Data Ingestion process in Microsoft Fabric

Data ingestion, also known as data extraction, is a fundamental process in modern data management. It involves the acquisition and gathering of data from diverse sources into a centralized data estate or data ecosystem. The primary objective is to extract meaningful information and insights from this data, enabling data-driven decision-making. The sources from which data is collected can include databases, Excel spreadsheets, CSV/TXT files, APIs, and streaming systems. Once collected, the data is brought into a central repository or data lake for further processing and analysis. 

Data ingestion can be executed through batch processing or scheduled intervals, depending on the requirements and nature of the data. It is a crucial step in the data management pipeline, enabling organizations to harness the full potential of their data and derive actionable insights. 

Extraction, on the other hand, involves the process of transforming raw data into a structured format suitable for processing, analysis, and reporting. This phase plays a significant role in uncovering valuable patterns and insights from the collected data. During extraction, various essential steps are undertaken, such as data cleaning, transformation, aggregation, and joining with other datasets. These steps ensure data consistency, handle missing values, and format the data to ensure data quality. 

In essence, data ingestion and extraction are about moving data from its source to another system, ensuring its cleanliness, transforming it into the required format, and then copying it into another destination. In the context of Microsoft's data management solutions, several tools facilitate this process, including Azure Data Factory, Azure Event Hub, Azure Stream Analytics, Azure Databricks, Azure SQL Database, and Azure Synapse Analytics. 

Now, let's look into Azure Data Factory, a cloud-based integration service designed to create pipelines for data ingestion and transformation, supporting both batch and real-time data processing. Data ingestion and extraction can be achieved using data flows and data pipelines.  

• Data Flows: Within the Azure Data Factory ecosystem, data flows serve as a low-code visual UI tool, simplifying the design and orchestration of data transformation processes. Users can conveniently drag and drop visuals to pull data from various sources and leverage a wide array of transformation tools. Data can be seamlessly pushed to different destinations, making the process user-friendly, even for non-technical personnel. 
• Data Pipelines: Data pipelines are essential tools that connect interrelated processes and operations required for the smooth flow of data from sources to destinations. These pipelines serve to control data movement and transformation, enabling efficient data processing, storage, or consumption. 
• Fabric: Fabric emerges as an all-encompassing solution for enterprises, covering data movement, data science, real-time analytics, and business intelligence. By integrating Power BI, Azure Synapse, and Azure Data Factory into a single environment, Fabric empowers organizations to streamline their data management processes effectively. 

To read more click here

At the core of Fabric lies the data lake, referred to as "One Lake." This foundational element is built on Azure Data Lake Storage (ADLS) Gen2 and offers a unified Software-as-a-Service (SaaS) experience, catering to both professional and citizen developers. One Lake serves as the central repository where all data is sourced and stored, ensuring seamless access and retrieval for further processing and analysis.  

Data ingestion and extraction are critical components of data management, facilitating the conversion of raw data into actionable insights. With innovative solutions like Azure Data Factory and Fabric, organizations can streamline their data management processes and test the full potential of their data assets.