Overview 2 Flashcards
Tell me about yourself (VERY common - you should have a 2-3 sentence answer ready for this. (Where did you graduate? What did you study? What work have you done till now?))
I graduated with a degree in Computer Science from [University Name]. Over the past few years, I’ve worked on projects involving data analysis, machine learning, and big data processing. I have hands-on experience with technologies like Spark, Kafka, and Hadoop, and I’m excited to continue building my skills in this field.
How do you handle team conflict?
I believe in open communication and understanding all perspectives. I listen carefully to the concerns of team members, seek common ground, and facilitate a solution that works for everyone. It’s important to remain calm and professional, even when disagreements arise.
Do you see yourself as a leader of a team? (there’s no wrong answer to this besides something like ‘yes I’m better than everyone’ or ‘no I hate people’)
I believe I can step up as a leader when needed. I value collaboration and support my teammates in achieving goals. I see leadership as guiding and motivating others, rather than being in control.
Tell me something interesting about your schooling.
During my schooling, I worked on a research project where we analyzed large datasets using Spark. It was interesting because we learned how to optimize data workflows, which helped me understand the importance of performance tuning in distributed systems.
What conflicts/challenges happened in any of your 3 projects?
In one of my projects, we faced challenges related to data inconsistencies and integration. To overcome this, we developed a robust data validation pipeline to ensure accuracy across the system.
Are you willing to learn different programming languages?
Absolutely! I’m always eager to expand my skill set. I believe that learning new languages can open up different perspectives on solving problems.
Describe what part of your most recent project you worked on and how you went about designing it.
In my most recent project, I worked on designing a scalable data pipeline using Apache Kafka and Spark. I focused on optimizing data ingestion and processing by using partitioning strategies in Kafka and Spark’s built-in transformations.
Describe a moment where you failed?
In a previous project, I underestimated the complexity of data transformation. This caused delays, but I learned the importance of thorough planning, especially when working with large datasets.
What makes you excited or passionate about Big Data?
I’m passionate about Big Data because it has the power to provide insights that drive decisions. The ability to work with vast datasets and derive meaningful results is what excites me most.
How to count the number of times a word appears in a text file? (was required to write code to solve this)
You can count the number of times a word appears in a text file by reading the file and using a word count algorithm, such as: with open('file.txt', 'r') as file: text = file.read().split() count = text.count('word').
What is an RDD?
An RDD (Resilient Distributed Dataset) is a fundamental data structure in Apache Spark. It is an immutable distributed collection of objects that can be processed in parallel.
How to create a dataframe in spark?
You can create a DataFrame in Spark using the following code: df = spark.read.csv('file.csv', header=True, inferSchema=True).
How to create an RDD?
You can create an RDD by parallelizing a collection: rdd = sc.parallelize([1, 2, 3, 4]).
How to create the schema of a dataframe in spark?
You can define a schema using StructType and StructField: schema = StructType([StructField('name', StringType(), True), StructField('age', IntegerType(), True)]) df = spark.read.schema(schema).csv('file.csv').
What are the different types of joins in Spark?
The different types of joins in Spark are: inner join, outer join, left outer join, right outer join, and cross join.
How to do a join?
To perform a join, you can use the .join() function: df1.join(df2, df1.id == df2.id).
How did you use Kafka in your producer program?
In my producer program, I used Kafka’s KafkaProducer to publish messages to a topic. I configured it with the Kafka broker details and serialized messages to be sent.
How did you use Kafka in your consumer program?
In the consumer program, I used Kafka’s KafkaConsumer to subscribe to a topic and consume messages in real-time. I also set up appropriate deserialization for message formats.
If your Kafka consumer stops running, what would you do?
If the Kafka consumer stops, I would first check for issues like connection problems, message deserialization errors, or resource constraints. I’d review logs and restart the consumer as needed.
SQL Query For Top Sales Employee’s by Department
SELECT department, employee_name, SUM(sales) AS total_sales FROM sales_table GROUP BY department, employee_name ORDER BY total_sales DESC LIMIT 5.
How did you use Hive in your projects?
I used Hive to store and query large datasets in a distributed environment. It allowed me to perform SQL-like queries on data stored in HDFS, which was very useful for data analysis.
How did you use HDFS?
In my projects, I used HDFS to store large volumes of data, which allowed for scalable and distributed processing. I interacted with it using Spark and Hadoop tools for data reading and writing.
What do we use spark-submit for?
We use spark-submit to submit Spark applications to a cluster for execution. It handles resource allocation and job execution across multiple nodes.
How to read a CSV file in Spark.
You can read a CSV file in Spark with spark.read.csv('file.csv', header=True, inferSchema=True).
How do we use Kafka in our projects?
In my projects, we use Kafka as a messaging system for real-time data streaming. It helps with ingesting data into the system and enables communication between different services.
Describe the most recent project.
In my most recent project, we built a real-time data pipeline using Kafka, Spark, and HDFS. We ingested data from various sources, processed it with Spark, and stored the results in HDFS for further analysis.
