IQZ SYSTEMS Data Engineer Interview Questions and Answers

Q1. Sql constrainsts, star schema, dml dcl commands
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Q2. About cureent project and responsibilities
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Q1. Current projects and resposibilities
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Q2. Where vs having, reason for job change
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Q1. How do you optimize SQL queries?
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Optimizing SQL queries involves using indexes, avoiding unnecessary joins, and optimizing the query structure.
• Use indexes on columns frequently used in WHERE clauses

• Avoid using SELECT * and only retrieve necessary columns

• Optimize joins by using INNER JOIN instead of OUTER JOIN when possible

• Use EXPLAIN to analyze query performance and make necessary adjustments
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Q2. How do you do performance optimization in Spark. Tell how you did it in you project.
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Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.
• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my projec...read more
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Q3. What is SparkContext and SparkSession?
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SparkContext is the main entry point for Spark functionality, while SparkSession is the entry point for Spark SQL.
• SparkContext is the entry point for low-level API functionality in Spark.

• SparkSession is the entry point for Spark SQL functionality.

• SparkContext is used to create RDDs (Resilient Distributed Datasets) in Spark.

• SparkSession provides a unified entry point for reading data from various sources and performing
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Q4. When a spark job is submitted, what happens at backend. Explain the flow.
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When a spark job is submitted, various steps are executed at the backend to process the job.
• The job is submitted to the Spark driver program.

• The driver program communicates with the cluster manager to request resources.

• The cluster manager allocates resources (CPU, memory) to the job.

• The driver program creates DAG (Directed Acyclic Graph) of the job stages and tasks.

• Tasks are then scheduled and executed on worker nodes ...read more
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Q5. Calculate second highest salary using SQL as well as pyspark.
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Calculate second highest salary using SQL and pyspark
• Use SQL query with ORDER BY and LIMIT to get the second highest salary

• In pyspark, use orderBy() and take() functions to achieve the same result
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Q6. 2 types of modes for Spark architecture ?
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The two types of modes for Spark architecture are standalone mode and cluster mode.
• Standalone mode: Spark runs on a single machine with a single JVM and is suitable for development and testing.

• Cluster mode: Spark runs on a cluster of machines managed by a cluster manager like YARN or Mesos for production workloads.
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Q7. If you want very less latency - which is better standalone or client mode?
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Client mode is better for very less latency due to direct communication with the cluster.
• Client mode allows direct communication with the cluster, reducing latency.

• Standalone mode requires an additional layer of communication, increasing latency.

• Client mode is preferred for real-time applications where low latency is crucial.
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Q1. Scenario based. Write SQL and pyspark code for a dataset.
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Q2. If you have to find latest record based on latest timestamp in a table for a particular customer(table is having history) , how will you do it. Self join and nested query will be expensive. Optimized query...read more
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Q1. What are different type of joins available in Databricks?
Ans. 
Different types of joins available in Databricks include inner join, outer join, left join, right join, and cross join.
• Inner join: Returns only the rows that have matching values in both tables.

• Outer join: Returns all rows when there is a match in either table.

• 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 ...read more
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Q2. How do you make your data pipeline fault tolerant?
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Implementing fault tolerance in a data pipeline involves redundancy, monitoring, and error handling.
• Use redundant components to ensure continuous data flow

• Implement monitoring tools to detect failures and bottlenecks

• Set up automated alerts for immediate response to issues

• Design error handling mechanisms to gracefully handle failures

• Use checkpoints and retries to ensure data integrity
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Q3. What is AutoLoader?
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AutoLoader is a feature in data engineering that automatically loads data from various sources into a data warehouse or database.
• Automates the process of loading data from different sources

• Reduces manual effort and human error

• Can be scheduled to run at specific intervals

• Examples: Apache Nifi, AWS Glue
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Q1. How do you connect to different services in Azure?
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To connect to different services in Azure, you can use Azure SDKs, REST APIs, Azure Portal, Azure CLI, and Azure PowerShell.
• Use Azure SDKs for programming languages like Python, Java, C#, etc.

• Utilize REST APIs to interact with Azure services programmatically.

• Access and manage services through the Azure Portal.

• Leverage Azure CLI for command-line interface interactions.

• Automate tasks using Azure PowerShell scripts.
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Q2. What are linked Services?
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Linked Services are connections to external data sources or destinations in Azure Data Factory.
• Linked Services define the connection information needed to connect to external data sources or destinations.

• They can be used in Data Factory pipelines to read from or write to external systems.

• Examples of Linked Services include Azure Blob Storage, Azure SQL Database, and Amazon S3.
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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.
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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
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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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Q1. Explain adf questions in detail
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ADF questions refer to Azure Data Factory questions which are related to data integration and data transformation processes.
• ADF questions are related to Azure Data Factory, a cloud-based data integration service.

• These questions may involve data pipelines, data flows, activities, triggers, and data movement.

• Candidates may be asked about their experience with designing, monitoring, and managing data pipelines in ADF.

• Exam...read more
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Q2. Project related questions
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Q1. Project data related questions
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Q2. Databricks and SQL interview questions
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Q1. What are the optimization techniques used in Apache Spark?
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Q2. 2 SQL queries , 1 PySpark code and 1 Python Code .
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Q3. 2-3 Scenario Based questions from ADF and databricks .
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Oops dsa sql network

Q1. Dsa based python question
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Q2. Dsa based python question on tress
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Q1. Sql question queries
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Q2. Sql question query
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