Azure Synapse Pipeline: A Comprehensive Guide

Azure Synapse Pipeline is a powerful tool that enables you to integrate and transform data from various sources into a unified view.

It provides a scalable and secure way to manage data workflows, allowing you to automate data processing and analytics tasks.

With Azure Synapse Pipeline, you can create data pipelines that can handle large volumes of data and scale as needed.

The pipeline can be easily monitored and managed through the Azure portal, providing real-time insights into data processing and analytics.

Azure Synapse Pipeline supports a wide range of data sources, including relational databases, NoSQL databases, and cloud storage services like Azure Blob Storage.

This flexibility makes it an ideal choice for organizations with diverse data ecosystems.

By leveraging Azure Synapse Pipeline, you can streamline your data workflows, reduce costs, and improve data-driven decision-making.

To set up an Azure Synapse pipeline, you'll first need to create a Synapse Serverless database that can read data from your data lake via SQL views. This database will be used to read ScheduleTriggers and TriggerList CSV files.

A SQL view is used to read the ScheduleTriggers.csv file and return the data in SQL table format. This view is a crucial part of the pipeline setup process.

You'll also need to create a Synapse pipeline called DoWork, which will expect a parameter called ExtractType to be passed when called and simply execute a Wait task for one second. This pipeline is a simple starting point for more complex pipeline logic.

To create a Synapse pipeline that can orchestrate the flow of logic to stop and delete existing ExtractType triggers, you'll need to create a pipeline called DeleteTriggers. This pipeline will retrieve the list of triggers from the TriggersList Data Lake file and make a web request call to the Key Vault to retrieve your Synapse workspace URL endpoint.

Here are the main steps to create a Synapse pipeline:

By following these steps, you can set up a Synapse pipeline that can read data from your data lake, stop and delete existing triggers, and create new triggers as needed.

A Synapse pipeline is composed of several key components that work together to provide a platform for data-driven workflows. These components include pipelines, which are the top-level workflow that orchestrates the flow of logic to move and transform data.

Pipelines can be broken down into smaller activities, which are the individual steps that are executed as part of the pipeline. These activities can include tasks such as reading data from a data lake, executing a SQL stored procedure, or making a web request call to a Key Vault.

Datasets are another key component of Synapse pipelines, and they are used to store and manage the data that is being processed. Linked services, on the other hand, are used to connect to external systems and services, such as Key Vault or Azure Databricks.

Data Flows are used to transform and process data, and they can be created using a variety of tools, including Data flow, SQL stored procedures, Synapse Notebooks, and Azure Databricks. Integration Runtimes are also used to execute data flows and other activities in the pipeline.

Here is a summary of the key components of a Synapse pipeline:

Synapse Analytics workflow is composed of several key components that work together to provide a platform for composing data-driven workflows. These components are the foundation of building complex data pipelines.

At the core of Synapse Analytics are Pipelines, which represent a series of activities that are executed in a specific order. Pipelines can be used to orchestrate the flow of logic to move and transform data.

Pipelines are made up of individual Activities, which represent a processing step in a pipeline. Activities can be used to copy data, transform data, or execute a stored procedure.

Data is stored in Datasets, which represent the structure of the data. Datasets can be used to store data in various formats, such as CSV, JSON, or Avro.

Linked services are used to define the connection information needed to connect to external resources. They are like connection strings, which define the connection to the data source.

Here are the key components of Synapse Analytics, summarized in a table:

By understanding these key components, you can build complex data pipelines that move and transform data efficiently and effectively.

In a Synapse Notebook, you can ingest SAP tables into a dedicated SQL pool for distributed computing-enabled databases.

Dedicated SQL pools in Synapse use a massively parallel processing architecture, similar to Snowflake, designed for big data.

Data is loaded into a Spark dataframe in a Synapse notebook and transformed using PySpark.

The resulting table is persisted into a table in Synapse's Lake database, with physical files stored in the workspace data lake.

The metadata for the Lake database table is stored in a metastore, likely using Hive, although documentation on this is unclear.

Data flow is Synapse pipelines' low code/no code option for transformation, using a Spark pool as compute. It allows data engineers to develop data transformation logic without writing code.

You can design code-free ETL using data flow, which involves copying data from on-premise, other clouds to Azure, staging data transformation, scheduling triggers for pipeline execution, and monitoring processes and configuring alerts.

A data flow activity can be triggered after a 'Copy Data' activity is successful, and it uses the staged data as a source and goes through the transformation logic defined in the data flow.

Here's a comparison of Synapse Data Pipelines and ADF Pipelines within Microsoft Fabric:

The Integration Runtime is the compute infrastructure used by Azure Data Factory and Azure Synapse pipelines.

It's referenced by the linked service or activity, and provides the compute environment where the activity either runs on or gets dispatched from.

An integration runtime provides the bridge between the activity and linked services, making it a crucial component in the data flow process.

A linked service defines a target data store or a compute service, and the integration runtime is what enables the activity to interact with these services.

It's essentially the behind-the-scenes infrastructure that makes data integration possible in Azure Data Factory and Azure Synapse pipelines.

ETL Flow is a crucial part of data integration, and with Synapse, you can create code-free ETL flows that focus on building business logic and data transformation.

These flows can be designed to copy data from on-premise or other cloud sources to Azure, allowing for seamless integration with your existing systems.

Data flows in Synapse are visually designed data transformations that don't require writing code, making it easier for data engineers to develop data transformation logic.

Here are the key steps to design a code-free ETL flow in Synapse:

By following these steps, you can create an ETL flow that is efficient, scalable, and easy to maintain, allowing you to focus on more complex data integration tasks.

Data flows in Synapse use a Spark pool as compute, making it a low-code/no-code option for transformation, and can be executed as activities within Azure Synapse pipelines that use scaled-out Apache Spark clusters.

This approach allows for real-time data transformation and integration, making it ideal for applications that require high-speed data processing and analysis.

Microsoft Fabric is a unified solution for data integration that streamlines analytics and data operations under one umbrella. It integrates various data tools, including Power BI, Synapse Analytics, and Data Factory.

Synapse Data Pipelines are used for advanced big data analytics, handling large-scale, metadata-driven ETL processes. This makes them ideal for high-scale, analytical workloads, especially when dealing with data lakes and real-time analysis.

Microsoft Fabric leans heavily toward Synapse Data Pipelines for massive data volumes. This is because Synapse Data Pipelines are well-suited for high-scale, analytical workloads.

ADF Pipelines, on the other hand, are better suited for simple, operational data flows across hybrid environments. They're the go-to solution for hybrid integration and orchestration.

Here's a comparison of Synapse Data Pipelines and ADF Pipelines:

Pipeline Management is a crucial aspect of Azure Synapse Pipelines. It allows you to orchestrate the flow of logic to automate complex tasks.

To create Synapse Pipelines, you'll need to define the sequence of tasks that need to be executed. This can be done by creating a parent pipeline that calls other child pipelines.

A parent pipeline can be used to orchestrate the flow of logic by calling other pipelines in a specific order. For example, the GetExtractTypeTriggers pipeline calls the DeleteTriggers pipeline and then the CreateExtractTypeTriggers pipeline.

Here are some key steps to consider when managing pipelines:

The DeleteTriggers pipeline retrieves the list of triggers from the TriggersList Data Lake file and makes a web request call to the Key Vault to retrieve the Synapse workspace URL endpoint.

The CreateExtractTypeTriggers pipeline calls the SQL stored procedure dbo.SP_GetSchedule to retrieve the list of triggers to create and the JSON definitions by passing in a pipeline parameter called ExtractType.

Azure Synapse Pipeline is organized into a hierarchical structure, consisting of a pipeline, activities, and tasks. This structure allows for efficient management of complex data pipelines.

A pipeline can have multiple activities, which are the individual components that perform specific tasks. Each activity is a self-contained unit, making it easy to manage and maintain.

Activities can be grouped into tasks, which are collections of activities that are executed together. This allows for more control over the execution of activities and improves pipeline performance.

Tasks can be executed in parallel, enabling faster execution of pipeline activities. This is particularly useful for large datasets or complex pipelines.

Azure Synapse Pipeline also supports conditional execution of activities, allowing for more flexibility in pipeline design. This is achieved through the use of conditional statements, such as IF-THEN-ELSE, which enable activities to be executed based on specific conditions.