Diggibyte Technologies Azure Data Engineer Interview Questions and Answers

Spark architecture is a distributed computing framework that processes large datasets in parallel across a cluster of nodes.

• Spark has a master-slave architecture with a driver program that communicates with the cluster manager to allocate resources and tasks to worker nodes.

• Worker nodes execute tasks in parallel and store data in memory or disk.

• Spark supports various data sources and APIs for batch processing, streamin...read more

DAG stands for Directed Acyclic Graph and is a way to represent dependencies between tasks. RDD stands for Resilient Distributed Datasets and is a fundamental data structure in Apache Spark.

• DAG is used to represent a series of tasks or operations where each task depends on the output of the previous task.

• RDD is a distributed collection of data that can be processed in parallel across multiple nodes in a cluster.

• RDDs ar...read more

Serialization is the process of converting an object into a stream of bytes for storage or transmission.

• Serialization is used to transfer objects between different applications or systems.

• It allows objects to be stored in a file or database.

• Serialization can be used for caching and improving performance.

• Examples of serialization formats include JSON, XML, and binary formats like Protocol Buffers and Apache Avro.

Accumulators are variables used for aggregating data in Spark. GroupByKey and ReduceByKey are operations used for data transformation.

• Accumulators are used to accumulate values across multiple tasks in a distributed environment.

• GroupByKey is used to group data based on a key and create a pair of key-value pairs.

• ReduceByKey is used to aggregate data based on a key and reduce the data to a single value.

• GroupByKey is less...read more

Choose a cluster based on data size, complexity, and processing requirements.

• Consider the size and complexity of the data to be processed.

• Determine the processing requirements, such as batch or real-time processing.

• Choose a cluster with appropriate resources, such as CPU, memory, and storage.

• Examples of Azure clusters include HDInsight, Databricks, and Synapse Analytics.

To create mount points in ADLS, use the Azure Storage Explorer or Azure Portal. To load data source, use Azure Data Factory or Azure Databricks.

• Mount points can be created using Azure Storage Explorer or Azure Portal

• To load data source, use Azure Data Factory or Azure Databricks

• Mount points allow you to access data in ADLS as if it were a local file system

• Data can be loaded into ADLS using various tools such as Azure D...read more