Matrix Ascentech Data Engineer Interview Questions and Answers for Freshers

Q1. Difference between bigtable and bigquery.
Ans. 
Bigtable is a NoSQL database for real-time analytics, while BigQuery is a fully managed data warehouse for running SQL queries.
• Bigtable is a NoSQL database designed for real-time analytics and high throughput, while BigQuery is a fully managed data warehouse for running SQL queries.

• Bigtable is used for storing large amounts of semi-structured data, while BigQuery is used for analyzing structured data using SQL queries.

• ...read more
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Q2. How to remove duplicate rows from bigquery? find the month of a given date in bigquery.
Ans. 
To remove duplicate rows from BigQuery, use the DISTINCT keyword. To find the month of a given date, use the EXTRACT function.
• To remove duplicate rows, use SELECT DISTINCT * FROM table_name;

• To find the month of a given date, use SELECT EXTRACT(MONTH FROM date_column) AS month_name FROM table_name;

• Make sure to replace 'table_name' and 'date_column' with the appropriate values in your query.
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Q3. What operator is used in composer to move data from gcs to bq
Ans. 
The operator used in Composer to move data from GCS to BigQuery is the GCS to BigQuery operator.
• The GCS to BigQuery operator is used in Apache Airflow, which is the underlying technology of Composer.

• This operator allows you to transfer data from Google Cloud Storage (GCS) to BigQuery.

• You can specify the source and destination parameters in the operator to define the data transfer process.
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Q4. Write a code for this - input = [1,2,3,4] output = [1,4,9,16]
Ans. 
Code to square each element in the input array.
• Iterate through the input array and square each element.

• Store the squared values in a new array to get the desired output.
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Q5. Dataflow vs dataproc.
Ans. 
Dataflow is a fully managed stream and batch processing service, while Dataproc is a managed Apache Spark and Hadoop service.
• Dataflow is a serverless data processing service that automatically scales to handle your data, while Dataproc is a managed Spark and Hadoop service that requires you to provision and manage clusters.

• Dataflow is designed for both batch and stream processing, allowing you to process data in real-t...read more
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Q6. Architecture of bq. Query optimization techniques in bigquery.
Ans. 
BigQuery architecture includes storage, execution, and optimization components for efficient query processing.
• BigQuery stores data in Capacitor storage system for fast access.

• Query execution is distributed across multiple nodes for parallel processing.

• Query optimization techniques include partitioning tables, clustering tables, and using query cache.

• Using partitioned tables can help eliminate scanning unnecessary data.

• ...read more
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Q7. RDD vs dataframe vs dataset in pyspark
Ans. 
RDD vs dataframe vs dataset in PySpark
• RDD (Resilient Distributed Dataset) is the basic abstraction in PySpark, representing a distributed collection of objects

• Dataframe is a distributed collection of data organized into named columns, similar to a table in a relational database

• Dataset is a distributed collection of data with the ability to use custom classes for type safety and user-defined functions

• Dataframes and Data...read more
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Aptitude test involved with quantative aptitude, logical reasoning and reading comprehensions.

Q1. Tell me your introduction.
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Q2. Tell me about your skills.
Ans. 
I have strong skills in data processing, ETL, data modeling, and programming languages like Python and SQL.
• Proficient in data processing and ETL techniques

• Strong knowledge of data modeling and database design

• Experience with programming languages like Python and SQL

• Familiarity with big data technologies such as Hadoop and Spark
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Q1. Are you ready relocate,?
Ans. 
Yes, I am open to relocating for the right opportunity.
• I am willing to relocate for the right job opportunity.

• I have experience moving for previous roles.

• I am flexible and adaptable to new locations.

• I am excited about the possibility of exploring a new city or country.
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Q2. Document verification
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Round 1 was online Test where one basic coding question was there and few aptitiude , verbal ability and python based question.

Q1. What is Data WareHouse..?
Ans. 
A Data Warehouse is a centralized repository that stores integrated data from multiple sources for analysis and reporting.
• Data Warehouses are designed for query and analysis rather than transaction processing.

• They often contain historical data and are used for decision-making purposes.

• Data Warehouses typically use a dimensional model with facts and dimensions.

• Examples of Data Warehouse tools include Amazon Redshift, Sn
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Q2. Nested Queries in Bigquery..?
Ans. 
Nested queries in BigQuery allow for querying data from within another query, enabling complex data analysis.
• Nested queries are queries that are embedded within another query

• They can be used to perform subqueries to filter, aggregate, or manipulate data

• Nested queries can be used in SELECT, FROM, WHERE, and HAVING clauses
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Q1. Difference between select and withcolumn in pyspark
Ans. 
select is used to select specific columns from a DataFrame, while withColumn is used to add or update columns in a DataFrame.
• select is used to select specific columns from a DataFrame

• withColumn is used to add or update columns in a DataFrame

• select does not modify the original DataFrame, while withColumn returns a new DataFrame with the added/updated column

• Example: df.select('col1', 'col2') - selects columns col1 and co...read more
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Q2. Difference between variables and parameters in ADF
Ans. 
Variables are used to store values that can be changed, while parameters are used to pass values into activities in ADF.
• Variables can be modified within a pipeline, while parameters are set at runtime and cannot be changed within the pipeline.

• Variables are defined within a pipeline, while parameters are defined at the pipeline level.

• Variables can be used to store intermediate values or results, while parameters are use...read more
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Q1. SQL - Given 2 tables with some nullls and asked to output the count of rows we get for all types of joins
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Q2. Types of joins and explain cross join
Ans. 
Types of joins in SQL include inner, outer, left, right, and cross join.
• Inner join: Returns rows when there is a match in both tables

• Outer join: Returns all rows when there is a match in one of the tables

• Left join: Returns all rows from the left table and the matched rows from the right table

• Right join: Returns all rows from the right table and the matched rows from the left table

• Cross join: Returns the Cartesian produ
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Q1. Projects and impact
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Q1. Spark architecture
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Q2. Spark Optimisation techniques
Ans. 
Spark optimization techniques aim to improve performance and efficiency of Spark jobs.
• Use partitioning to distribute data evenly

• Cache intermediate results to avoid recomputation

• Optimize shuffle operations by reducing data shuffling

• Use broadcast variables for small lookup tables

• Tune memory and executor settings for better performance
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Q1. Spark related questions
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Q2. Hadoop architecture and ecosystem questions
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Q1. About the data structures
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