Data pipelines are a series of steps that data flows from various resources into a centralized data warehouse or data lake. Effective data pipelines are crucial for implementing big data solutions and delivering actionable insights to business users. Microsoft Azure provides several data engineering services for creating data pipelines.
Azure Data Factory
Azure Data Factory is a cloud-based service allowing data engineers to create and manage ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines at scale. The pipelines can extract data from a variety of sources, transform the data using compute services like Azure HDInsight and Databricks, and then load them into the data store of choice.
Creating a data pipeline in Azure Data Factory involves several stages:
- Data Ingestion: Data is collected from various sources like databases, online services, or local files.
- Data Transformation: The ingested data is processed and transformed into a usable format. Azure Data Factory supports using compute services such as Azure HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Azure Machine Learning.
- Data Loading: The transformed data is then loaded into a data store or a business intelligence tool for analysis.
Azure Data Factory uses a graphical interface with drag-and-drop capabilities, making it easier for users without any coding experience to create data pipelines.
# Code example creating simple pipeline with ADLS and Databricks
# Step 1: Define your ADLS and Databricks linked service
adls_ls = AzureDataLakeStoreLinkedService(account_name='myadls', tenant='mytennant',
subscription_id='mysubscription', resource_group_name='myresourcegroup')
databricks_ls = AzureDatabricksLinkedService(existing_cluster_id='myclusterid')
# Step 2: Define your input and output dataset
input_ds = AzureDataLakeStoreDataset(adls_ls, folder_path='input-folder')
output_ds = AzureDataLakeStoreDataset(adls_ls, folder_path='output-folder')
# Step 3: Define your DatabricksNotebookActivity
notebook_activity = DatabricksNotebookActivity(name='My Notebook Activity',
linked_service=databricks_ls,
notebook_path='/path/to/my/notebook',
notebook_params={'input': input_ds, 'output': output_ds})
# Step 4: Create your pipeline
pl = Pipeline(name='My Databricks Pipeline',
activities=[notebook_activity])
Azure Data Lake
Azure Data Lake Storage is a secure, scalable, and highly available data lake that allows businesses to store and analyze large amounts of data. It comes with Azure Data Explorer that enables exploration of large volumes of raw and pre-processed data, and run ad-hoc analytics.
Azure Synapse Analytics
Azure Synapse Analytics, also known as Azure SQL Data Warehouse, integrates on-demand or provisioned resources to ingest, prepare, manage, and serve data. It enables users to create extensive data pipelines that utilize both on-premise and cloud data sources.
Azure Databricks
Azure Databricks is an Apache Spark–based analytics service that enables fast data analytics and collaborative notebooks. With a fully managed, full spectrum, open-source analytics service, Azure Databricks provides efficient data pipelines, including monitoring, management, and troubleshooting capabilities. Microsoft Azure, including Azure Databricks, is ideal for the DP-203 Data Engineering on Microsoft Azure exam.
Comparison of Microsoft Azure Data Engineering Services
| Azure Service | Primary Use Case | Benefits |
| Azure Data Factory | Data Orchestration | Supports 90+ data source connectors, Provides visual tools, Allows monitoring and management |
| Azure Data Lake Storage | Big Data Analytics | Highly scalable, Builds on Azure Storage, Includes Azure Data Explorer tool |
| Azure Synapse Analytics | Data Warehousing | Combines on-demand and provisioned resources, Allows real-time analytics |
| Azure Databricks | Machine Learning | Simplified management and monitoring, Collaborative, Built on Apache Spark |
In conclusion, Azure offers robust services for data engineers to create, manage, and analyze data pipelines. Depending on the use case, Azure Data Factory, Azure Data Lake, Azure Synapse Analytics, or Azure Databricks conceivably may serve the purpose. Nevertheless, a combination of these services often provides the best solution for most data scenarios.
Practice Test
True or False? Azure Data Factory (ADF) is a serverless, fully managed, cloud-based integration service that ingests, prepares, transforms, and publishes data.
- True
- False
Answer: True
Explanation: Azure Data Factory is a serverless integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation.
What is the key component of an Azure Data Factory pipeline?
- a) Activities
- b) Triggers
- c) Parameters
- d) Variables
Answer: a) Activities
Explanation: Activities in ADF represent a processing step in a pipeline. They usually take zero or more datasets as input and produce one or more datasets as output.
True or False? ADF supports only structured data.
- True
- False
Answer: False
Explanation: Azure Data Factory supports a wide range of data types, including structured, semi-structured and unstructured data.
Which service is NOT used to process data in Azure Data Factory?
- a) Azure HDInsight
- b) Azure Databricks
- c) Azure SQL Database
- d) Azure DevOps
Answer: d) Azure DevOps
Explanation: Azure DevOps is a set of development tools for software teams whereas the other options are all data processing services used in Azure Data Factory data pipelines.
True or False? Data in a pipeline can only move in one direction.
- True
- False
Answer: True
Explanation: In a data pipeline, data flow occurs in a single direction from the source to the destination.
Which of the following cannot be used as a trigger for disabling a Data Factory pipeline?
- a) Event
- b) Schedule
- c) Threshold
- d) None of the above
Answer: d) None of the above
Explanation: All of these can be used as triggers to disable a Data Factory pipeline as per the requirements.
True or False? You can monitor Azure Data Factory pipelines using Azure Monitor.
- True
- False
Answer: True
Explanation: Azure Monitor does provide features that enable you to closely track and respond to critical activities and conditions in your Azure Data Factory pipelines.
What is one function of Azure Stream Analytics?
- a) Real-time data streaming
- b) Batch data processing
- c) Data storage
- d) Data visualization
Answer: a) Real-time data streaming
Explanation: Azure Stream Analytics is a real-time analytics and complex event-processing engine that is designed to analyze and visualize streaming data in real-time.
Which language is used to write transformations in Azure Data Factory?
- a) SQL
- b) Python
- c) Java
- d) C#
Answer: a) SQL
Explanation: Azure Data Factory uses a SQL-based language for data transformation called Mapping Data Flows.
True or False? Azure Data Factory supports hybrid data integration.
- True
- False
Answer: True
Explanation: Azure Data Factory supports hybrid data integration, which means it can handle data both on-premises and in the cloud.
What does the term ‘pipeline’ in Azure Data Factory refer to?
- a) A sequence of data processing steps
- b) A connector for data sources
- c) A method for storing data
- d) A tool for visualizing data
Answer: a) A sequence of data processing steps
Explanation: In Azure Data Factory, a pipeline represents a sequence of activities or data processing steps to be executed.
What is NOT a main mediator of data transfer within an Azure Data Factory pipeline?
- a) Linked services
- b) Datasets
- c) Dataflows
- d) Visual Studio
Answer: d) Visual Studio
Explanation: Visual Studio is not involved in data transfer within an Azure Data Factory pipeline. It is a development environment used for building applications, whereas linked services, datasets, and dataflows all specifically facilitate data transfer in Azure Data Factory pipelines.
True or False? Azure Data Factory allows you to schedule data movement and transformation jobs.
- True
- False
Answer: True
Explanation: Azure Data Factory indeed provides mechanisms to schedule and automate ETL (Extract, Transform, Load) tasks, which involve data movement and data transformation jobs.
Which one isn’t an activity type in Azure Data Factory?
- a) Data movement activities
- b) Data transformation activities
- c) Control activities
- d) Data storage activities
Answer: d) Data storage activities
Explanation: Azure Data Factory includes data movement, data transformation, and control activities, but does not include ‘data storage’ as an activity type.
True or False? In Azure Data Factory, a pipeline can contain multiple activities but an activity cannot belong to more than one pipeline.
- True
- False
Answer: True
Explanation: In Azure Data Factory, a pipeline indeed can have multiple activities, each performing a certain task. However, an activity is an integral part of its pipeline and does not belong to multiple pipelines.
Interview Questions
What is a data pipeline in Azure?
A data pipeline in Azure is a tool used for moving and transforming data from one place to another, such as from on-premises data warehouses to cloud-based storage, or from one database to another. Azure Data Factory is one commonly used service to create data pipelines in Azure.
What is Azure Data Factory?
Azure Data Factory (ADF) is a cloud-based data integration service that allows you to create data-driven workflows for orchestrating and automating data movement and data transformation.
How can you monitor data pipelines in Azure Data Factory?
Azure Data Factory provides built-in support for pipeline monitoring via Azure Monitor, API, PowerShell, Azure Monitor logs, and Azure Log Analytics.
What does the Copy Activity in Azure Data Factory do?
The Copy Activity in Azure Data Factory is a powerful activity that can be used to copy data from a source data store to a sink data store, while optionally transforming the data with different levels of complexity.
Which service in Azure can be used to visualize the data flow in the pipeline?
Azure Data Factory includes a visual interface for creating, configuring, and managing data pipelines. In Azure Data Factory, you can use the Data Flow designer to create a graphical representation of data transformations.
How can data pipelines be executed in Azure Data Factory?
There are two ways to execute data pipelines in Azure Data Factory. One is on-demand, where you directly run the pipeline, and the other is scheduled, where you set a trigger, like setting a time and frequency, to run the pipeline.
What is data masking in Azure Data Factory?
Data masking in Azure Data Factory is a feature that helps secure sensitive information by obfuscating it in the data flow. It provides various masking functions such as default value, null value, custom substitution, etc.
What is data partitioning in Azure Data Factory?
Data partitioning in Azure Data Factory allows you to divide your data into smaller, manageable parts. These parts can then be processed simultaneously, which can lead to performance improvements and increased efficiency.
What is Mapping Data Flow in Azure Data Factory?
Mapping Data Flow is a visually designed data transformation tool within Azure Data Factory. It allows you to design data transformations without writing any code, and it’s powered by Azure Databricks under the hood.
How can you handle schema drift in Azure Data Factory?
Schema drift handling is available as part of Mapping Data Flow. It allows for flexible schema handling by capturing additional columns during runtime without having to modify the data flow.
What is the use of the lookup activity in Azure Data Factory pipelines?
The Lookup activity in Azure Data Factory is used to retrieve a dataset from any of the Azure Data Factory supported data sources. It returns the first row from the dataset by default or can return multiple rows based on configuration.
What is the “integration runtime” in Azure Data Factory?
The integration runtime (IR) in Azure Data Factory is the compute infrastructure used by Azure Data Factory to provide data integration capabilities across different network environments.
Can you create data pipelines using Azure Databricks?
Yes, Azure Databricks can be used to create complex data transformations and then be integrated within Azure Data Factory for orchestration and automation as part of data pipelines.
What service should you use in Azure if you only want to move data from source to destination without any transformation?
Azure Data Factory is the appropriate service to use when you need to copy data from a source to a sink without performing any transformation.
Can Azure Data Factory support SaaS (Software as a Service) applications as a source in data pipeline activities?
Yes, Azure Data Factory supports various SaaS applications like Dynamics 365, Salesforce, and others as a source in data pipeline activities.
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