What is a Data Platform?

A short history of the data platform

The concept of a data platform has evolved significantly over the years, tracing its origins back to the early days of digital computing. In the beginning, data management was a rudimentary process, often confined to simple databases and basic file storage systems. As businesses grew and technology advanced, the 1980s and 1990s saw the emergence of more sophisticated database management systems (DBMS), which laid the foundation for what we now recognize as early data platforms. These systems were primarily focused on structured data, stored in tabular form, and were used mainly for transaction processing and traditional business intelligence tasks.

With the advent of the internet and e-commerce in the late 1990s and early 2000s, the volume, velocity, and variety of data began to explode, leading to the concept of "Big Data." This era marked a significant shift in data platform technologies, with a newfound emphasis on scalability and the ability to handle unstructured data, such as text, images, and video. Technologies like Hadoop and NoSQL databases emerged during this period, challenging the dominance of traditional relational database systems and paving the way for modern data platforms.

Today, a data platform encompasses a suite of technologies that collectively address an organization's comprehensive data requirements. It facilitates the acquisition, storage, management, and governance of data, supporting user and application security. Understanding a data management platform's intricacies can be challenging. Let's delve into what constitutes a data platform, how it's designed, and differentiate between various types such as customer data platforms, big data platforms, and operational data platforms.

Data platform, defined

A data platform is an integrated set of technologies that collectively meet an organization's end-to-end data needs. It enables the acquisition, storage, preparation, delivery, and governance of your data, as well as a security layer for users and applications. A data platform is key to unlocking the value of your data. But data platforms can be complex. What exactly is behind a data platform? How do you approach designing one? And what's the difference between a customer data platform, a big data platform, and an operational data platform?

Table of contents

• Advantages of data platforms
• Data platforms vs big data platforms
• Modern data platforms: an in-depth exploration
• Building a data platform: a strategic approach
• Operational data platforms
• Conclusion
• FAQs

Advantages of data platforms

Over the last 20 years, IT vendors have been trying to develop and offer solutions to address the flood of data that companies face from both inside and outside the business.

Cloud is the new norm, and cloud-native data warehouses are now massively parallel-processed. Data pipelines can handle terabytes of data. Storage has become cheap and fast, and data processing frameworks like Spark can handle large volumes of data. NoSQL augments relational databases. And AI/ML applications have proliferated everywhere.

Although many technologies have matured, most enterprises have been unable to integrate advanced enterprise tools. The result is data silos that are often unscalable, contain duplicate and often out-of-date data, are locked into proprietary solutions, and lack a single security layer.

A modern data platform tries to solve this problem. It's a combination of interoperable, scalable, and replaceable technologies working together to deliver an enterprise's overall data needs.

Data platforms vs big data platforms: a detailed comparison

Understanding the nuances between data platforms and big data platforms is crucial for organizations looking to optimize their data management strategies. While the two share some commonalities, they are distinct in their focus, capabilities, and use cases. Here's a more detailed breakdown:

Enterprise data platforms (EDPs)

Traditional data handling

EDPs are often rooted in traditional data sources and methodologies. They primarily exist in on-premise or hybrid environments and are built around established data management systems. These platforms are designed to handle structured data and are typically used for operational databases, data warehousing, and data lakes. EDPs include a suite of tools and processes tailored for data acquisition, preparation, and analytical reporting.

Focused on centralized access

A key feature of EDPs is their emphasis on centralized access to data assets within an organization. This centralization enables controlled and standardized data management practices, ensuring data consistency and reliability across various business functions.

Modern data platforms

Evolution of data management

Modern data platforms represent an evolutionary step from traditional EDPs. They extend the capabilities of EDPs by incorporating more flexible and future-proof technologies. This evolution is driven by the need to accommodate a wider variety of data types and larger volumes of data.

Handling diverse data and workloads

Modern data platforms are particularly adept at processing both streaming and batch data. They can manage structured, semi-structured, and unstructured data, facilitating the development of AI/ML applications and complex operations like natural language processing (NLP). These platforms often leverage cloud technologies to offer cost-effective, scalable, and flexible managed services.

Cloud data platforms

Fully cloud-based solutions

Cloud data platforms are entirely built on cloud computing technologies. They offer comprehensive solutions that integrate various cloud-based data stores and processing tools. This integration includes object storage, managed relational and NoSQL databases, and data warehouses.

Versatility and scalability

These platforms are known for their virtually unlimited storage capabilities, scalability, and ability to handle diverse workloads. They are particularly advantageous for businesses looking to harness the full power of cloud computing for their data management needs.

Big data platforms

Specialized in data analytics

Big data platforms, or big data analytics platforms, are specialized data platforms focused on analytics. They are engineered to run complex queries on large volumes of data, regardless of its form. These platforms combine several big data tools and utilities, providing scalability, availability, security, and performance optimization.

Beyond traditional SQL queries

Big data platforms excel in areas beyond traditional SQL queries on structured data. They are often part of a cloud suite or a SaaS solution, offered as data as a service (DaaS). These platforms are commonly used in conjunction with operational data from enterprise, modern, or customer data platforms.

Customer data platforms (CDP)

A CDP focuses solely on customer-related data. It brings together customer data from multiple sources, such as CRM, transactional systems, social media, emails, websites, digital ads, and e-commerce stores. The aggregated data builds a complete user profile that can be used for marketing and other business purposes, like behavior segmentation. Although traditional CRMs often talk about providing a 360-degree customer view, unlike a CRM, a CDP can aggregate both known and anonymous customer data from multiple sources.

Modern data architecture: an in-depth exploration

Modern data architecture (MDA) is a foundational aspect of contemporary data platforms, providing a blueprint for how data is managed and utilized in an organization. MDA has evolved to address the complexities and demands of modern data ecosystems, characterized by vast amounts of diverse data types and the need for flexible, scalable solutions. Here, we delve deeper into the key components of an MDA.

User-centric design

Empowering end-users

At the forefront of MDA is the empowerment of end-users. This paradigm shift allows users to not just consume but also contribute to the data ecosystem. They can import their datasets, create customized data pipelines, and generate insights, fostering a culture of data-driven decision-making and innovation.

Customization and flexibility

User-centric design in MDA provides the flexibility for users to tailor data solutions to their specific needs. This includes custom analytics, reporting, and the ability to integrate with various data sources, enhancing overall user engagement and productivity.

Hybrid cloud integration

Balancing on-premise and cloud benefits

MDA leverages the combined strength of on-prem systems with the scalability and innovation of cloud technologies. This blend offers organizations the ability to maintain control over sensitive data while leveraging cloud-based tools for enhanced processing capabilities and cost-effectiveness.

Elasticity and scalability

The hybrid model in MDA provides elasticity in data storage and processing, allowing organizations to scale resources up or down based on demand, thus optimizing costs and performance.

Virtual data storage layer

Unified data access At the core of a modern data platform is the virtual data storage layer that can handle diverse data formats and workloads. For example, the platform can support different data storage formats for the operational/transactional databases supporting real-time interactions, the data lakes containing unstructured data, and the data warehouses needed for the structured datasets required for known analytics jobs.

Federated data management

The storage layer is therefore more of an “abstraction” over other platform components. At a low level, users and applications will access it using a common set of protocols and standards, like REST APIs. In MongoDB, our federated queries are using the MongoDB query API. From a usage perspective, this data will be transparently federated and virtualized, allowing users to share and collaborate on it.

Scalable data integration

Adaptable data ingestion

MDA prioritizes scalable solutions for integrating data from a wide array of sources. This includes tools and methodologies for batch processing, real-time streaming, and event-driven data flows, ensuring that the architecture can adapt to varying data volumes and velocities.

Integration with legacy systems

Scalable integration also involves the ability to connect with legacy systems, allowing organizations to leverage their existing data assets while transitioning to more modern data practices.

Extensible processing logic

Modular application development

MDA encourages a modular approach to application development. This facilitates the creation of reusable, domain-specific applications that can be easily integrated or updated, enhancing operational efficiency and agility.

Incorporating advanced technologies

The pluggable architecture supports the inclusion of cutting-edge technologies like AI, machine learning, and advanced analytics. This enables organizations to stay at the forefront of technological advancements and derive deeper insights from their data.

End-to-End data governance

Robust data management

Data governance within MDA involves stringent management of data access, quality, and compliance. Automated tagging and classification streamline data discovery and usage, ensuring that data remains reliable and trustworthy.

Regulatory compliance and security

MDA places a strong emphasis on adhering to regulatory standards and securing sensitive data. This encompasses everything from data privacy laws to industry-specific regulations, ensuring comprehensive data protection.

Self-service analytics

Democratizing data analysis

Self-service analytics are a hallmark of MDA, allowing users across the organization to access, analyze, and visualize data without specialized technical skills. This empowers a wider range of employees to derive insights and make data-driven decisions.

Diverse analytical tools

The modern data platform architecture supports a variety of analytics tools and platforms, from BI dashboards to complex data modeling software. This diversity caters to different user needs and analytical requirements within the organization.

Automation

Streamlining operations

Automation in MDA covers both infrastructure management and data operations. It simplifies the deployment, maintenance, and scaling of data platforms, reducing the manual effort and potential for errors.

Efficient data processing

Automated data pipelines and processes accelerate data processing and analysis, enabling organizations to respond more quickly to market changes and business opportunities.

Unified security layer

Consolidated access control

A unified security layer is integral to MDA, providing a single point of control for data access and permissions. This simplifies the management of user privileges and enhances overall data security.

Compliance and standardization

The security layer ensures data handling practices comply with relevant standards and regulations, providing a consistent approach to data security across the organization.

Building a data platform: a strategic approach

Constructing a modern data platform is a multifaceted endeavor that requires careful planning, strategic decision-making, and a deep understanding of both technology and business needs. This process involves several key steps, each contributing to the creation of a robust, efficient, and scalable data platform.

Engaging subject matter experts (SMEs)

Assembling a diverse team

The first step in building a data platform is to assemble a team of experts. This team should be a blend of technical and non-technical members, including data architects, engineers, business analysts, and end-users. Including diverse perspectives ensures that the platform caters to a wide range of requirements and leverages domain-specific knowledge.

Leveraging external expertise

In many cases, it can be beneficial to include external consultants or industry experts. They can provide insights into emerging trends, best practices, and innovative solutions that might not be present internally.

Focusing on people and processes

Understanding user needs

A successful data platform is one that is built with the end-user in mind. It’s crucial to understand how different teams and individuals will interact with the platform, what their specific needs are, and how these can be best addressed.

Optimizing business processes Examining and understanding current business processes is vital. The data platform should be designed to enhance these processes, improve efficiency, and provide opportunities for new capabilities to be developed.

Gathering business requirements

Defining use cases and personas

A clear understanding of business requirements is critical. This includes defining user personas, use cases, data sources, security requirements, and existing applications. These requirements should be detailed and prioritized to guide the development process.

Aligning with business goals

The platform should align with the broader business objectives and goals. Whether it’s driving innovation, enhancing customer experience, or improving operational efficiency, the platform should be a tool that helps achieve these goals.

Building incrementally

Adopting an agile approach

Building a data platform should not be a one-off, monolithic project. Instead, an agile, incremental approach is recommended. This allows for regular feedback, continuous improvement, and the ability to adapt to changing business needs.

Phased rollouts

Implementing the platform in phases allows for manageable chunks of work and reduces the risks associated with large-scale deployments. Each phase can focus on specific aspects of the platform or functionality, ensuring thorough testing and integration.

Leveraging existing assets

Utilizing current data and workflows

A new data platform should build upon and enhance existing data assets and workflows. This includes leveraging current data sources, integrating with existing applications, and utilizing established data management practices.

Balancing innovation with practicality

While it’s important to innovate, it’s equally crucial to be practical. The platform should not be a complete overhaul but rather an evolution that brings tangible improvements and benefits.

Emphasizing data quality and governance

Ensuring data integrity

A core component of a data platform is the mechanisms put in place to ensure data quality. This includes processes for data validation, cleansing, and standardization.

Robust governance framework

Implementing a strong data governance framework is essential. It should cover aspects like data access control, compliance with regulations, and data privacy standards.

Planning for scalability and flexibility

Future-proofing the platform

The data platform should be designed with scalability in mind, able to handle increasing volumes of data and evolving user demands. This includes considering cloud-based solutions, modular architectures, and technologies that can scale as needed.

Flexibility for adaptation

Flexibility is key in a data platform. It should be capable of integrating new data sources, adapting to new business requirements, and accommodating emerging technologies.

Operational data platforms

The data platform types we've talked about so far primarily deal with aggregating data from different sources and using that aggregated data to answer business analytics questions.

Another type of data platform deals with operational, high-volume data used for developing applications. These “operational” and application data platforms are increasingly cloud-hosted for scalability and ease of use, have built-in high availability and disaster recovery, offer strong data security at rest and in transit, and allow workload isolation, performance monitoring, and alerting.

One such platform is MongoDB Atlas. Atlas is a database as a service (DBaaS) from MongoDB that allows organizations to spin up MongoDB clusters in the cloud — without worrying about provisioning infrastructure, patching, scaling, performance monitoring, high availability, security, backups, disaster recovery, and database administration.

In addition, most SQL-based BI tools can connect to Atlas and analyze its data.

Conclusion

Data platforms are instrumental in unlocking the full potential of an organization's data. They serve as the foundation for understanding, governing, and effectively accessing the vast repositories of information that modern businesses accumulate. The choice of data platform significantly influences how an organization leverages its data assets.

When considering what you want to achieve with your data, it's essential to align your objectives with the capabilities of the chosen data platform. For instance, if your goal is to gain deep insights into customer behavior and preferences, a customer data platform (CDP) could be the ideal solution. CDPs are designed to consolidate and integrate customer data from various sources, providing a comprehensive view of the customer journey.

On the other hand, if dealing with large volumes of complex, unstructured, or semi-structured data is your primary concern, a big data platform may be more appropriate. These platforms are engineered to handle the “three Vs” of big data — volume, velocity, and variety — making them suitable for tasks like data mining, predictive modeling, and real-time analytics.

For organizations seeking a more operational focus, platforms like MongoDB Atlas offer a robust solution. These operational data platforms are tailored for high availability, scalability, and real-time performance, crucial for day-to-day business operations. MongoDB Atlas, for example, provides a cloud-based, fully-managed database service that simplifies the complexities of data management, allowing businesses to focus on innovation and application development rather than on database administration.

Ultimately, the power of data platforms lies in their ability to transform raw data into actionable insights and operational excellence. By choosing the right platform, organizations can not only unlock hidden potential and revenue in their data but also gain a competitive edge in today's data-driven business landscape. The decision on which data platform to use should, therefore, be driven by the specific data needs and strategic objectives of the organization, ensuring that the chosen solution aligns with its overall vision and goals.