Azure Data Factory что это: Полное Описание и Применение

Azure Data Factory - это мощный инструмент для интеграции и преобразования данных, который позволяет автоматизировать процесс загрузки, хранения и обогащения данных в облаке Azure.

Azure Data Factory позволяет создавать потоки данных, которые можно использовать для интеграции данных из различных источников, таких как базы данных, файловые системы и облачные хранилища.

В Azure Data Factory используются активы, такие как Data Flow, что позволяет создавать сложные потоки данных, а также Data Lake Storage Gen2, которое обеспечивает быструю и эффективную работу с большими объемами данных.

Azure Data Factory is a cloud-based service offered by Microsoft Azure that helps manage a large amount of data from different sources.

It's a big problem for companies to deal with data from various sources, including relational databases, file-based data, and data from external sources that are hard to communicate with each other.

Data Factory Azure makes this process easier and faster by allowing users to move, transform, and load data from different sources in a scalable and reliable way.

This means users don't have to spend a lot of time and money implementing ad hoc solutions, which can be complicated and costly.

Azure Data Factory acts as an orchestrator, enabling users to ingest data from diverse sources, transform it as needed, and load it into target systems.

Its code-free design and visual interface make it accessible to a broad range of users, while its underlying scalability and data processing power cater to complex enterprise data integration needs.

At its core, Azure Data Factory facilitates the extraction, transformation, and loading (ETL) or extraction, loading, and transformation (ELT) processes.

It supports both batch and real-time data processing, catering to a wide range of data integration requirements.

Azure Data Factory is a powerful tool for data integration and transformation, and understanding its key components is essential for designing and managing data pipelines effectively.

Data Flows define the data transformation logic within Azure Data Factory, consisting of activities that perform various operations on the data such as filtering, joining, aggregating, and mapping columns.

Datasets represent the data structures within Azure Data Factory, defining the structure and schema of the data being ingested, transformed, or outputted by activities in the data pipelines. Datasets can reference data stored in various sources such as files, databases, tables, or external services.

Linked Services establish connections to external data sources and destinations within Azure Data Factory, providing the necessary configuration settings and credentials to access data stored in different platforms or services.

Pipelines serve as the coordinators of data movement and transformation processes, consisting of activities arranged in a sequence or parallel structure to define the workflow of the data processing tasks. Pipelines can include activities for data ingestion, transformation, staging, and loading into target systems.

Triggers define the execution schedule or event-based triggers for running pipelines in Azure Data Factory, enabling automated and scheduled execution of data integration workflows based on predefined conditions.

Integration Runtimes provide the compute infrastructure for executing data movement and transformation activities within Azure Data Factory, managing the resources needed to connect to data sources, execute data processing tasks, and interact with external services securely.

Data Flow Debug Mode enables developers to debug data flows within Azure Data Factory, allowing them to validate the transformation logic, troubleshoot issues, and optimize performance.

Here are the key components of Azure Data Factory at a glance:

Azure Data Factory offers a graphical user interface that allows you to create and manage activities and pipelines without requiring coding skills, although complex transformations may still need experience with the tool.

You can work with Azure Data Factory to integrate with various data sources, including on-premise data sources like MySQL, SQL Server, and Oracle DBs, thanks to its default connectors.

Azure Data Factory supports branching, where the output of one activity can trigger the start of another, and it also supports tumbling window trigger and event trigger, which are useful for creating partitioned data in a Data Lake setup.

You can work with parameters in Azure Data Factory, which enables you to pass on parameters dynamically between datasets, pipelines, and triggers, for example, to dynamically set the filename of the destination file.

Azure Data Factory allows you to run pipelines up to 1 run per minute, which doesn't enable real-time processing but gets close to it.

Here are some of the key features of Azure Data Factory:

Extracting data is a crucial step in any data processing workflow. You can extract data from various sources, including structured, unstructured, and semi-structured data.

Azure Data Factory offers preconfigured connectors for a wide range of data sources, making it easy to extract data from different sources. These connectors allow you to access data from various applications, databases, and file systems.

To extract data, you can use the Azure Data Factory's visual interface, which doesn't require coding skills. However, complex transformations may require experience with Azure Data Factory.

Azure Data Factory supports various data transfer protocols, including FTP, SFTP, and HTTP, allowing you to transfer data to and from external systems. You can use these protocols to extract files from third-party SFTP servers or other external data sources.

Here are some common data sources that Azure Data Factory can extract data from:

Azure Data Factory also supports branching, where the output of one activity can be a trigger for the start of another activity. This allows you to create complex workflows and automate data processing tasks.

In summary, extracting data is a critical step in any data processing workflow, and Azure Data Factory provides a range of tools and features to make it easy to extract data from various sources.

Data transformation is a crucial step in the data pipeline process. It allows you to manipulate and refine data from various sources into a usable format for analysis.

With Azure Data Factory, you can combine, divide, add, derive, remove, or copy data from one source to another, making it easier to integrate data from different systems.

Data transformation can also be achieved through mapping and business rules, which enable you to apply specific logic to your data. For instance, you can use the Copy Activity to move data from both cloud sources and local data repositories to a centralized data warehouse in the cloud.

Scheduling the execution of pipelines is also possible, allowing you to update data at predefined times or in response to specific events. This ensures that data is always up-to-date and available when needed.

Azure Data Factory supports external activities, such as executing SQL queries on relational databases, which can be performed on computing services like Spark, HDInsight Hadoop, Machine Learning, and Data Lake Analytics.

Activating the UI is a straightforward process. Once your resource is created, click on Go to resource and select Start Studio to access the Azure Data Factory user interface in a separate tab.

You can configure data pipelines to extract data from the local SQL Server database and load it into the Azure SQL data warehouse. This is where the real magic happens, allowing you to define activities like copying, transforming, and loading data.

To access the UI, you'll need to have your resource created first. The process of creating a resource is detailed in a separate section, but essentially, it involves logging in to the Azure portal, selecting Analytics, and clicking on Create a resource.

In the UI, you can use available visual components to extract data from a SQL Server database, transform data using Azure Databricks, or load data from local files into Azure SQL Data Warehouse. This flexibility is one of the key benefits of using Azure Data Factory.

Parameterization in Services is a crucial aspect of designing effective features and functionality. It allows developers to define a set of parameters that can be used to customize the behavior of a service.

A well-structured parameterization framework can help reduce complexity and improve maintainability of the service. This is evident in the example of the "Payment Gateway" service, which uses a parameterization framework to support multiple payment methods.

By using a parameterization framework, developers can easily add or remove payment methods without modifying the underlying code. This flexibility is a key benefit of parameterization in services.

The "Order Management" service also benefits from parameterization, allowing it to support multiple shipping options and payment gateways. This enables developers to easily switch between different shipping options and payment gateways without modifying the code.

Parameterization in services can also help improve scalability and performance. For example, the "Product Recommendation" service uses a parameterization framework to support multiple recommendation algorithms, allowing it to scale more easily to handle large volumes of traffic.

In summary, parameterization in services is a powerful tool that can help improve the flexibility, maintainability, and scalability of a service. By using a well-structured parameterization framework, developers can create services that are more adaptable and efficient.

Azure Data Factory works by providing intuitive visual tools based on a drag-and-drop interface that allows you to perform various transformation operations, such as uniting, filtration, aggregation, and custom transformations, to prepare data for analysis or loading to a destination.

It offers a comprehensive end-to-end platform tailored for data engineers, operating through a streamlined process encompassing several key stages: Connect and Ingest, Transform and Enrich, Deploy, and Monitor.

You can leverage numerous built-in connectors to access data from various cloud and on-premise sources, including databases, SaaS applications, and data warehouses.

Azure Data Factory provides a code-free user interface for designing data pipelines, allowing you to construct pipelines by dragging and dropping elements.

Here are the key stages of the data pipeline process:

Azure Data Factory also provides monitoring and management tools to observe the progress of the execution of workflows, identify any problems or anomalies, and optimize their performance.

Azure Data Factory's pricing system is based on actual use of the service, where you pay for computing and storage resources used during data pipeline execution.

You'll be charged for the minutes tasks were executed, data integration units used, and storage space occupied by data, with costs calculated automatically.

Some external connectors and resources used, like Azure SQL Database or Azure Blob Storage, may incur additional costs based on usage and service rates.

The actual costs may vary based on current rates, actual usage, and other factors specific to your use case, so it's a good idea to consult the official Azure documentation for more information.

Microsoft offers an Azure cost calculation tool to help estimate specific costs for your company based on geographical area, currency, and service usage duration.

To create an Azure Data Factory, you'll need to log in to the Azure portal using your account. If you don't have an account, you can create one for free in just a few clicks.

You'll then need to click on Create a resource in the portal menu and select Analytics. From the list of analysis services, choose Azure Data Factory and click on it.

To fill in the basic details, click on Create and enter the required information in the fields provided, such as name, subscription, resource group, and area.

Once you've completed the basic details, click on Git configuration to set up integration with a Git repository to manage the pipeline code.

You'll then need to verify the default settings on the Network page and make any necessary changes to permissions and network rules based on your needs.

Finally, review the settings and details of your Data Factory on the Review and create page, and click on Create to start creating your resource.

Pricing is a key consideration for any Azure Data Factory user. The pricing system is based on actual use of the service, where you pay for computing and storage resources used during data pipeline execution.

You'll be charged for the minutes tasks were executed, data integration units used, and storage space occupied by data. The storage class used also plays a role in determining costs.

External connectors and resources used, as well as external cloud services like Azure SQL Database or Azure Blob Storage, may incur additional costs based on usage and rates. These costs can add up quickly, so it's essential to keep them in mind.

Actual costs may vary based on current rates, usage, and other factors specific to your use case. To get an accurate estimate, consult the official Azure documentation.

Microsoft offers a convenient cost calculation tool to help you estimate costs based on geographical area, currency, and service usage length. This tool can be a valuable resource in finding the most suitable solution for your needs.