The Key to Successful Data Ingestion: A Metadata-Driven ApproachPublish Date: April 11, 2023
As the ‘information about information,’ metadata is the backbone of all digital objects and is critical to their management and usage. A metadata-driven data pipeline is a powerful tool for efficiently processing data files. However, this blog discusses metadata-driven data pipelines specifically designed for RDBMS sources.
A data ingestion framework contains vital information describing the steps required to process data files successfully. There is no doubt that data ingestion is the first step in a data pipeline. It creates the repository where data is imported and from where it is obtained. However, it is also a complex process requiring a strategic approach to ensure data is appropriately managed. Metadata-driven data ingestion framework simplifies the data ingestion process.
What does a metadata-based data ingestion framework include?
In application development, a framework provides a programming basis in addition to the generic structure, tools, functions, and classes supporting app development. Similarly, a metadata-driven data ingestion framework provides a foundation for data ingestion, enabling data to be processed efficiently and accurately. The metadata ingestion framework helps gather and integrate data from different sources. Such a framework comprises the technologies and processes – data repositories, data integration software, and data processing tools – used to extract and load information for data ingestion.
The most common data ingestion architectures include batch, real-time, and one-time modes. The end-user application’s data processing purpose determines the choice of a method – this could be building a data-driven product or making a business-critical analytical decision.
Users may either hand-code a bespoke framework to meet their organization’s unique requirements or employ an off-the-shelf data ingestion tool. The data’s complexity, the possibilities of automating the process, the urgency of data analysis, the applicable regulatory and compliance requirements, and the quality parameters are the factors to consider in building the data ingestion framework.
At YASH Technologies, we have developed reusable data pipelines as part of our ingestion framework that can be configured with specific metadata and parameters to trigger the execution of the actual data pipeline. This approach allows for greater flexibility and efficiency in the data processing.
We add specific audit columns to the existing data to ensure the traceability and lineage of the ingested data. This metadata provides valuable information about the origin and processing of the data, adding more meaning and value to the overall dataset.
By incorporating configurable metadata and audit columns, our data pipelines can be easily traced and managed throughout the data ingestion process. This approach enhances the data’s quality and facilitates better decision-making based on its insights.
Scaling the ingestion process with metadata-driven data ingestion
Our metadata-driven approach leverages cloud-native services and tools. Our framework utilizes several Azure services, including Azure Data Lake Storage (ADLS) as the data repository. ADLS offers a scalable and easy-to-manage solution to meet any capacity requirements, with the advantage of being hosted on Azure’s global infrastructure.
For data integration, we rely on Azure Data Factory pipelines, which provide a high-performance mechanism for ingesting large amounts of data while being cost-effective. Azure Data Factory is optimized for handling large volumes of data and has been designed to be scalable, allowing it to grow alongside our data needs.
For data processing, we utilize Azure Data Factory and Azure Databricks, which are optimized for speed, reliability, and scalability across all workloads.
The performance of a data pipeline can also be significantly impacted by spikes in usage from end-user applications. However, our ingestion framework is designed to operate independently of end-user apps, so our data pipeline performance remains unaffected by these spikes. This is because we have decoupled the ingestion framework from end-user applications, ensuring that issues with end-user app usage do not impact the overall data pipeline performance.
Thus, our decoupled ingestion framework, coupled with the scaling capabilities of underlying technologies, allows us to maintain the high-quality performance of our data pipeline, even in the face of unexpected spikes in usage from end-user applications.
Advantages of Metadata-Driven Data Ingestion Framework
- Uniformity: Standardizes data ingestion process with generic workflows.
- Shorter development time: Decreases custom code required for significant time savings.
- Agility: Offers flexibility to create and modify configurations easily without code changes.
- Scalability: Adding new data sources, configurations, and environments via metadata is easy.
- Maintainability: Excel spreadsheets contain almost everything from business logic to the data flow.
- Speed: Enables using existing ETL platforms without having to use new ones.
- Reusability: The generic design allows features to be reused by other sources and applications.
- Ease of maintenance: Only one set of codes to maintain can be applied to all sources.
YASH’s support for enhancing the value of business data
Today, many organizations in BFSI, healthcare, and other verticals strategize their growth via mergers and acquisitions. They need metadata-driven data ingestion frameworks to integrate their business systems and data from partner companies efficiently. To enhance their general business decision-making and create better customer experiences, companies also want the ability to integrate a massive variety of data sources while keeping the process fast, manageable, and repeatable.
YASH helps you achieve these goals and simplify your data analytics by implementing a flexible metadata-driven data ingestion network. We have the technical knowledge and experience that companies need to use the tools for ETL development. Our solution architects will integrate your data from multiple sources and load it into a repository to analyze it in real time with business intelligence tools for insightful dashboards.
To learn more about our user-friendly and secure metadata-driven data ingestion procedures, email us at email@example.com.
Senior Tech Lead