Azure Data Engineering Interview

Questions and Answers

1. State the different integration runtimes available in Azure Data Factory

Azure Data Factory offers the following integration runtimes,

Azure Integration Runtime
Self-hosted Integration Runtime
Azure SSIS Integration Runtime

2. How to monitor and troubleshoot pipelines in Azure Data Factory?

Monitor pipelines in Azure Data Factory using the Monitoring & Management App. It provides detailed views of the pipeline with its activity and triggers. Check the activity run logs and diagnostic settings for troubleshooting.

3. Mention the difference between Azure Data Lake Storage Gen1 and Gen2?

Azure data lake storage Gen1 is designed for big data analytics workloads whereas Gen2 is built on top of Azure Blob Storage. Gen1 organizes files in a directory-like structure but Gen2 makes use of Blob Storage foundation. The performance and security features are advanced in Gen2 when compared with Gen1. In Gen2, the integration with other Azure services is advanced from Gen1.

4. Explain about Data Flow in Azure Data Factory.

Data Flow in Azure Data Factory allows data engineers to handle data transformation pipelines effectively. It is used to perform data transformations using visual interfaces.

5. What is Azure Synapse Analytics?

Azure Synapse Analytics is an analytics service that combines big data and data warehousing. It is used for querying and analyzing large volumes of data.

6. How do you handle schema evolution in Azure Data Lake?

In Azure Data lake, schema evolution is handled using tools and practices like:

Schema validation
Versioning
Data contracts

Use Azure Schema Registry service to manage changes in data structure.

7. What is the process involved in setting up a pipeline in Azure Data Factory?

The steps involved in setting up a pipeline in Azure Data Factory are:

Define the activities like copy data, data flow, and stored procedure
Create a workflow by linking the activities in a sequence
Schedule and trigger the pipeline

8. What are Linked Services in Azure Data Factory?

Linked Services are responsible for secure connections to external resources. It stores the necessary connection details to interact with the external data stores. Linked Services contains connection information, such as:

Server name
Database name
Authentication details
Configuration settings

9. What is a dataset in Azure Data Factory?

Dataset represents the data structure in Azure Data Factory, such as tables, files, or folders.
To operate the activities in a pipeline, the dataset defines the schema and location of the data.

10. What are the uses of the Copy Activity in Azure Data Factory ?

Copy Activity is used to copy data from a source to a destination. It also handles data transfer and supports various data stores.

11. How to optimize performance in Azure Synapse Analytics?

In Azure Synapse Analytics, Performance optimization can be achieved through techniques like:

Distributed tables
Partitioning large tables
Statistics
Optimizing query plans
Materialized views

12. What strategies are available to ensure high availability and disaster recovery for an Azure data solution?

Strategies to achieve high availability and disaster recovery are:

To use geo-redundant storage
Implementation of failover mechanisms
Automating backups using Azure Site Recovery
Regular testing of disaster recovery plans

13. What are the best practices for designing Azure Data Factory pipelines?

Best practices involved in designing Azure data factory pipelines are:

Modularizing pipelines
Parameterizing linked services and datasets
Using retries and error handling
Optimizing data movement
Monitoring performance metrics

14. How to achieve data security in Azure Data Lake Storage?

Data security can be achieved in Azure Data Lake Storage using:

Access control lists,
Role-based access control,
Data encryption,
Integrating with Azure Active Directory for authentication.

15. Specify the importance of partitioning in data processing?

Partitioning is important in data processing as it improves query performance and manageability. It divides large datasets into smaller, more manageable pieces. This enables parallel processing and reduces query latency.

16. What are the common data transformation techniques used in ETL processes?

The common data transformation techniques involved in ETL processes are:

Filtering
Sorting
Joining
Aggregating
Cleaning

These data transformations shapes the data into the desired format for analysis.

17. What is a Data Flow Debug in Azure Data Factory?

Data Flow Debug is a feature that allows us to interactively debug the data flows. It inspects the data transformation steps in real-time and identifies issues before running them in production.

18. Explain the role of Azure Databricks in a data engineering pipeline.

Azure Databricks provides a collaborative workspace for data engineers and data scientists. It seamlessly integrates with Azure services and enables data processing and advanced analytics.

19. State the importance of data lineage in data engineering

Data lineage provides visibility into the data flow from source to destination. It is crucial for:

Data governance
Compliance
Debugging
Data dependencies

20. How do you implement incremental data load in Azure Data Factory?

Incremental data load is implemented using techniques like:

Watermarking
Change data capture
System variables like lastModified

21. Describe a scenario where Azure Stream Analytics is applicable.

Azure Stream Analytics is used in various scenarios that require real-time data processing.

Examples:

Monitoring IoT device data
Analyzing social media feeds
Detecting fraud in financial transactions

22. How would you design a data lake architecture on Azure?

Data lake architecture on Azure include:

Data ingestion layers using Azure Data Factory
Storage layers by Azure Data Lake Storage
Processing layers with the help of Azure Databricks
Analytics layers using Azure Synapse Analytics

23. Explain a use case for using Azure Cosmos DB in a data engineering project.

Azure Cosmos DB is suitable for handling highly available, and low-latency data access.

Examples:

E-commerce applications
Gaming leaderboards
IoT device data storage

24. How would you implement data versioning in a data lake?

Data versioning is implemented using folder structures with version numbers. It maintains metadata about versions, and uses Delta Lake to provide version control.

25. State the benefits of Linked Services in Azure Data Factory

The key benefits of linked services in Azure Data Factory are:

Centralized management that simplifies management and maintenance of connection information.
Achieves scalability by connecting to a wide range of data stores and services.
Secure storage of sensitive information using parameterization and integration with Azure Key Vault.
Managing connection settings and securing connection information by separating from the transformation logic

26.What are Linked Services in Azure Data Factory?

To connect Data Factory with external resources, Linked Services define the connection information to external resources, like databases, storage accounts, and compute resources.

27. Explain the difference between Azure Data Lake and Azure Blob Storage.

Azure Data Lake is optimized for big data analytics with hierarchical namespace and support. It can be used for structured, semi-structured, and unstructured data. Azure Blob Storage is designed for unstructured data storage with flat namespaces.

28. What are Dataflows in Azure Data Factory?

Dataflows are a graphical tool in ADF to build ETL pipelines. It allows data transformation and movement without writing code.

29. What is the use of the Integration Runtime in Azure Data Factory?

Integration Runtime (IR) is the compute infrastructure used by ADF. It is used for data movement, data transformation, and the execution of SSIS packages.

30. What is Azure Synapse Analytics?

Azure Synapse is an evolution of Azure SQL Data Warehouse which is useful for querying data. It integrates big data and data warehousing and offers both on-demand and provisioned resources.

31.What are triggers in Azure Data Factory?

Triggers are used to schedule pipeline executions and the common types are:

Scheduled triggers,
Tumbling window triggers,
Event-based triggers.

32. Explain the purpose of Azure Databricks.

Azure Databricks is an Apache Spark-based analytics platform and it is optimized for Azure. It provides an interactive workspace for collaboration and running Spark-based workloads.

33. How would you secure data in an Azure Data Lake?

Data engineers can be secured using:

Azure Active Directory (AAD) for authentication,
Role-based access control (RBAC),
Encryption at rest and in transit.

34. What is PolyBase in Azure SQL Data Warehouse?

PolyBase enables querying external data stored in Hadoop or Azure Blob Storage from within Azure SQL Data Warehouse. It allows for seamless integration with big data systems.

35. What is a pipeline in Azure Data Factory?

A pipeline is a collection of activities in Azure Data Factory. It performs a unit of work, such as moving or transforming data.

36. How can you handle failures in Azure Data Factory pipelines?

Failures can be handled using:

Retry policies,
Fault tolerance mechanisms,
Custom alerting systems using Azure Monitor

37. State the uses of Delta Lake?

Delta Lake is used to perform ACID transactions, scalable metadata handling, and unifies streaming and batch processing on top of existing data lakes.

38. What are the different types of storage accounts in Azure?

General-purpose v2,
General-purpose v1,
Blob storage accounts,
File storage accounts.

39. What is an Azure Data Lake Storage Gen2?

To optimize for big data analytics, Azure Data Lake Storage Gen2 combines the scalability and cost-effectiveness of object storage with the hierarchical file system of data lakes.

40. Explain the concept of partitioning in Azure Synapse Analytics.

Partitioning in Azure Synapse Analytics is a technique to divide a table into smaller pieces. It improves query performance by reducing the amount of data that needs to be scanned.

41. What is an Activity in Azure Data Factory?
An activity represents a unit of work in Azure Data Factory. It could be data movement, data transformation, and custom activities using Azure Functions.

42. How do you monitor Azure Data Factory pipelines?

ADF pipelines can be monitored using:

Built-in monitoring tool through Azure Monitor,
Setting up alerts for pipeline success/failure metrics.

43. What is the difference between Managed and External tables in Synapse?

Managed tables store data within the Synapse environment, while external tables reference data stored outside of Synapse.

44. Explain the use of Azure Stream Analytics.

Azure Stream Analytics is a real-time analytics service designed to process and analyze streaming data from various sources like IoT devices, applications, and logs.

45. What is the purpose of the Copy Activity in Azure Data Factory?

The Copy Activity in ADF is used to copy data from source data stores to destination data stores. It supports a variety of formats and storage types, including cloud-based and on-premises sources

46. What is a Dataflow in Azure Databricks?

A dataflow in Azure Databricks refers to the process of loading, transforming, and storing data in a Spark-based data pipeline for big data processing.

47. What are the key components of Azure Synapse Analytics?

The Key components of Azure Synapse Analytics:

Dedicated SQL pools,
On-demand SQL pools,
Apache Spark pools,
Integration with Azure Data Lake for unified analytics.

48. What is the role of Azure Key Vault in securing data?

Azure Key Vault securely stores encryption keys, secrets, and certificates. It can be used to protect sensitive information in Azure services like Azure Data Lake and Azure SQL.

49. What are the different pricing tiers available in Azure Data Lake Storage?

Pricing tiers:

Hot (frequent access),
Cool (infrequent access),
Archive (long-term storage).

50. How would you optimize a pipeline in Azure Data Factory for large data volumes? Techniques of Optimization:

Parallel processing,
Using partitioned data sets,
Leveraging staging areas,
Tuning the integration runtime for performance.

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Azure Data Engineering Interview

Questions and Answers