Modern Data Architecture: Making the Most of Big Data
One of the many advantages of cloud computing is practically unlimited data storage. And if you want to take advantage of big data, you’ll need to adopt a modern data architecture. In this post, we’ll explore modern data architectures, explaining what they are, why they matter, and their pros and cons.
This is a good introduction to giving you a better sense of why modern data architecture is important, but to fully take advantage of this method of storing and organizing data, it’s wise to hire a database administrator (DBA) with solid cloud experience.
Table of contents
What is modern data architecture?
Data architecture is the structure of a system’s data management resources and data assets. Modern data architecture is designed to proactively anticipate complex data needs, with flexibility and scalability at its core. It serves as a blueprint for managing data, ensuring that data is managed properly and meets business needs for information.
Modern data architectures are developed with a plan of how your data assets and information systems will interact with each other. This involves outlining how data in your system will be created, stored, processed, and transported.
But where does this all exist? There are three main components of data architecture:
- Physical, which includes hardware mechanisms used for processing, moving, and storing data.
- Logical, which involves how different types of data relate to one another.
- Conceptual, consisting of business entities and operations.
Why do data architectures matter?
Modern data architecture serves as the foundation of data strategy that backs business priorities and goals. It helps organizations gain a better understanding of their data and provides guidelines for managing and analyzing data.
A data architecture supports data integration, data quality improvement, data engineering, and data preparation. It also allows organizations to implement data governance and the development of internal data standards, which helps teams ensure their data is accurate, secure, and consistent.
The evolution of data architecture
In the past, data architectures were far less complicated than they are now. Organizations created static, IT-driven data architectures, called data warehouses. These data warehouses mostly consisted of structured data from transaction processing systems and required a large team of people to maintain.
The adoption of big data technologies throughout the mid-2000s added semistructured and unstructured forms of data to architectures. This led to the creation of data lakes, a centralized location where organizations can store all structured and unstructured data at any scale. Data lakes can store raw data rather than reconstructing it for analysis upon ingestion, which was a huge change for the data warehouse process.
Today, modern data architectures support machine learning and artificial intelligence applications in addition to the basic reporting and BI of data warehouses. The increasing popularity of cloud-based systems also adds to the complexity of today’s data architectures.
How is data architecture different from data modeling?
Data modeling focuses on the granular details of particular data assets and offers a visual representation of data entities and how they’re related to each other. Data architecture takes on a more high-level view of an organization’s data to create a blueprint for data usage and management.
Other key differences between data architecture and data modeling include:
|Data modeling||Data architecture|
|All about the representation of data||All about what tools and platforms are used to store and analyze data|
|Focused on the accuracy of data||Focused on the infrastructure housing that data|
|A representation and visualization of reality||A framework of logistics and systems|
|Representation of a limited set of concepts and how they related to each other||The infrastructure of an entire organization|
|Deals with the reliability of data||Deals with keeping that data safe and secure|
While they’re different, these two processes complement each other. An established data architecture makes data modeling easier and data models provide critical insight to the data architecture process.
Characteristics of a modern data architecture
A well-designed modern data architecture includes the following six characteristics:
- Flexibility. A modern data architecture must be flexible enough to support a variety of business needs. It needs to maintain multiple types of load operations and refresh rates, business users, data processing engines, query operations, and pipelines.
- Elasticity. With big data and variable workloads, organizations need elastic data architecture that can scale and roll back resources when needed. An elastic data architecture frees administrators from worrying about overbuying hardware, calibrating capacity exactly, or throttling usage. Public and private cloud platforms allow organizations to get on-demand scalability at affordable prices.
- Automation. Modern architectures are too massive for a manual approach to data management. Automating processes like data structuring, predictive analytics, and data relational schemas help maintain system integrity on a large scale. Automation also makes it easier to detect abnormalities and alert the appropriate users.
- Intelligence. In addition to automation, a well-designed data architecture uses machine learning and AI to create data objects, models, views, and tables. Intelligence makes a modern data architecture more efficient and effective by making it easier to identify data types, pinpoint and resolve data quality errors, map tables, determine relationships, and recommend analytics.
- Simplicity. The simpler a data architecture, the better. Sounds easy enough, but it can be difficult to keep it simple given the range of requirements and complexity of components in modern data architecture. An organization with small data might be best served by a BI tool with built-in data management, rather than a huge parallel processing appliance. Organizations should aim to limit data movement and data duplication, as well as promote a uniform database platform, analytic platform, and data assembly framework.
- Security. A modern data architecture allows authorized users access to data while protecting it from hackers. A well-designed data architecture complies with privacy regulations, like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). It meets these standards by masking personally identifiable information, encrypting data, and tracking all data elements in a data catalog. Through lifecycle management, organizations can make sure each data object has an owner, location, and an obsolescence plan, ensuring security every step of the way.
Benefits and challenges of building modern data architectures
Any large-scale technological approach will always come with benefits and challenges. Let’s take a closer look at what some of the benefits of creating a modern data architecture include.
Benefits of building modern data architecture
There are various advantages to a modern data architecture, such as:
- Scalable systems. Flexible and scalable infrastructure frees you from relying on specific platforms, environments, or data types.
- A centralized approach to integration. Large organizations have massive amounts of data, which is complex to manage. A centralized view of data from various sources makes it easier for administrators to configure and maintain data from the entire organization.
- Enhanced data analytics and visualization. Monitor how your data travels through your systems, how it’s used, and where there may be barriers with built-in data analytics and visualization.
- Storage for large amounts of data. Data architecture allows organizations to store massive amounts of data with no restrictions.
- Innovation and efficiency. Machine learning and AI simplify data processing methods and generate innovation and efficiency across the entire organization’s system.
- Resilient infrastructure. Modern data architecture builds resilient infrastructure that supports secure backups and elastic scaling that incorporates or releases various platforms on demand.
- Reduced latency in hybrid environments. Duplicating data from one place to another increases latency. Modern data architecture makes the data available throughout the entire organization, so all users can have access to it as quickly as possible.
Challenges of building modern data architecture
While the benefits are numerous, there are also challenges associated with modern data architecture. Some of these challenges include:
- Complexity. While a modern data architecture makes workloads easier through unified interfaces, the underlying architecture is still extremely complex.
- Sustaining data quality. Even the best-built system is only as good as its data. If you don’t have processes set up to determine the quality of incoming and outgoing data, you’ll end up with inadequate results.
- Aligning tech and business goals. Beyond the systems you use to manage your data, your organization needs to align tech and business goals. It’s important to have leaders who prioritize both and promote innovation.
- Developing data strategies. A well-built modern data architecture is not enough to ensure strong results. Your IT team should understand your business and data needs, using that knowledge to develop a data strategy that produces meaningful results.
With an understanding of what the challenges are, your team can plan accordingly and set your organization up for success.
Make the most of your data
Cloud services like Amazon S3 and Azure SQL open a lot of possibilities for what you can do with your data. But without a solid data architecture, you could be missing out on some of the most exciting applications of big data, like machine learning and business intelligence. If you haven’t already, look into hiring a cloud database administrator (DBA) to make sure your data is stored and organized in a usable way.
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