As more organizations look to solutions for controlling the movement of data sources across multiple sources to a single repository, flexible real-time connections are being developed to ensure the correct ETL of data assets. This includes graphical data transformation integrations through no-code interfaces.
One such solution is using Azure Data Factory data flow through data transformation. Both Azure Data Factory and Azure Synapse Analytics have pipelines that provide numerous SAP connectors, offering potent support across a wide range of data extraction (ETL) scenarios.
This is crucial considering the improvement and integration of Change Data Capture (CDC), which is able to identify and track any changes to a set of data in real time. That improves the downstream or system processes – a critical tool for streaming or event-driven architectures.
There are plenty of other applications, but with these tools in hand, companies are able to better manage, access, collect, and aggregate data assets – even with multiple internal/external touchpoints and apps.
Considering the need for businesses and independent organizations to initiate data transformation to gain the most benefit from assets, it only makes sense to explore tools like Azure Data Factory data flow integration. This way, when a conversion, cleansing, or restriction is required, it can be completed through a simplified process.
What is Data Transformation
In its simplest form, data transformation converts data assets from one format into a different format. Most of the time, this is applied to raw data assets that are not correctly formatted for later use by data tools or users.
Data in its raw form may have missing fields, duplicate entries, or incomplete information. Running these sources through data transformation ensures they can be valuable later on.
This is especially true if a company works with raw or source data from multiple sources. As many organizations have numerous data systems throughout their history, it can be challenging to get all the legacy data and new data into the same format.
Having a method in place to streamline this operation helps ensure data governance, management, and organization into the best-fit databases. This also helps with security and oversight issues that may crop up related to any industry-specific regulations.
Data transformation is also a valuable tool for cleaning, organizing, and controlling older data being newly digitized or prepared for cloud management. That is why some companies have moved to data transformation platforms and service integration to improve the overall quality of data across various on-site, cloud, or hybrid systems. However, tools are also available to those wishing for an easy-to-use graphical interface. Once such example is known as Azure Data Factory.
Azure Data Factory – Introduction
Raw data is useless until it is cleaned and organized. The only way to gain powerful or meaningful insights is to refine these resources so data scientists, analytical tools, or decision-makers can benefit from the information gathered.
Azure Data Factory provides the orchestration and smooth operational processes to help initiate and maintain the data transformation of various assets. It is a managed cloud service built to handle highly complex and scalable ETL data projects.
Using ADF allows an organization to compare data stores from various sources against other critical information like customer data, projections, and real-time changes to compute valuable insights. All of this is achieved through creating and scheduling data pipelines. The complexity of the ETL process or workflow you wish to initiate is entirely based on your unique needs, connectors, and raw data.
The critical aspect to getting the most value out of Azure Data Factory involves how each layer contributes to the overall design and solution of the infrastructure you have in place. This can include:
- Pipelines – a GUI that can use drag-and-drop widgets to draw different data paths.
- Activities – anything that actually does something to your raw data, usually viewable through a graphical widget.
- Source & Sink – where data moves into and out of a given activity.
- Data Set – any particular defined set of data resources that Azure Data Factory uses in operations.
- Linked Service – the specific connector allowing Azure Data Factory to access and utilize outside data resources.
- Integration Runtime is the gateway layer where ADF communicates to other software processes outside the native environment.
One of the significant benefits of using Azure Data Factory is the graphical visualization of data and how it moves and connects to the more substantial system. Being able to use no-code/low-code solutions that allow for real-time change detection allows more flexibility for pipeline and data flow creation specific to the needs of the organization.
Azure Data Factory Pipeline vs. Azure Data Factory Data Flow
The key to the difference between an Azure Data Factory pipeline and a data flow has to do with orchestration and data transformation.
ADF pipelines are created and laid out to orchestrate the various processes of copying data through connectors. In contrast, data flow has much more to do with the actual data transformation process.
Engineers, data scientists, and other users can create data flows using a graphical representation that doesn’t require an in-depth knowledge of working code. These data flows are then executed as part of the activities inside various pipelines. This makes them scalable and able to connect with a wide range of other resources through connectors.
This creates high value for organizations looking to transform raw data into usable resources for later decision-making or analytical tools. Understanding the core benefits of this systematic process will help your team decide whether or not Azure Data Factory is best for your unique situation.
Benefits of Leverage ADF Data Flow for Data Transformation
After you have added different data sources through connectors to your Azure Data Factory pipelines, you can create different transformations and apply that logic to your raw or data assets.
This can include an extensive list of functions that report any change detections through data flow transformation like:
- Join
- Split
- Exists
- Union
- Lookup
- Derived Column
- Select
- Aggregate
- Pivot
- Window
- Rank
- Flatten
- And more
The point being you create a great deal of versatility and flexibility in the data being collected and how you can clean, organize, and transform it into usable resources.
Best of all, this can be completed through a graphical interface without the need for coded solutions.
That directly benefits your organization by making data assets:
- Easier to digest and govern across your system due to the unification of formatting.
- Higher quality and easier to protect.
- More compatible across various apps and data types gathered from other sources.
- More standard, increasing efficiency in cloud adoption and inputting into new analytical tools.
- Speeds up the retrieval of information from various queries because it has been properly cleaned and transformed into more appropriate formats.
- Available with real-time change updates.
All of this is significantly more user-friendly because every step of the ETL process is completed with UI no-code actions, especially when copying data from one source to another.
Using Data Copy in Azure Data Factory
Bulk data assets have become easier to manage with the Data Copy action inside of Azure Data Factory.
For example, if a company uses the provided connectors, batch data can be copied or updated via triggers. This allows the software to locate and extract the specific data that has been somehow modified since the last time there was a refresh.
This is achieved without modifying or losing the related history – improving the trackability of any data changes.
If you wanted to take data from Azure Blob storage and move it to an SQL database, you could create a new data factory and then a pipeline that directly copies from one location to another in bulk.
Using Azure Data Factory does a similar action but with real-time updates based on source change detection.
This speeds up many processes and enables copying data from various on-site and cloud sources over to new stores of your choosing. You then use data flows within these pipelines to clean, organize, and transform that data into the format of your choosing.
In other words, you get the data you need in the manner you need it without having to wait for numerous manual processes of the past.
Conclusion
Using data in today’s fast-paced, highly competitive world requires consistent updates and real-time changes of any assets. Implementing Azure Data Factory data flow resources ensures this information is accurate, cleaned, organized, and recent, so any future decision-making based on these assets is accurate.
ADF integration has become significantly more accessible through the various connectors present. With an easy-to-use graphical UI and no-code/low-code data flow through created pipelines, organizations can benefit and scale quickly, even with massive data resources across a wide range of data sources.