In today’s digital age, organizations are challenged to keep up with the unprecedented pace of data generation and the plethora of enterprise systems and digital technologies that collect all types of data. This is coupled with the need to rapidly and efficiently analyze these large volumes of data to generate insights and intelligence in order to maximize their business value. As a result, big data platforms have become an essential foundation for organizations to efficiently deploy data solutions that provide timely data-driven business decisions and competitive advantage.

“Data analytics and intelligence solutions are proliferating across organizations to enable business growth. Organizations should build big data platforms as solid foundations to deploy data solutions at scale. These data platforms should be purpose-built for business since they are only as good as the business insights and intelligence they enable; and they should be built to be future-proof, benefiting from the constant advances in data infrastructure services and technologies.”
Oussama Ahmad, Data Consulting Partner at Artefact

Key Objectives of the Big Data Platform

Big data platforms aim to break down data silos and integrate the different types of data sources required to implement advanced data analytics and intelligence solutions. They provide a scalable and flexible infrastructure for collecting, storing, and analyzing large volumes of data from multiple sources. These platforms should leverage best-in-class data management services and technologies and fulfill three key objectives:

  • Centralize data sources: A big data platform should break down data silos by automatically ingesting and storing different types and sizes of data sources from enterprise data systems and third-party data sources. It should become the central data repository, providing a single source of truth for all data sources required for data analytics solutions.

  • Enable data analytics solutions: A big data platform should provide a robust infrastructure for developing, running, and deploying different types of analytics solutions (from simple reporting to advanced machine learning) as needed to meet business needs for intelligence and insights for decision making.

  • Ensure compliant and secure access to data and applications: With a big data platform, organizations should be able to provide consolidated, secure data access to both internal and external stakeholders. It should also store, process, and distribute data in a manner that complies with local data laws and regulations, as well as international standards and best practices.

Infrastructure of the Big Data Platform

There are several infrastructure options for a big data platform: fully on-premise, fully cloud or hybrid cloud/on-premise, each with its own advantages and challenges. Organizations should consider a number of factors when choosing the most appropriate infrastructure option for their big data platform, including data security and residency requirements, data source integrations, functionality and scalability requirements, and cost and time. A fully cloud-based architecture offers lower and more predictable costs, out-of-the-box services and integrations, and rapid scalability, but lacks control over hardware and may not comply with data privacy and residency regulations. A fully on-premise architecture provides full control over hardware and data security, typically complies with privacy and residency regulations, but incurs higher costs and requires long-term planning for scaling. A hybrid cloud/on-premise architecture offers the best of both worlds, facilitating full migration to the cloud at a later date, but may require a more complex setup.

Many organizations choose a hybrid infrastructure for their big data platforms due to organizational requirements to keep highly sensitive data (such as customer and financial data) on their own servers, or due to the lack of government-certified cloud service providers (CSPs) that meet local data privacy and residency requirements. These organizations also prefer to keep cloud-native or non-sensitive data sources in the cloud to optimize storage and compute resource costs and take advantage of out-of-the-box data analytics and machine learning services available from CSPs. Other organizations that have no organizational or regulatory requirements for data residency within the company or country opt for fully cloud-based infrastructure for faster time to implement, optimized costs, and easily scalable resources.

Figure 1: Hybrid Cloud & On-Premise Data Platform Infrastructure

A big data platform typically involves setting up seven main layers that reflect the data lifecycle from “raw data” to “information” to “insights”. Organizations should carefully consider the appropriate services and tools required for each of the layers to ensure seamless dataflow and efficient generation of data insights. These services and tools should perform key functions in each layer of the big data platform as shown in Figure 2: Big Data Platform Data Layers.

Figure 2: Big Data Platform Data Layers

Evolution of the Big Data Platform

The development of a big data platform should evolve through several stages, starting with a minimum viable platform (MVP) and continuing with incremental upgrades. An organization should synchronize the evolution of its big data platform with increased requirements for broader and faster data insights and intelligence for business decisions. These increased requirements affect the complexity of the big data platform in terms of data analytics solutions, data source volumes and types, and internal and external users. The evolution of the big data platform includes the addition of more storage and compute resources, advanced features and functionality, and improvements in platform security and management.

Exhibit 3: Big Data Platform Evolution

“We have seen that many organizations tend to build big data platforms with advanced and unnecessary features from day one, which increases the technology cost of ownership. Big data platform deployment should start with a minimum viable platform and evolve based on business and technology requirements. In the early stages of building the platform, organizations should implement a robust data governance and management layer that ensures data quality, privacy, security, and compliance with local and regional data laws.”
 Anthony Cassab, Data Consulting Director at Artefact

Guidelines for a Future-Proof Big Data Platform

A big data platform should be built according to key architectural guidelines to ensure that it is future-proof, allowing for easy scalability of resources, portability across different on-premise and cloud infrastructures, upgrade and replacement of services, and expansion of data collection and sharing mechanisms.

  • Modular data layers: All platform layers should be well defined and integrated, from the data ingestion layer to the data visualization and BI layer. Each layer should leverage best-in-class services or tools, which typically requires that the architecture doesn’t rely on a “black box” solution and allows for the configuration and integration of standalone tools and services that provide specific functionality.

  • Containerized applications: The platform should containerize data ingestion, processing, and analysis procedures and applications using orchestration platforms like Kubernetes. Containers offer a logical packaging mechanism in which applications can be abstracted from the runtime environment allowing run of containerized workloads on various types of infrastructure. This facilitates portability of platform applications across different on-premise and cloud infrastructures and deployment across multiple clouds.

  • Microservices-based architecture: Platform applications should be broken down into microservices, each serving a specific function and interacting with each other. This facilitates build and maintenance of applications, allows independent deployment and scaling of microservices, and enables rapid and frequent delivery of large complex applications.

  • Standard services and tools: The selection of tools and services for the platform should focus on shared industry standards (open standards) and reduce reliance on those that are specific to any single technology vendor. For example, the platform should include cloud services that are common to multiple cloud service providers. This facilitates migration across different on-premise and cloud infrastructures and multi-cloud deployments, reducing cost and time.

  • Robust data governance: From the outset, the platform should incorporate a robust data governance framework in the form of governance tools, services, processes, controls, and rules that ensure continued monitoring and improvement of data quality, secure access to data and data analytics, privacy protection, compliant storage and processing, and standardized data and metadata management. This facilitates the scaling of platform resources and capabilities, and facilitates the broad adoption of data analytics solutions and the use of available datasets.

“An adaptable and modular platform that can scale as business needs evolve is preferable to a “black box” platform that is well integrated but allows limited customization. These platform architectures can be built fully or partially in the cloud to leverage the benefits of cloud computing, such as scalability and cost efficiency, while also meeting the privacy and security requirements of data protection regulations.”
Faisal Najmuddin, Data Engineering Manager at Artefact

In summary, a big data platform brings multiple benefits to organizations, such as centralizing data sources, enabling advanced data analytics solutions, and providing enterprise-wide access to data analytics solutions and sources. However, implementing a big data platform entails a number of strategic decisions, such as choosing the right infrastructure(s), adopting a future-proof architecture, selecting standard and “migratable” services, carefully considering data protection regulations, and finally, defining an optimal evolution plan that’s closely linked to business requirements and maximizes return on data investment.

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