises to improve your design and problem-solving skills. 3.⁠ ⁠Prepare examples of your experience with data architecture and engineering concepts. 4.⁠ ⁠Demonstrate your ability to communicate complex technical concepts to non-technical stakeholders. 5.⁠ ⁠Show enthusiasm and passion for data architecture and engineering. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

🔥 ETL vs ELT: What's the Difference? When it comes to data processing, two key approaches stand out: ETL and ELT. Both involve transforming data, but the processes differ significantly! 🔹 ETL (Extract, Transform, Load) - Extract data from various sources (databases, APIs, etc.) - Transform data before loading it into the storage (cleaning, aggregating, formatting) - Load the transformed data into the data warehouse (DWH) ✏️ Key point: Data is transformed before being loaded into the storage. 🔹 ELT (Extract, Load, Transform) - Extract data from sources - Load raw data into the data warehouse - Transform the data after it's loaded, using the power of the data warehouse’s computational resources ✏️ Key point: Data is loaded into the storage first, and transformation happens afterward. 🎯 When to use which? - ETL is ideal for structured data and traditional systems where pre-processing is crucial. - ELT is better suited for handling large volumes of data in modern cloud-based architectures. Which one works best for your project? 🤔

Data engineering Interview questions: Accenture Q1.Which Integration Runtime (IR) should be used for copying data from an on-premise database to Azure? Q2.Explain the differences between a Scheduled Trigger and a Tumbling Window Trigger in Azure Data Factory. When would you use each? Q3. What is Azure Data Factory (ADF), and how does it enable ETL and ELT processes in a cloud environment? Q4.Describe Azure Data Lake and its role in a data architecture. How does it differ from Azure Blob Storage? Q5. What is an index in a database table? Discuss different types of indexes and their impact on query performance. Q6.Given two datasets, explain how the number of records will vary for each type of join (Inner Join, Left Join, Right Join, Full Outer Join). Q7.What are the Control Flow activities in the Azure Data Factory? Explain how they differ from Data Flow activities and their typical use cases. Q8. Discuss key concepts in data modeling, including normalization and denormalization. How do security concerns influence your choice of Synapse table types in a given scenario? Provide an example of a scenario-based ADF pipeline. Q9. What are the different types of Integration Runtimes (IR) in Azure Data Factory? Discuss their use cases and limitations. Q10.How can you mask sensitive data in the Azure SQL Database? What are the different masking techniques available? Q11.What is Azure Integration Runtime (IR), and how does it support data movement across different networks? Q12.Explain Slowly Changing Dimension (SCD) Type 1 in a data warehouse. How does it differ from SCD Type 2? Q13.SQL questions on window functions - rolling sum and lag/lead based. How do window functions differ from traditional aggregate functions? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Don't aim for this: SQL - 100% Python - 0% PySpark - 0% Cloud - 0% Aim for this: SQL - 25% Python - 25% PySpark - 25% Cloud - 25% You don't need to know everything straight away. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Life of a Data Engineer..... Business user : Can we add a filter on this dashboard. This will help us track a critical metric. me : sure this should be a quick one. Next day : I quickly opened the dashboard to find the column in the existing dashboard's data sources.  -- column not found Spent a couple of hours to identify the data source and how to bring the column into the existence data pipeline which feeds the dashboard( table granularity , join condition etc..). Then comes the pipeline changes , data model changes , dashboard changes , validation/testing. Finally deploying to production and a simple email to the user that the filter has been added. A small change in the front end but a lot of work in the backend to bring that column to life. Never underestimate data engineers and data pipelines 💪

Here are three PySpark questions: Scenario 1: Data Aggregation Interviewer: "How would you aggregate data by category and calculate the sum of sales, handling missing values and grouping by multiple columns?" Candidate:

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

# Handle missing values
df_filled = df.fillna(0)

# Aggregate data
from pyspark.sql.functions import sum, col
df_aggregated = df_filled.groupBy("category", "region").agg(sum(col("sales")).alias("total_sales"))

# Sort the results
df_aggregated_sorted = df_aggregated.orderBy("total_sales", ascending=False)

# Save the aggregated DataFrame
df_aggregated_sorted.write.csv("path/to/aggregated/data.csv", header=True)

Scenario 2: Data Transformation Interviewer: "How would you transform a DataFrame by converting a column to timestamp, handling invalid dates and extracting specific date components?" Candidate:

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

# Convert column to timestamp
from pyspark.sql.functions import to_timestamp, col
df_transformed = df.withColumn("date_column", to_timestamp(col("date_column"), "yyyy-MM-dd"))

# Handle invalid dates
df_transformed_filtered = df_transformed.filter(col("date_column").isNotNull())

# Extract date components
from pyspark.sql.functions import year, month, dayofmonth
df_transformed_extracted = df_transformed_filtered.withColumn("year", year(col("date_column"))).withColumn("month", month(col("date_column"))).withColumn("day", dayofmonth(col("date_column")))

# Save the transformed DataFrame
df_transformed_extracted.write.csv("path/to/transformed/data.csv", header=True)

Scenario 3: Data Partitioning Interviewer: "How would you partition a large DataFrame by date and save it to parquet format, handling data skewness and optimizing storage?" Candidate:

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

# Partition by date
df_partitioned = df.repartitionByRange("date_column")

# Save to parquet format
df_partitioned.write.parquet("path/to/partitioned/data.parquet", partitionBy=["date_column"])

# Optimize storage
df_partitioned.write.option("compression", "snappy").parquet("path/to/partitioned/data.parquet", partitionBy=["date_column"])

Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Two Commonly Asked Pyspark Inrerview Questions!!: Scenario 1: Handling Missing Values Interviewer: "How would you handle missing values in a PySpark DataFrame?" Candidate:

from pyspark.sql.functions import when, isnan

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

# Check for missing values
missing_count = df.select([count(when(isnan(c), c)).alias(c) for c in df.columns])

# Replace missing values with mean
from pyspark.sql.functions import mean
mean_values = df.agg(*[mean(c).alias(c) for c in df.columns])
df_filled = df.fillna(mean_values)

# Save the cleaned DataFrame
df_filled.write.csv("path/to/cleaned/data.csv", header=True)

Interviewer: "That's correct! Can you explain why you used the fillna() method?" Candidate: "Yes, fillna() replaces missing values with the specified value, in this case, the mean of each column." *Scenario 2: Data Aggregation* Interviewer: "How would you aggregate data by category and calculate the average sales amount?" Candidate:

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

# Aggregate data by category
from pyspark.sql.functions import avg
df_aggregated = df.groupBy("category").agg(avg("sales").alias("avg_sales"))

# Sort the results
df_aggregated_sorted = df_aggregated.orderBy("avg_sales", ascending=False)

# Save the aggregated DataFrame
df_aggregated_sorted.write.csv("path/to/aggregated/data.csv", header=True)

Interviewer: "Great answer! Can you explain why you used the groupBy() method?" Candidate: "Yes, groupBy() groups the data by the specified column, in this case, 'category', allowing us to perform aggregation operations." Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

15 of my favourite Pyspark interview questions for Data Engineer 2024. 1. Can you provide an overview of your experience working with PySpark and big data processing? 2. What motivated you to specialize in PySpark, and how have you applied it in your previous roles? 3. Explain the basic architecture of PySpark. 4. How does PySpark relate to Apache Spark, and what advantages does it offer in distributed data processing? 5. Describe the difference between a DataFrame and an RDD in PySpark. 6. Can you explain transformations and actions in PySpark DataFrames? 7. Provide examples of PySpark DataFrame operations you frequently use. 8. How do you optimize the performance of PySpark jobs? 9. Can you discuss techniques for handling skewed data in PySpark? 10. Explain how data serialization works in PySpark. 11. Discuss the significance of choosing the right compression codec for your PySpark applications. 12. How do you deal with missing or null values in PySpark DataFrames? 13. Are there any specific strategies or functions you prefer for handling missing data? 14. Describe your experience with PySpark SQL. 15. How do you execute SQL queries on PySpark DataFrames? Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

𝗞𝗔𝗙𝗞𝗔 interview questions for Data Engineer 2024. - Explain the role of a broker in a Kafka cluster. - How do you scale a Kafka cluster horizontally? - Describe the process of adding a new broker to an existing Kafka cluster. - What is a Kafka topic, and how does it differ from a partition? - How do you determine the optimal number of partitions for a topic? - Describe a scenario where you might need to increase the number of partitions in a Kafka topic. - How does a Kafka producer work, and what are some best practices for ensuring high throughput? - Explain the role of a Kafka consumer and the concept of consumer groups. - Describe a scenario where you need to ensure that messages are processed in order. - What is an offset in Kafka, and why is it important? - How can you manually commit offsets in a Kafka consumer? - Explain how Kafka manages offsets for consumer groups. - What is the purpose of having replicas in a Kafka cluster? - Describe a scenario where a broker fails and how Kafka handles it with replicas. - How do you configure the replication factor for a topic? - What is the difference between synchronous and asynchronous commits in Kafka? - Provide a scenario where you would prefer using asynchronous commits. - Explain the potential risks associated with asynchronous commits. - How do you set up a Kafka cluster using Confluent Kafka? - Describe the steps to configure Confluent Control Center for monitoring a Kafka cluster. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Pyspark Interview Questions!! Interviewer: "Imagine you're working with a massive dataset in PySpark, and suddenly, your code comes to a grinding halt. What's the first thing you'd do to optimize it, and why?" Candidate: "That's a great question! I'd start by checking the data partitioning. If the data is skewed or not properly partitioned, it can lead to performance issues. I'd use df.repartition() to redistribute the data and ensure it's evenly split across executors." Interviewer: "That's a good start. What other optimization techniques would you consider?" Candidate: "Well, here are a few:  1.⁠ ⁠Caching: Cache frequently used data using df.cache() or df.persist().  2.⁠ ⁠Broadcast Join: Use broadcast join for smaller datasets to reduce shuffle.  3.⁠ ⁠Data Compression: Compress data using algorithms like Snappy or Gzip.  4.⁠ ⁠Filter Early: Apply filters before joining or grouping.  5.⁠ ⁠Select Relevant Columns: Only select needed columns using df.select().  6.⁠ ⁠Avoid Using collect(): Use take() or show() instead.  7.⁠ ⁠Optimize Aggregations: Use groupBy() and agg() instead of map().  8.⁠ ⁠Increase Executor Memory: Allocate more memory to executors.  9.⁠ ⁠Increase Executor Cores: Allocate more cores to executors. 10.⁠ ⁠Monitor Performance: Use Spark UI or metrics to monitor performance. Interviewer: "Excellent! How would you determine the optimal caching strategy?" Candidate: "I'd monitor the cache hit ratio and adjust the caching strategy accordingly. If the cache hit ratio is low, I might consider using a different caching level or adjusting the cache size." Interviewer: "Great thinking! What about query optimization? How would you optimize a complex query?" Candidate: "I'd:  1.⁠ ⁠Analyze the Query Plan: Use explain() to identify performance bottlenecks.  2.⁠ ⁠Optimize Joins: Use efficient join algorithms like sort-merge join.  3.⁠ ⁠Optimize Aggregations: Use groupBy() and agg() instead of map().  4.⁠ ⁠Avoid Correlated Subqueries: Rewrite subqueries to avoid correlation. Interviewer: "Impressive! Last question: How would you handle a scenario where the data grows exponentially, and the existing optimization strategies no longer work?" Candidate: "That's a challenging scenario! I'd consider:  1.⁠ ⁠Distributed Computing: Use distributed computing frameworks like Spark on Kubernetes.  2.⁠ ⁠Data Sampling: Use data sampling to reduce dataset size.  3.⁠ ⁠Approximate Query Processing: Use approximate query processing techniques.  4.⁠ ⁠Revisit Data Model: Revisit the data model and consider optimizations at the data ingestion layer. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍

Data Engineering Interview coming up? This may help you 🚀 Tech Round 1 • DSA (Arrays, Strings): 1- 2 questions (easy to medium level) • SQL: Answered 3-5 SQL questions, working with complex queries. • Spark Fundamentals: Discussed core concepts of Apache Spark, including its role in big data processing. 🚀 Tech Round 2 • DSA (Arrays, Stack): Worked on problems related to arrays and stack, demonstrating my algorithmic thinking and problem-solving skills. • SQL: Tackled advanced SQL queries, focusing on query optimization and data manipulation techniques. • Spark Internals: Delved into Spark's internal workings and how it scales for large datasets. 🚀 Hiring Manager Round • Data Modeling: Designed a data model for Uber and discussed approaches to managing real-world scenarios. • Team Dynamics & Project Management: Engaged in scenario-based questions, showcasing my understanding of team collaboration and project management. • Previous Project Experiences: Highlighted my contributions, challenges faced, and the impact of my work in past projects. 🚀 HR Round • Work Culture: Discussed salary, benefits, and growth opportunities, work culture, and company values. Here, you can find Data Engineering Resources 👇 https://topmate.io/analyst/910180 All the best 👍👍