10+ Hindustan Media Ventures Interview Questions and Answers

RDD, Dataframe and Dataset are data structures in Spark. RDD is a low-level structure, Dataframe is tabular and Dataset is a combination of both.

• RDD stands for Resilient Distributed Datasets and is a low-level structure in Spark that is immutable and fault-tolerant.

• Dataframe is a tabular structure with named columns and is similar to a table in a relational database.

• Dataset is a combination of RDD and Dataframe and provides type-safety and object-oriented programming features...read more

ADF key components include pipelines, activities, datasets, triggers, and linked services.

• Pipelines - logical grouping of activities

• Activities - individual tasks within a pipeline

• Datasets - data sources and destinations

• Triggers - event-based or time-based execution of pipelines

• Linked Services - connections to external data sources

• Examples: Copy Data activity, Lookup activity, Blob Storage dataset

Yes, encryption keys can be created in Databricks. Cluster size can be adjusted based on workload.

• Encryption keys can be created using Azure Key Vault or Databricks secrets

• Cluster size can be adjusted manually or using autoscaling based on workload

• Encryption at rest can also be enabled for data stored in Databricks

Steps involved in fetching data from an on-premises Unix server

• Establish a secure connection to the Unix server using SSH or other protocols

• Identify the data source on the Unix server and determine the data extraction method

• Use tools like SCP, SFTP, or rsync to transfer the data from the Unix server to Azure storage

• Transform the data as needed before loading it into Azure Data Lake or Azure SQL Database

ADLS gen 2 is an upgrade to gen 1 with improved performance, scalability, and security features.

• ADLS gen 2 is built on top of Azure Blob Storage, while gen 1 is a standalone service.

• ADLS gen 2 supports hierarchical namespace, which allows for better organization and management of data.

• ADLS gen 2 has better performance for large-scale analytics workloads, with faster read and write speeds.

• ADLS gen 2 has improved security features, including encryption at rest and in transit.

• AD...read more

As an Azure Data Engineer, my current responsibilities include designing and implementing data solutions on Azure, optimizing data storage and processing, and ensuring data security and compliance.

• Designing and implementing data solutions on Azure

• Optimizing data storage and processing for performance and cost efficiency

• Ensuring data security and compliance with regulations

• Collaborating with data scientists and analysts to support their data needs

Semantic layer is a virtual layer that provides a simplified view of complex data.

• It acts as a bridge between the physical data and the end-user.

• It provides a common business language for users to access data.

• It simplifies data access by hiding the complexity of the underlying data sources.

• Examples include OLAP cubes, data marts, and virtual tables.

Partitioning in Azure Data Engineer involves dividing data into smaller chunks for better performance and manageability.

• Partitioning can be done based on a specific column or key in the dataset

• It helps in distributing data across multiple nodes for parallel processing

• Partitioning can improve query performance by reducing the amount of data that needs to be scanned

• In Azure Synapse Analytics, you can use ROUND_ROBIN or HASH distribution for partitioning

Medallion Architecture is a data processing architecture that involves breaking down data into smaller pieces for easier processing.

• Medallion Architecture involves breaking down data into smaller pieces for easier processing

• It allows for parallel processing of data to improve performance

• Commonly used in big data processing systems like Hadoop and Spark

Spark Architecture is a distributed computing framework that provides an efficient way to process large datasets.

• Spark Architecture consists of a driver program, cluster manager, and worker nodes.

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark supports various programming languages like Scala, Java, Python, and SQL.

• It includes components like Spark Core, Spark SQL, Spark Streaming, and MLlib for different data processing task...read more

Types of triggers in Azure Data Factory include schedule, tumbling window, event, and manual triggers.

• Schedule trigger: Runs pipelines on a specified schedule.

• Tumbling window trigger: Runs pipelines at regular intervals based on a time window.

• Event trigger: Triggers pipelines based on events like file arrival or HTTP request.

• Manual trigger: Allows manual execution of pipelines.