Modern Data Platforms: Tips, Tools & Best Practices
Wondering about modern data platforms? We explain what a modern data platform is, how to build one, and best practices for data-intensive applications.
What is a modern data platform?
A data platform is the set of components that collectively meet all of an organization’s data needs, including acquisition, storage, preparation, and analysis. A modern data platform is designed proactively with scalability and flexibility in mind, anticipating complex data needs.
What Is Modern Data and Why Should My Business Care About It?
We’ve all heard the term “data-driven business” and often take for granted that data doesn’t just appear for business use. Even the smallest businesses will use some form of data. Still, they often do so after collecting it in simple, secure databases with information collected through sales or user portals.
However, modern data architecture calls for strategic collection, storage, and management solutions to get the most from that information when it comes to enterprise businesses. As the volume of information businesses can collect increases exponentially, technologies like Hadoop have attempted to fill the gap with semi-controllable data storage, particularly unstructured data.
Hadoop, and similar technologies, are referred to as data lakes. While these technologies have done much to support increasing data needs, they have also fallen short in some critical areas:
- Scalability: Scaling data lake clusters has been a notorious challenge when resources are needed quickly—which, unfortunately, is most of the time.
- Integration: Adoption of new technologies and software can make using data lakes difficult, especially when that technology calls for new stacks that might not play well with data lakes.
- Hardware: Rapidly shifting workloads can stress hardware and resources, especially when an organization can’t provision hardware fast enough to meet demand.
Therefore, to address modern challenges of scalability and responsiveness, data scientists and businesses have developed agile alternatives that have been called modern data platforms. This agility and flexibility come from the combination of two key attributes:
- Availability: Data should always be readily available to users for consumption, analysis, and computing. Availability goes far beyond just direct, end-user interactions through data portals (although this is important). It includes readily available information for advanced analytics and high-performance workloads.
- Elasticity: Modern data platforms must be able to scale up and down as needed and with a speed that responds to user demands rather than hardware or software limitations.
What Are the Components of a Modern Data Platform?
Modern data platforms are platforms comprising several interconnected systems and processes. The interactions between these systems should facilitate a few critical benefits:
- Optimal resource allocation and provisioning for scaling and accessibility.
- Design choices integrate cybersecurity, compliance, audit logging, data governance, and disaster recovery.
- Fine-grained control over technology.
With that in mind, modern data platforms include the following components:
- Data Lakes: Data lakes are critical components for the storage and retrieval of unstructured data—but they aren’t a solution in and of themselves. They can, however, serve as important parts of a modern data platform.
- Batch Data Ingestion and Processing: It’s critical that information be pulled into the system in large quantities, processed for use, and injected into data flows or long-term storage depending on the need. This aspect is critical to support big data analytics by applying domain models and unstructured data processing.
- AI or Machine Learning Workspaces: Many modern data platforms now include machine learning and AI capabilities to support multiple goals: developing more powerful AI systems and then using those systems to optimize the platform itself.
- Real-Time and Long-Term Databases: Many modern data platforms can separate data flows needed for real-time processing and analysis and those needed for long-term storage. In both cases, the focus will be on making data readily available for users.What gives these systems flexibility is that storage isn’t limited to one kind of approach. A modern data platform can, with the right configuration, support on-premise private cloud systems, public cloud systems, or hybrid environments.
- Analysis and Reporting: Data, cleaned, processed and structured can be funneled into tabular models or machine-based data services for analytics. Users can engage this data through customized dashboards and visualization and other tools.
What Are the Challenges of Developing a Modern Data Platform?
Modern data platforms are hard work to implement for multiple stakeholders in an organization.
Some of the challenges of implementing modern data platforms include the following:
- Cultivating the Right Culture: Business users may not understand the value of completely migrating into a new data platform, especially if it disrupts established (if inefficient) workflows and practices. IT, security, and data science leadership must make a case for adopting a modern data platform.
- Compliance and Cybersecurity: Moving to a modern data platform doesn’t excuse your organization from its security and compliance obligations. The platform must be able to protect data, privacy, and availability under any regulations your business operates under—and in some cases, a modern data platform can introduce unforeseen issues.
- Migrating from Legacy Systems: Your on-premise or legacy cloud infrastructure may resist rapid migration or integration with a new platform. If this is the case, the technical leadership in your organization must strategize with experts to develop a migration plan.
WEKA Data Platform for AI
WEKA’s data platform addresses the storage challenges posed by today’s enterprise AI workloads and other high performance applications running on-premises, in the cloud or bursting between platforms. With WEKA, you accelerate every stage of the data pipeline from data ingestion to cleansing to modeled results, improving training times and reducing time to market.
Contact us to learn more about WEKA and how it can serve as the foundation of your modern data platform.
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