In today’s world, IT Professionals and business stakeholders alike know that data is the most valuable asset for organizations. To manage that asset efficiently, organizations are adopting a modern data stack. Once they adopt the Modern Data Stack to democratize the creation, processing, and analysis of data, they need a reliable and efficient data integration platform to prevent that data ecosystem from turning into an unwieldy beast due to organic growth. A metadata-driven data integration framework is one such methodology that can help organizations manage and integrate their data in a more efficient manner with the modern data stack. In this blog post, we will explore what a metadata-driven data integration framework is, its benefits over the traditional approach, its use cases, and how Fresh Gravity can help expedite building Data Integration (DI) platforms using this framework. 

What is a Metadata-driven data integration framework? 

A metadata-driven data integration framework isn’t just a combination of traditional and contemporary technologies, but a design concept that relies on metadata to manage and integrate data from various sources. Metadata is data that provides information about other data. It includes information about data structure, data types, data format, and data relationships. In a metadata-driven data integration framework, metadata is used to describe the data sources, transformation rules, and target data structures. This metadata is used to generate dynamic mapping/code which can then be used in the data integration process. 

Six Reasons that metadata-driven data integration is superior to traditional data integration 

(1) Standardization: Metadata-driven data integration provides a standardized approach to integrating data from multiple sources. It ensures that all data sources are integrated using a set of managed rules and standards, improving data quality, and reducing the risk of errors. Traditional data integration, on the other hand, relies on manual coding and can lead to inconsistencies and errors.

(2) Reusability: Metadata-driven data integration promotes the create-once-and-re-use approach. The mappings between the source and target data structures can be reused for future data integration projects, which further reduces development time and cost. In a traditional approach, developers build point-to-point pipelines which cater to specific use cases and are often not reusable.

(3) Automation: Metadata-driven data integration automates various steps of the data integration process, eliminating the need for manual mapping effort. Using advanced metadata-managers, developers can automate source-to-target mapping. In this augmented approach metadata manager can use a Machine Learning (ML) driven auto-data-mapper/classifier to analyze and compare the metadata, data, semantics, contexts, relations across data sets and predict the source-to-target mapping along with the rules needed to transform source data into the desired target data. On the contrary, traditional data integration requires a Business Analyst (BA) to manually profile data, prepare the source-to-target mapping and then developers to write custom code for each data mapping, which is both inefficient and time-consuming.

(4) Flexibility: Metadata-driven data integration provides a flexible and scalable solution. Since it is configuration driven, there is no need to code every time for a new source or requirements. Businesses can add new data sources and data structures as their needs change, and they can scale their data integration processes to meet their growing data integration needs. Traditional data integration is limited by the skills and availability of developers, making it less flexible and scalable.

(5) Easier Maintenance: Metadata-driven data integration is easier to maintain than traditional data integration. Changes to the data integration process can be made by updating the metadata, rather than modifying the code. This makes it easier to update and maintain the data integration process over time.

(6) Improved Collaboration: Metadata-driven data integration promotes collaboration between developers and business users. Business users can create and manage metadata without requiring any programming skills, which improves communication and collaboration between the IT department and the business users.

In summary, metadata-driven data integration is better than traditional data integration because it provides a standardized, automated, flexible, and easier-to-maintain approach to integrating data from multiple sources. It promotes collaboration between developers and business users and can be scaled to meet the growing data integration needs of businesses. 

The Three Components of a Metadata-driven Data Integration Framework 

A metadata-driven integration framework includes three major components.  

The first component is the metadata repository. The metadata repository is a centralized database that stores the metadata about the data sources, data structures, and business rules. The metadata repository also stores the mappings between the source and target data structures, orchestration rules, job-run/audit information, water-mark tables, and other supporting configuration information that’s relevant for the metadata-driven pipelines. 

The second component is the metadata management tool. The metadata management tool is used to create, update, and manage the metadata stored in the metadata repository. The metadata management tool should provide an intuitive user interface that allows non-technical or business users to create and edit metadata for source to target mapping along with transformations, orchestration, exception handling, data validation rules without requiring any programming skills. As mentioned above, with the help of ML-driven data classification algorithm, this metadata manager can also be upgraded to an auto-data-mapper or classifier, that can auto generate source-to-target mapping with little to no human intervention. 

The third component is the integration engine. The integration engine is responsible for reading the metadata from the metadata repository and using it to perform various actions to integrate data from various sources. The integration engine uses the mappings stored in the metadata repository to transform the data from the source format to the target format. To build such an engine you don’t have to re-invent the wheel, as lots of off-the-shelf integration and orchestration tools like Talend, Informatica, Matillion, Glue, Azure Data Factory, DBT, Airflow, and Databricks can support this design with some customization/combination. Also, tools like Fivetran, Stitch, and DBT are already several steps ahead in adopting this methodology. Therefore, technology is not a challenge to adopt this framework. 

Eight Key Principles of building a metadata driven data integration framework 

(1) Metadata is the Foundation: Metadata should be considered as the foundation of the data integration framework. It should be used to describe the data assets, including their structure, content, quality, lineage, and usage. 

(2) Standards-Based: To ensure consistency and interoperability, it’s important to use a standardized metadata model that is applicable to all data assets being integrated. This model should cover key aspects of data integration such as data structure, data quality, data lineage, and data usage. 

(3) Business-Focused: The metadata should be business-focused, meaning that it should describe the data in terms that are meaningful to the business stakeholders. This includes using business language to describe the data, as well as aligning the metadata with the business goals and objectives. 

(4) Integrated: The metadata-driven data integration framework should be integrated with other systems and technologies used in the organization. This includes data profiling tools, data quality tools, data governance tools, data modeling tools, and data visualization tools. 

(5) Agile: The metadata-driven data integration framework should be agile and adaptable to changing business requirements. This means that the framework should be able to accommodate new data assets, new metadata standards, and new data integration scenarios as they arise. 

(6) Automated: The data integration framework should be automated to the extent possible,  to reduce manual effort and increase efficiency. This includes using tools to automate data mapping, transformation, and loading processes. 

(7) Governed: The metadata-driven data integration framework should be governed by a set of policies and procedures. This includes defining roles and responsibilities for managing the metadata, as well as defining processes for resolving metadata-related issues. 

(8) Measurable: The metadata-driven data integration framework should be measurable, with key performance indicators (KPIs) established to track its effectiveness. This includes measuring data quality, data lineage, and data usage. 

By following these key principles, organizations can build a robust and effective metadata-driven data integration framework that supports their business goals and objectives. 

Some of the use cases for a metadata-driven data integration framework are –  

(1) When migrating data to a new system, metadata-driven data integration framework can be used to map and migrate the data from the old system to the new system. This reduces the time and effort required to migrate data and ensures that the data is consistent and accurate. 

(2) Metadata-driven data integration frameworks can be used to integrate data from multiple sources such as databases, APIs, and files. This makes it easier to manage and analyze data from various sources. Once the metadata-driven pipeline is built then business users can reuse that pipeline and access or integrate data from different sources just by defining source-to-target mapping without the need to program skills. This enables self-service data preparation and analysis, which can improve data democratization and empower business users to make data-driven decisions. 

(3) Metadata-driven data integration frameworks can be used for real-time data integration. Using advanced schema/metadata registry, data can be mapped, integrated, and analyzed in real-time, providing organizations with up-to-date insights. 

(4) Metadata-driven data integration can help organizations approach data with a product mindset by providing a comprehensive understanding of the data, its attributes, and its use cases. By leveraging metadata to describe the structure, content, and business rules of the data, metadata-driven data integration can enable teams to build and manage data products with the same rigor and discipline as they would with any other product. It can help the analytics engineer (comparatively new but very important specialized role in data analytics) curate the catalog more efficiently so that the researchers can do their work more effectively. 

(5) Finally, popular contemporary concepts (like Data Fabric) need a robust data integration backbone to succeed. Only a metadata-driven approach can help with standardizing and unifying metadata across different systems and platforms, improving data governance, enabling the reuse of data integration processes, and supporting self-service data preparation and analysis. It can make a data integration platform easily compatible with various data delivery styles (including, but not limited to, ETL, ELT, streaming, replication, messaging, and data virtualization or data microservices). Therefore, it’s essential to adopt this approach for implementing Data Fabric. 

Making it Work for You 

A Metadata-driven data integration framework is a solution that simplifies the data integration process. It basically turns traditionally ignored passive metadata into an active metadata. It provides a standardized, automated, and flexible approach to integrating data from multiple sources. The metadata-driven integration framework reduces development time and cost and improves data quality. As businesses continue to rely on data, metadata-driven data integration framework will become even more important in the future. 

At Fresh Gravity, we follow this framework and have built a reusable, ready-to-deploy data integration package that follows the design principles outlined above. We have successfully built various Data Integration (DI) platforms using this approach with tools like Talend, Matillion, Glue, Azure Data Factory, and Databricks, among others. Here are some of the key benefits of using Fresh Gravity’s ready-made Metadata-driven data integration package: 

(1) The base version of the integration package can be deployed in 4-6 weeks, as it comes with ready-to-use boilerplate pipelines for preferred DI tools 

(2) All the pre-built pipelines are not only designed for metadata-driven data processing, but also equipped to handle custom orchestrations, error handling, and other important tasks along with ELT (Extract, Load, Transform) based data massaging 

(3) It comes with a pre-defined and ready–to–deploy Metadata Repository 

(4) It comes with an intuitive UI to add/update/manage Metadata seamlessly 

(5) As an added feature, Fresh Gravity has also developed an AI-driven auto-data-mapper/metadata manager, called Penguin, that simplifies and accelerates the data mapping process by automatically analyzing the metadata, data, semantics, contexts, relations across data sets and predicting the source-to-target mapping for any given data sets 

(6) Finally, it comes with an out–of–the–box audit-balance-control log to ensure better operational control 

Please reach out to Soumen Chakraborty at soumen.chakraborty@freshgravity.com if you want to schedule a demonstration of Fresh Gravity’s Metadata-driven Data Integration Framework.   

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