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Onward Technologies Inc
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I was interviewed in Aug 2024.
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posted on 6 Jan 2025
Aptitude questions on time and distance
Java coding question
I applied via Naukri.com and was interviewed in Aug 2024. There was 1 interview round.
Consumers read data from topics, while producers write data to topics in Kafka.
Consumers subscribe to topics to read messages from them
Producers publish messages to topics for consumers to read
Consumers can be part of a consumer group to scale out consumption
Producers can specify key for messages to control partitioning
I applied via Recruitment Consulltant and was interviewed in Jan 2024. There were 2 interview rounds.
The API allows users to retrieve weather data based on location.
Create a new endpoint that accepts a location parameter
Use a weather API service to fetch weather data based on the location
Return the weather data in a standardized format, such as JSON
The feature added to the project is a user authentication system.
Implement a login page with username and password fields.
Create a database table to store user credentials.
Hash and salt passwords for secure storage.
Implement password reset functionality.
Add user roles and permissions for access control.
posted on 22 Apr 2024
I applied via LinkedIn and was interviewed in Mar 2023. There were 4 interview rounds.
Optimisation techniques in Pyspark and sample code for SCD type 2
Use broadcast variables to reduce data shuffling
Partition data based on key columns to improve performance
Use cache() or persist() to avoid recomputing data
Use coalesce() or repartition() to reduce the number of partitions
For SCD type 2, use merge() function to update or insert new records
Example code for SCD type 2: https://github.com/awantik/pyspark-101
I have worked with huge data sets in the past.
I have experience working with data sets ranging from a few gigabytes to several terabytes.
One of the toughest problems I faced was optimizing a query that was taking too long to execute.
I resolved it by analyzing the query plan and identifying the bottleneck, then optimizing the query accordingly.
Another challenge I faced was dealing with data inconsistencies, which I reso...
Our project architecture follows a microservices approach with containerization using Docker and orchestration with Kubernetes.
We have divided our application into smaller, independent services that communicate with each other through APIs.
Each service is containerized using Docker, allowing for easy deployment and scaling.
We use Kubernetes for orchestration, which automates the deployment, scaling, and management of o...
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