In today's digital age, the information explosion has radically transformed the way organisations manage and use their data. In this context, modern cloud data architectures have emerged as a cornerstone for efficiency, innovation and business success. These platforms offer an agile and powerful ecosystem that goes beyond simply being a place to store data, becoming catalysts for data-driven decisions and process optimisation.
As companies seek to remain competitive in a dynamic business environment, the adoption of a cloud data platform is a crucial element. In addition to providing flexibility and scalability, these platforms stand out for their ability to address critical aspects of data management, such as data governance and data quality. The implementation of robust governance frameworks ensures that data is managed in an ethical, secure and compliant manner, while built-in data quality assurance mechanisms enhance the reliability of information used for decision-making.
A modern cloud data architecture stands out for its security, robustness, ease of management and support for a variety of user types and workloads. Rather than focusing on one data platform, data architectures being developed in cloud environments prioritise achieving optimal versatility, flexibility and scalability through the use of data platforms such as cloud data lake or cloud data lakehouse.
One of the key objectives of modern data architectures is to facilitate data sharing between authorised users without requiring database administrators to replicate or create new data silos, while maintaining centralised data security and data governance policies. In addition, they enable adaptation to new design patterns, such as data meshing.
When we talk about modern data architectures in the cloud, we are not referring to a single typology, as there are many types of modern data architectures and the selection of the most appropriate one will depend on the capabilities, requirements and needs of each corporation.
Below we explore some of the most salient benefits for enterprises in adopting a modern cloud data architecture.
Modern cloud data architectures play a crucial role in enabling advanced data analytics, providing a number of features and capabilities that enhance this process.
According to the report "The State of Data Science 2020: Moving from Hype Toward Maturity", data scientists spend approximately 45% of their time preparing data before they can use it to develop machine learning (ML) models and visualise results in a meaningful way.
In this context, modern data architectures fulfil three crucial attributes that facilitate data science processes and analytical tasks:
The complications associated with data management find an effective solution in the form of a cloud data architecture, which establishes an organic structure for various types of data. Beyond simply storing raw data, as is characteristic of a conventional data lake, such architectures do not only allow for storage, but also facilitate the management of metadata that enables data scientists to conduct meaningful analyses.
The vital core of a modern cloud data platform lies in its services layer. This layer stands as the epicentre that manages metadata, transactions and other essential operations. It executes these functions both locally and globally, spanning multiple regions and clouds.
In essence, this end-to-end infrastructure not only addresses the challenges inherent in data management, but also lays the foundation for effective collaboration and accurate analytical results in a constantly evolving environment.
A well-designed data infrastructure not only supports diverse business units and workloads, but also replaces data fragmentation with a centralised data repository that puts an end to data silos. Most modern cloud data architectures manage a single dynamic copy that feeds and updates machine learning (ML) models, business intelligence (BI) dashboards and predictive analytics applications.
This architecture enables data professionals to seamlessly process information relevant to their specific operations, while all teams can collaborate on a unified, shared data repository. This synergy is especially beneficial for data science teams, as consolidating data in a central location streamlines workflow, enabling more effective collaboration between data scientists, data engineers and machine learning engineers.
Today, data science teams employ a variety of tools, algorithms and machine learning (ML) principles to extract business insights from large volumes of data. Seamless interaction with the cloud data platform is essential and the productivity of data professionals increases significantly when they collaborate on a single, shared version of the data.
To ensure the productivity of all data professionals, a modern data architecture must support the most popular machine learning frameworks and languages, such as SQL, Python and Java for data engineers, and Python, SQL and R for data scientists.
When the data architecture is designed to support multiple teams and workloads without competing for resources, the productivity of data teams increases.
A shared, multicluster data architecture independently and virtually limitlessly expands compute and storage resources. This makes it possible for multiple users to query data simultaneously without degrading performance, even while other workloads such as data ingestion or training machine learning models are in progress.
A well-designed data architecture enables the combination of internal data with third-party data sets, generating rich insights and business opportunities. This enriched data can be shared with customers and partners, even monetised through data applications, thus extending the impact of data science to internal and external communities. Connectivity to a cloud data mart is essential, enabling collaboration with external providers and expanding the possibilities for data science teams.
In short, a shared, multicluster data architecture includes storage, compute and service layers that are logically integrated, but scale independently. This structure provides an efficient and versatile approach to managing workloads and facilitating collaboration in advanced data analytics.
Effective implementation of a modern data architecture means being able to track the origin of data, identify interactions and understand the relationships between severfal data sets.
A robust cloud data platform automates the generation of this metadata for both internal and external stages. Metadata is typically stored in virtual columns and can be queried using standard commands, such as Structured Query Language (SQL) SELECT statements, and integrated into a table alongside traditional columns of data. This approach facilitates efficient data management and monitoring, contributing to robust and transparent governance.
Managing and understanding metadata is fundamental to ensuring effective data governance within an organisation.
A data catalogue becomes a vital tool by empowering users to discover and understand the data they work with. Many data catalogues provide a self-service portal, improving accuracy and enabling more informed decision making.
While some organisations opt for external data catalogues, modern data architectures are moving towards the integration of internal catalogues. Some solutions incorporate directory tables that function as internal file catalogues.
Data cataloguing is a must, as the absence of cataloguing can lead to clutter that prevents companies from realising the value of their data. Data catalogues track information types, accesses, popularity, genealogy and usage of data, providing a complete view of available data and its usage for effective management and optimal utilisation.
In the maelstrom of data being stored in cloud data warehouses, classifying and contextualising it is essential for tracking sensitive and personally identifiable information (PII), preserving strong customer relationships and avoiding regulatory violations. It is crucial to know not only the location and types of sensitive data, but also how, when and by whom it is accessed.
In this regard, cloud data platforms that incorporate data classification tools become key allies, allowing administrators to classify, control and monitor the use of internal data.
These tools not only locate sensitive data, but also automatically understand the context of each part of the data set, including its creation date, last modification and relevance to the business. Furthermore, classification by department or business function helps to allocate costs to specific areas, optimising financial management.
A modern cloud data architecture plays a crucial role in fostering data governance and data quality in an organisation. By centralising data in a single cloud repository, greater consistency and control is achieved. This means that data governance policies, which define how data should be used, shared and protected, can be applied more efficiently and consistently across the organisation.
In addition, cloud architecture facilitates the unification of metadata, providing a detailed description of data and enabling more effective classification. This unified information about the data facilitates the implementation of governance policies by providing a clear view of where the data comes from, what it means and how it is used.
As we have mentioned many times in this blog, data governance and data quality are closely linked. Data governance is essential to ensure the quality of the data an organisation works with. In terms of data quality, centralising data in the cloud allows quality standards to be implemented more effectively. Data quality rules can be applied consistently, making it easier to identify and correct quality issues in a centralised environment. In addition, continuous monitoring of data quality is simplified through metadata management services that include relevant data quality information.
In an environment where different users interact with different data that is updated with new or transformed information on a recurring basis, transparency about the origin of the data is essential.
In this sense, data genealogy is positioned as an essential practice, as it allows data owners to query how data flows, transforms and manipulates within and outside the cloud data platform. Genealogy tools, integrated into the data platform or available as add-on services, provide a detailed view of the data's journey through processing systems. This detailed information includes the sources of the data, its trajectories and events in the process.
Data genealogy creates a complete map of the direct and indirect dependencies between data entities, making it easy to track the use of sensitive data and anticipate the impact of future changes.
Conclusion
In conclusion, a modern cloud data architecture offers a number of key benefits to organisations in terms of compatibility with programming tools and languages, support for multiple workloads and communities, metadata management, data cataloguing, classification and exploration of sensitive data, data governance and data quality, and data genealogy. These features enable data professionals to collaborate more efficiently, understand and make the most of their data, and ensure information security and quality. To take full advantage of the benefits of a modern data architecture, organisations are encouraged to explore these capabilities further and consider implementing them to optimise their data analytics and make more informed decisions.