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Image showing API generation for data mesh

Data mesh, it's one of the hottest data science topics among software engineering teams, data scientists, and anyone interested in building a more effective data infrastructure. This concept is a relatively new model for data management, helping large enterprises scale their data footprint to accelerate digital transformation. Many industries, like retail and banking, see how crucial data is, yet few have mastered ways to harness it. API generation for data mesh is one of the ways you can start.

Companies are using multiple data pipelines and engineering tools but aren't leveraging data to its fullest potential — and that data is becoming more complicated each day. The solution? Data mesh principles and strategies paired with API generation. This relationship will help companies accelerate this process and optimize data analytics while ensuring greater decision-making and scalability. 

Here's the key things to know about API Generation for data mesh:

  • The big data analytics market is projected to reach $103 billion in 2023, but 95% of businesses struggle with managing unstructured data, highlighting the importance of prioritizing data management.
  • Many organizations recognize the value of data but face challenges in fully leveraging it due to issues like data silos, noncompliance, and poor risk management.
  • Data mesh, introduced in 2019, offers a new approach to data management, focusing on decentralizing data responsibility to domain-oriented teams and treating data as a product.
  • Data mesh aims to address challenges associated with centralized data management, such as lack of data ownership, quality issues, and poor scalability.
  • The concept of data mesh is complemented by API generation, which plays a vital role in connecting data products within a data mesh architecture, enhancing data accessibility and governance.

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The True Value of Data

The big data analytics market is anticipated to hit $103 billion in 2023, yet 95% of businesses say managing unstructured data is a problem for their business. That figure is eye-opening, showing the potential competitive advantages of making data a top priority this year and beyond. 

Most modern organizations understand the value of data — that's clear. However, whether it be data integration or greater unification, many organizations aren't taking full advantage of their data. Data management is an ongoing issue for many companies, creating concerns like data silos, noncompliance, data loss, and poor risk management. Taking a proactive approach is crucial, so you must take all necessary steps concerning your company's goals, capabilities, budget, etc.

Unlike most resources, data doesn't depreciate in value. The same data can be used more than once regarding varying use cases. Plus, the more combinations your organization creates, the more value data offers. From real-time analytics to more accurate AI and machine learning predictions, knowing how to leverage your data can help revolutionize your business. 

Diving Deeper Into Data Mesh

Although all organizations should maximize their data's potential, the more data you create and share, the more you must consider the data risks — especially in highly regulated industries. Although many brands are now primarily focusing on a data mesh architecture, it's not the best move for all companies. JP Morgan is one brand taking this approach, aiming to align its data technology with the company's defined data products.

So, what's this concept all about?

Data mesh was first introduced in 2019 by Zhamak Dehghani and has since caught the attention of enterprises around the globe. It remains a hot industry topic, providing a new approach to data management. This concept focuses on solving the issues related to enterprise data analytics. For years, companies have invested in central data lakes and data teams, thinking they would drive their business based on data. After the initial quick wins, many noticed bottleneck issues that affected their ability to make timely data-driven decisions. 

In response, the concept of data mesh was born. As responsibility shifts from the central data team to domain-oriented teams, these teams can perform comprehensive, cross-domain data analysis to interconnect data — similar to how APIs function in a microservice architecture. 

This concept was built on fundamental principles, including:

  • Domain ownership in that each domain team is responsible for its data. For example, if you have a team that manages all things related to your podcast and the APIs when releasing podcasts, these members should be responsible for the related data, including historical data that focuses on listenership over time. Broader business domains include sales, marketing, customer service, etc. This principle ensures greater ownership to producers of a given dataset. 
  • Data as a product philosophy, understanding other business domains and data consumers can benefit from domain-specific data outside of that team. Essentially, domain data should be viewed and treated as a public API. 
  • The concept of a self-serve data platform to help teams quickly and easily consume data.
  • Federated governance ensures standardization through the entire data mesh. This data ecosystem adheres to all industry regulations or rules. 

Developing a data mesh strategy

When developing a data mesh strategy, companies need to rethink their data. A cultural shift needs to take place, treating data as a product — not just a by-product of a process. The ultimate goal is to boost collaboration, eliminate bottlenecks, and unlock the true potential of your data. 

While the concept of data mesh seems straightforward, implementing a successful strategy takes planning and foresight. Companies and their teams need to shift their mindset to understand the value of decentralized data. In doing so, organizations can solve the core challenges that come with a centralized data lake or data warehouse, including:

  • Lack of data ownership 
  • Lack of quality
  • Poor organizational scaling 

An enterprise data mesh simplifies the sharing of data products — while APIs (application programming interfaces) simplify data consumption (but more on that later). Data mesh aims to solve a primary issue — data is often isolated in a bubble, and companies struggle to optimize its value. Its full potential falls flat. This concept is a movement built off the pain points surrounding data architecture.

Discover more: Data Management and the Four Principles of Data Mesh

Building a data mesh: where to begin?

The process can be overwhelming when wondering how to implement a data mesh. However, when looking closer, data mesh is relatively simple. Data mesh overlaps with the concepts already applied to applications like security, observability, and governance — but instead, you apply those concepts to data. The core difference is how Dehghani packaged several ideas into a new paradigm — for example, applying ideas like DevOps or Domain Driven Design to data. 

Think of a data mesh as less of a technology and more of a vision within an enterprise as they begin to view data as a product — a concept that will help fix outdated, centralized processes. Since big data is still relatively new, few steps have been taken to implement better processes until now. Enterprises want to move toward a data mesh to achieve a more consistent data management and integration approach.

This transition is all part of the evolution of data-related innovation. For example, the improvements made surrounding the operational plane world. What began as monoliths have since evolved into containers and then microservices. From the stream processing capabilities of Kubernetes to the development of API management platforms, it is now easier than ever to scale when building applications. The infrastructure, tools, processes, and methodologies exist. However, most don't know where to begin concerning their data. 

Why Go Through the Trouble of a Data Mesh Strategy?

If your enterprise has leveraged data lakes and warehouses for some time, you may wonder whether it's worth the trouble to transition toward a data mesh. There is no point in making such an investment if it won't benefit your organization, so here's what to consider concerning data mesh benefits. In addition to increased interoperability and the prevention of bottlenecks, this paradigm shift in data management can also result in the following:

  • Higher levels of security — API security and authentication help ensure the connections between data products remain safe. Since this architecture frees up security teams, they can address other pressing concerns, like ransomware threats. 
  • Greater observability — With data mesh, every aspect of data is logged, providing the maximum amount of connected information about the systems. This benefit helps teams troubleshoot more quickly and efficiently. You can also enjoy clear viability across your enterprise in terms of where data is being shared. 
  • Enhanced data governance — How your data is accessed, stored, transferred, and deleted matters. Data governance is becoming increasingly complex, so you must ensure everyone adheres to diligent company policies, whether it's someone within the organization or a third-party product. 

What is API Generation?

API generation is an integral part of modern software development and data management strategies. It refers to the process of creating APIs that allow different software applications to communicate with each other. In the context of data management, particularly within a data mesh architecture, API generation plays a crucial role. Let's break it down for a clearer understanding:

1. The Basics of API Generation:

  • Definition: API generation involves automatically creating APIs from existing data sources, services, or schemas.
  • Tools and Frameworks: It often employs tools and frameworks that can read the structure of data sources or services and generate API endpoints that can be used to access, modify, or manage that data.

2. Role in Data Mesh:

  • Connecting Data Products: In a data mesh, where data is decentralized and managed as products by different domain teams, APIs serve as the connectors or bridges between these data products.
  • Standardization and Access: API generation in a data mesh environment ensures that all data products have a standardized way of being accessed, which simplifies integration and usage across different parts of an organization.

3. Benefits of API Generation:

  • Efficiency: Automatically generating APIs speeds up the development process, as it reduces the need for manual coding.
  • Consistency: It ensures consistency in API structures across various data services, which is critical for maintaining a seamless flow of data in complex systems.
  • Scalability: APIs allow for the scaling of data access and manipulation across an organization without significant changes to the backend data sources.

4. API Generation in Practice:

  • Example Tools: Tools like Swagger or OpenAPI can automatically generate documentation and API endpoints from existing codebases.
  • Customization: While APIs can be generated automatically, they often require some level of customization to ensure they meet specific business requirements and adhere to security protocols.

API generation is a pivotal aspect of modern data architectures like data mesh.

Why API Generation Matters for Data Mesh

Since data mesh is essentially a network of data products, every data source is a separate data product — for example, third-party apps or SaaS. Typically, the easiest way to connect these data products is to generate APIs. Once implemented, APIs can be a game-changer.

APIs make it easier for companies to find, analyze, and govern data. The same applies to JSON Schemas.

Think of APIs as the vehicles that allow enterprises to access a data mesh to connect with data products. They enable data mesh principles in the following ways:

  • Provide boundaries for data mesh capabilities 
  • Allow for self-serve consumption via any user, application, or developer
  • Decentralizing ownership while simplifying data consumption via data product consumers
  • API endpoints offer entry points for greater data mesh accessibility 
  • Promotes federated data governance  

APIs reduce the complexities associated with data mesh principles while offering a more straightforward interface to data. As connectivity increases, organizations save time and effort. So, APIs are a crucial component of the data mesh ecosystem. They allow businesses to access the data sources they need and can be used to connect to specific datasets within the data product.

Get started with DreamFactory

Taking steps toward a data mesh strategy can help you create greater business value by optimizing analytical data throughout the entire data lifecycle. If you're seeking a low-code solution for developing and managing APIs, DreamFactory can help. These components are key when developing any data mesh framework and will help accelerate your data strategy. Ready to take the next steps and benefit from API automation? Start your free 14-day trial today!

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