A new approach to building the modern Data Platform
The world of data is exponentially growing, making the need for an efficient way to manage and analyze it more critical than ever. Managers live in a time of rapid data evolution that requires speed, flexibility, and security: how do we address these fundamental areas to keep our business up-to-date?
Here is when the concept of data platform comes into play as the new frontier of data acquisition, storage, and management. It allows users to ingest, transform, and extract value from data so that companies can leverage data platform information to their benefit and obtain actionable insights.
In this article, we’ll discuss what data platforms are, how they work, and how to get started with them.
Defining Data Platforms
A data platform integrates tools that enable organizations to manage data from ingestion to exposure. Thanks to the recent shift from data warehouses to data lakes, new demands of data consumers had to be addressed.
This change in demand was due to multiple aspects, such as the increase in unstructured and semi-structured data, the popularity of microservice architectures, and the need to achieve variety, volume, velocity, veracity, and value requirements of data management.
Traditionally, a data center is the physical infrastructure where the data is stored and managed. However, a data platform goes beyond this physical aspect as it acquires, stores, prepares, and distributes business data, guaranteeing safe governance policies at any user level. In other words, a data platform not only stores data but also provides a comprehensive suite of services that manage the lifecycle of data, ensuring it is always ready to use.
Following this definition, one main benefit is that these platforms maximize resources and reduce the underutilization of technologies by enabling the sharing of assets among users; the added value lies in the possibility of sharing data and information between and within companies.
The importance of integration: Platform Engineering ++
The concept of a data platform today is more comprehensive than ever. While platform engineering has become the complete IT foundation of data platforms, the essence of our data platform lies in its ability to manage and optimize data through processes such as ingestion, transformation, and exposure. As a result, businesses can translate raw data into actionable insights.
Platform Engineering ++ promises to offer businesses self-service capabilities for automation in software development teams in the cloud-native era, with a more efficient workflow and infrastructure. While the definition of platform engineering may still seem ambiguous, the supposedly blurry boundaries of this framework are due to the need to encompass a broader meaning.
This includes integrating Infrastructures and DevOps, marking a natural, more defined evolution of their roles. Still, platform engineering also supports product teams for data, machine learning, artificial intelligence, orchestration, API, events, and frontends. In this broader sense, Giulio Roggero, CTO and Co-Founder of Mia-Platform, calls it Platform Engineering++.
In this context, Internal Developer Platforms (IDPs) help with the management and use of data by providing tools that reduce complexity and speed up the data handling process.
The core components of Data Platforms
In general, the core components of a data platform can be broken down into layers:
- Data warehouse and lakes: where data is stored and managed;
- Ingestion layer: it brings in the data;
- Transformation tools: they refine the data and clean it;
- Integration: data from various sources is integrated into a unified platform, ensuring all incoming data is collected in a standardized form to enhance data consistency and reliability;
- Processing: once integrated, the data undergoes processing to aggregate information and create enriched entities tailored for specific purposes;
- Management: after integration and processing, the platform then manages the refined data. This includes maintaining its integrity, ensuring it remains up to date, and enforcing governance policies that protect and control access to the data.
- User access layer: where intelligence tools, product analytics, and more tools are used for decision support, experimentation, and customer management.
Therefore, the core of a data platform lies in its layered architecture that focuses on data storage, processing, and analysis, ensuring that data can flow seamlessly and securely throughout the organization.
More specifically, an efficient data platform has a modern data architecture. It comprises end-to-end data integration and management, including an IDP dedicated to business insights. This IDP contains microservices and functionalities tied to the company’s core business. Additionally, a data platform features a marketplace or digital products layer, with APIs allowing external channel integration. This capability is not just a technical feature: it enhances the platform’s utility. In fact, by facilitating the connection of external applications and data sources, the platform ensures data across systems is cleaner and more consistent. Such integration fundamentally improves data management and usability.
Between the IDP and the Data Fabric, it is also possible to have a semantic layer that functions as a single source of truth to harmonize and orchestrate data and provide metadata management.
Infrastructure considerations
As shown in the above figure, our data platform bridges the gap between data acquisition and business intelligence, enhancing scalability in handling data streams. This way, users can interact with companies and their systems, such as CRMs and e-commerce. As such, a more modern data stack can reduce the workload on systems, make scalability possible along with the flexibility of offered services, and reduce the necessary time to value.
In the “platform-as-a-product” era, platforms include tools to help developers and business users become self-sufficient. They can use starter kits or predefined reusable blocks to automate models and procedures, assist in joint and repetitive activities, suggest feedback on problems or security risks, and simplify operations through infrastructure.
There are different steps, from data ingestion to actionable outputs, which are handled by Mia-Platform Fast Data:
- Ingest: data enters the platform through connectors and collectors responsible for gathering raw information from various sources.
- Clean, Validate, Persist: once ingested, data undergoes cleaning and validation to ensure it is standardized, accurate, and reliable; for example, it is transformed and stored into a format suitable for analysis.
- Transform and Aggregate: the processed data is aggregated to facilitate access and analysis in an effective way.
- Expose, Product and Security: finally, data is made accessible to end users through APIs. In this step, data products are created along with giving priority to ensuring the security of the data throughout its lifecycle. As a result, insights can be integrated into decision-making processes.
The benefits of Data Platforms
In modern and structured data platforms, information can be accessed from the cloud and non-cloud providers, benefiting from a broader vision without processing delays and with the advantage of keeping track of access.
As data platforms often include dashboards, reports, and feedback messages, business decisions are immediately made more effective and valuable, enabling easier data sharing. Most importantly, adopting a data platform represents a data revolution against outdated and isolated approaches to handling data, which often lead to increased security risks.
A critical feature of the data platform is that it solves data quality, trust, and reliability problems. This is thanks to a layered architecture that, from ingestion to exposure, has specific modules for data cleaning, transformation and security to facilitate in-depth analysis. Indeed, striving for data quality is crucial in today’s world, where applications’ good performance relies on the data on which tests can be run. Ultimately, although data quality management can be done semi-manually, automation is the only way to scale, ensure efficient data governance and security, and cover the breadth and depth of testing.
This is why observability is a keyword in the context of a data platform. Observability ranges from the point of data ingestion to the end of reporting analytics while allowing rapid scaling of tasks from testing to automation and monitoring across the entire ecosystem.
In this regard, the platform engineer and team are focused on creating workflows and automating custom-made logic for companies. In this way, they can create the so-called “golden paths” and, through a more efficient infrastructure, contribute to reducing the developers’ daily cognitive load and raising their productivity.
Getting started with Data Platforms
Assessing organizational needs and goals
In the broader context of Platform Engineering++, data platforms allow a functional data governance structure. Such a structure is optimal to build, for instance, through applicative cloud-native architectures and web-scale applications for new data-driven customer experiences. To harness the full potential of data platforms, begin by assessing how the platform will manage, process, and expose data to meet your organizational goals and needs.
This structure also answers the specific need for developing applications to improve speed and versatility while maintaining reasonable costs. It is essential to clearly define the business goals with a data platform solution, which can range from creating a personalized customer experience within a customer data platform to understanding the client’s journey.
To guide the company towards achieving a shared and ambitious objective and growth, it is also important to understand the roles of the current teams, especially the developer teams, and define them.
Selecting the proper management tools
Once the organization’s business requirements are understood, other aspects must be considered before selecting the proper management and analytics tools. The first one is whether the prospective platform autonomously allows the unification of the data flow.
Remember what clients the organization is catering to: users can be divided into groups with different needs and expectations, which allows them to offer a personalized experience.
Other aspects to consider are whether artificial intelligence-based tools, user-friendliness, real-time analytics and real-time data needs, integration with other systems and frameworks, and data security are supported.
These features are present in Mia-Platform Fast Data, which integrates essential functions to enhance application development speeds. It collaborates with partners such as Amazon Web Services and shows how to have a more efficient process, such as automating tier creation for customers from prospect to Gold level.
To find out how to be part of the leading platform builder and achieve your business goals, check out this video demo of Mia-Platform Fast Data.
Wrapping up
Data platforms are dynamic ecosystems that drive business intelligence by integrating data from various sources. The primary advantages include data consistency, accessibility, and security, along with reducing developers’ cognitive load. Mia-Platform Fast Data can be a breakthrough in your application development process by offering innovative solutions that accelerate your development speeds and optimize your business processes.
In fact, Platform Engineering ++ is a foundational, more comprehensive approach to Data Platforms that enables flexibility and scalability while improving automation and reducing developers’ costs and cognitive loads. While Platform Engineering ++ provides the infrastructure, the true power of data platforms lies in their ability to transform raw data into insights and actions, enhancing business agility and intelligence.
This approach is more efficient in prioritizing infrastructure efficiency while keeping data quality and security. All data platforms may share the same general structure but don’t share the same tools or provide the same functionalities.
In this regard, before choosing the right data platform for your organization, it is important to understand the company’s needs and set clear objectives to guide you through the desired functionalities. These steps are crucial for identifying the solution that meets the company’s unique requirements and all business objectives.
