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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 Az...
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.
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...
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.
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.
RD...
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, str...
I applied via Naukri.com and was interviewed in May 2022. There were 2 interview rounds.
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...
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...
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...
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...
Top trending discussions
I applied via Campus Placement and was interviewed before Jul 2021. There were 3 interview rounds.
In this round we have aptitude plus coding mcq questions
Here we have to write full fledge code 2 questions were there and are easy
I applied via Campus Placement and was interviewed before May 2020. There was 1 interview round.
Python has for and while loops. For loop iterates over a sequence and while loop executes a block of code while a condition is true.
For loop syntax: for variable in sequence: block of code
While loop syntax: while condition: block of code
Example: for i in range(5): print(i) will print 0 to 4
Example: while x < 5: print(x); x += 1 will print 0 to 4
SQL writing is crucial for data engineers to manage, manipulate, and retrieve data efficiently from databases.
Data Retrieval: SQL allows for efficient querying of large datasets. Example: SELECT * FROM users WHERE age > 30;
Data Manipulation: SQL enables data insertion, updates, and deletions. Example: UPDATE users SET status = 'active' WHERE last_login > '2023-01-01';
Data Aggregation: SQL can summarize data using...
I applied via Campus Placement and was interviewed before Jan 2021. There were 4 interview rounds.
I have worked on various technologies including Hadoop, Spark, SQL, Python, and AWS.
Experience with Hadoop and Spark for big data processing
Proficient in SQL for data querying and manipulation
Skilled in Python for data analysis and scripting
Familiarity with AWS services such as S3, EC2, and EMR
Knowledge of data warehousing and ETL processes
I applied via Walk-in and was interviewed before Feb 2020. There was 1 interview round.
I applied via Recruitment Consultant and was interviewed before Nov 2020. There were 3 interview rounds.
I appeared for an interview in Nov 2020.
Design a scalable and efficient platform for booking accommodations, flights, and experiences.
User Interface: Create a user-friendly interface for searching and booking accommodations.
Database Design: Use a relational database for storing user data, bookings, and property details.
Search Functionality: Implement a robust search algorithm to filter results based on user preferences.
Scalability: Use microservices architec...
I applied via Job Portal and was interviewed before Jul 2021. There were 2 interview rounds.
Coding test in hacker rank, easy
Some of the top questions asked at the Diggibyte Technologies Azure Data Engineer interview -
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