Azure Synapse Pipelines is a component of Azure Synapse Analytics. It provides the ability to ingest, prepare, manage, and transform data by orchestrating and automating data movement and transformation.
Azure Synapse Pipelines offers various capabilities, including:
- Copying data from both on-premises and cloud-based data stores
- Running SQL scripts on relational databases to cleanse and transform data
- Executing stored processes in Big Data systems such as Apache Hadoop and Apache Spark
- Implementing machine learning and statistical models to analyze data
For instance, you could use Azure Synapse Pipelines to ingest data from a CSV file in a blob storage, transform the data using an Apache Spark activity, and then output the transformed data to a SQL Data Warehouse.
Here’s a simple pseudo-code representation of what a data pipeline might look like:
start_pipeline()
copyActivity = CopySource("CSV File")
SparkJobActivity = ApacheSparkJob()
copyActivity.set_destination(SparkJobActivity)
outputActivity = CopyDestination("SQL Data Warehouse")
SparkJobActivity.set_destination(outputData)
end_pipeline()
Azure Data Factory
Azure Data Factory (ADF) is a cloud-based data integration service designed to orchestrate and automate the movement and transformation of data. It works across on-premises and cloud data sources and integrates natively with several other Azure services.
Similar to Azure Synapse Pipelines, Azure Data Factory offers the following capabilities:
- Data movement between a broad array of sources and sinks
- Performing transformation activities and running analytical computations on the data
- Scheduling and orchestrating complex workflows (using Pipelines)
An example use-case of Azure Data Factory might be to automate the movement of data from an on-premises SQL Server to Azure Blob storage. This data could then be transformed using an Azure Machine Learning activity and output to Power BI for visualization.
Here’s a simple representation of a Azure Data Factory pipeline:
start_pipeline()
copyActivity = CopySource("SQL Server")
MLActivity = AzureMLBatchExecutionActivity()
copyActivity.set_destination(MLActivity)
outputActivity = CopyDestination("Power BI")
MLActivity.set_destination(outputActivity)
end_pipeline()
Comparison
Both Azure Synapse Pipelines and Azure Data Factory offer similar functions including data extraction, transformation and loading (ETL), and data preparation.
Here is a comparison of key features:
| Feature | Azure Synapse Pipelines | Azure Data Factory |
|---|---|---|
| Data Sources | Multiple data sources and sinks, including but not limited to Azure Blob Storage, Azure Data Lake, etc. | Wide range of data sources and sinks, including on-premises SQL Server, Oracle, MySQL, etc. |
| Transformation | Various transformation activities including data wrangling and executing SQL Server Stored Procedure | Supports map-reduce through HDInsight Hive activity and machine learning through Azure ML Batch Execution |
| Orchestration | Pipeline and activity run monitoring, alerts, and operational insights | Complex scheduling of pipelines, including chaining |
In essence, the choice between Azure Synapse Pipelines or Azure Data Factory will depend on the specific requirements of your data engineering tasks, such as the complexity of the data transformation and rules of the organization. Both services are powerful tools for ingest, transform and load tasks regardless of the data volume, size, and performance requirements.
Practice Test
True or False: Azure Synapse Pipelines can be used to ingest data from different types of data stores.
- True
- False
Answer: True
Explanation: Azure Synapse Pipelines, similar to Azure Data Factory, can ingest data from various types of data stores including relational and non-relational databases, cloud storage, and big data stores.
Multiple-select: Which of the following activities are supported by Azure Synapse Pipelines?
- A. Data movement activities
- B. Data transformation activities
- C. Event trigger activities
- D. Machine learning activities
Answer: A, B, C
Explanation: Azure Synapse Pipelines supports data movement activities, data transformation activities, and event trigger activities. However, it doesn’t directly support machine learning activities.
Single select: Which service is used to orchestrate and automate the movement and transformation of data in Azure?
- A. Azure Synapse Studio
- B. Azure Data Factory
- C. Azure Data Lake
- D. Azure SQL Database
Answer: B. Azure Data Factory
Explanation: Azure Data Factory is a cloud-based ETL and data integration service that allows you to create data-driven workflows for orchestrating data movement and transforming data at scale.
True or False: Azure Data Factory supports batch processing but not real-time processing.
- True
- False
Answer: False
Explanation: Azure Data Factory supports both batch processing and real-time processing.
Single select: Which of the following transformations does the Mapping data flow in Azure Data Factory support?
- A. Aggregation
- B. Ranking
- C. Windowing
- D. All of the above
Answer: D. All of the above
Explanation: Mapping data flow in Azure Data Factory supports a wide range of transformations including Aggregation, Ranking, and Windowing.
True or False: The Azure Synapse Pipelines and Azure Data Factory support the same functionalities for data ingestion and transformation.
- True
- False
Answer: True
Explanation: Both Azure Synapse Pipelines and Azure Data Factory provide essentially the same capabilities for data ingestion, data transformation, and data integration tasks.
Multiple-select: Which transformations can you perform on data using Azure Data Factory?
- A. Filtering
- B. Joining
- C. Ranking
- D. Converting data into images
Answer: A, B, C
Explanation: Azure Data Factory supports various transformations such as filtering, joining, and ranking. However, converting data into images is not a direct transformation supported by Azure Data Factory.
True or False: Azure Synapse Pipelines support using Python scripts for data transformation.
- True
- False
Answer: True
Explanation: Azure Synapse Pipelines do indeed support the use of Python scripts for transforming data.
Single select: Which service is specifically designed to handle large scale data warehousing and Big Data analytics?
- A. Azure Data Factory
- B. Azure Data Lake
- C. Azure Synapse Analytics
- D. Azure Machine Learning
Answer: C. Azure Synapse Analytics
Explanation: Azure Synapse Analytics, which integrates Azure Synapse Pipelines, is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics.
True or False: Azure Data Factory can be used to automate the transformation and ingestion of data into Azure Synapse Analytics.
- True
- False
Answer: True
Explanation: Azure Data Factory is indeed capable of automating the ingestion, preparation, management, and transformation of data, and its output can be used to feed into Azure Synapse Analytics.
Interview Questions
What is Azure Synapse Pipelines?
Azure Synapse Pipelines is a service that provides data integration capabilities in Azure Synapse Analytics. The pipelines can move and transform data from various sources and then load the processed data into data stores for analytical processing.
How does the Azure Data Factory fit into Azure Synapse Pipelines?
Azure Data Factory is part of Azure Synapse Pipelines and provides the core data integration capabilities to move and transform data.
What are some transformations you can perform in Azure Synapse Pipelines?
In Azure Synapse Pipelines, you can perform a range of transformations including filtering, joining, aggregating, sorting data, and various other transformations using mapping data flows.
What types of data can be ingested using Azure Synapse Pipelines?
Azure Synapse Pipelines can ingest a wide variety of data including structured, semi-structured, and unstructured data from different data sources.
How can Azure Synapse Pipelines ingest data in real-time?
Azure Synapse Pipelines can ingest data in real-time through event-based triggering and tumbling window triggering that can facilitate real-time data processing and ingestion.
What is the role of Linked Services in Azure Synapse pipelines or Azure Data Factory?
Linked Services in Azure Synapse pipelines or Azure Data Factory is similar to connection strings. It defines the information to connect the data stores or compute services.
What is the purpose of the Integration Runtime?
Integration Runtime (IR) provides the bridge between public networks and private networks. It is used to provide data movement capabilities and dispatching transform activities to external computes.
What is Mapping Data Flow in Azure Synapse pipeline?
Mapping Data Flow is a visually-designed, data transformation feature in Azure Synapse pipeline that allows you to transform data without writing code.
Can you schedule Azure Synapse Pipelines?
Yes, Azure Synapse Pipelines can be scheduled. It can also perform data movement activities on a specified schedule by using the schedule trigger.
How is data transformation achieved in Azure Data Factory?
Data transformation in Azure Data Factory is achieved using mapping data flows. These flows utilize a visual interface to transform data without the need for hand-written code.
What is Data Lake in Microsoft Azure and how is it involved in data ingestion?
Azure Data Lake is a highly scalable and secure data lake that allows you to run big data analytics and machine learning workloads. It enables Azure Synapse Pipelines to ingest and store data from a variety of data sources for analytical processing.
What types of sources can Azure Synapse Pipelines or Azure Data Factory ingest data from?
Azure Synapse Pipelines or Azure Data Factory can ingest data from various sources including Azure Blob storage, Azure Cosmos DB, Azure SQL Database, Azure Data Lake Storage, and many other sources.
How does the copy activity work in Azure Data Factory or Azure Synapse Pipelines?
The copy activity in Azure Data Factory or Azure Synapse Pipelines reads data from the source, converts the data based on the mappings you provide, and writes the data to the sink.
How do you monitor pipelines in Azure Synapse or Azure Data Factory?
Monitoring in Azure Synapse or Azure Data Factory can be done using the Azure Monitor and the Monitor & Manage App which provides unified monitoring capabilities.
What is a trigger in relation to Azure Data Factory and Azure Synapse Pipelines?
A trigger in Azure Data Factory and Azure Synapse Pipelines is a unit of processing that determines when a pipeline run should be initiated. Triggers can be based on a schedule or an event.
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