Data architecture and data modeling are different from one another. Data architecture gives a global view of how data is managed in a business, whereas, data modeling are detailed rules that brief how different data must be arranged in a database.
Today, many data architects, data engineers, and data scientists use data architecture to get a clear understanding of the data, analyze it, and share it with team members for strategic decision-making. Without proper knowledge of data architecture, you will lack the ability to develop data models.
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Coming back to the discussion, many strategists demand more data to get quick insights. However, this is possible only if all the data are properly stored and managed. It is exactly where data architecture comes into the picture.
What Is Data Architecture?
Data architecture is a design flow that briefs you on how the data flows and integrates through the systems, manages, and meets business requirements. It is a fundamental part of information management.
In order to create effective data architecture, it is necessary to understand what types of data are required for the business and how they should be organized. Data architecture is not just about storing data, but also about organizing it so that it can be retrieved efficiently.
The most common types of data architectures involve relational databases (RDBMS) or NoSQL databases.
Components of Data Architecture
In today’s data-driven marketplace, if you want to stay ahead of your competitors, analyze your data architecture patterns and make better strategic business decisions. Learn how the data is collected, stored, and used via the data architecture framework. Below we have listed some of the main components of data architecture:
- Data Pipelines: Data Pipeline is a series of processing steps that include collecting data from different sources, storing, analyzing, and moving it from one source to the other. In simple terms, Data Pipelines use ETL to extract, and transform data and load it into data warehouses.
- Cloud storage: Cloud storage is a storage facility provided by third-party cloud vendors for storing the extracted data from data pipelines. It is secure and allows users to access their data at any time only through the internet.
- APIs: API is another vital component that helps create secure communication via IP address between the host and a requester. Using API, users can communicate and share a large amount of data with the user.
- AI & ML models: AI and ML models enable automation and understanding. It helps users make better decisions and predictions along with labeling, collection, etc.
- Data streaming: Data streaming is a way of accessing data in real-time. It is possible because the data is stored in a database, which can be queried for information and processed to produce results.
- Kubernetes: It is an open-source system used for automating deployment, computing, networking, scaling, management, and storing infrastructure workload.
- Cloud computing: Cloud computing is a term for a group of technologies and services that allow consumers to access their data from any location. It is an alternative to the traditional data center model such as colocation data center, which required companies to purchase, install, and maintain their own servers. which required companies to purchase, install, and maintain their own servers. Cloud computing helps save costs, maintains data security, and requires no additional servers for storage as everything is managed and analyzed through the cloud.
- Real-time analytics: Using this component, users can get deeper insights into the data in real time and make accurate decisions for their business.
Benefits of Data Architecture
Earlier, data architectures were less complicated as experts had to only manage structured data from transaction processing systems mostly stored in relational databases. But, after mid-2000, when businesses started adopting big data technologies, a good count of unstructured and semistructured forms of data were added to the architectures.
As a result, data lakes were deployed, for data warehouses were not strong enough to handle big data.
Today, with different types of data architectures, it has become easier to manage artificial intelligence and machine learning applications. Additionally, it helps organizations develop better data analytics platforms that deliver deeper insights. These insights later help businesses improve their strategies and operational decisions, resulting in better business performance.
The right data architecture strategy provides a global view of an organization’s data, helps streamline data integration, reduces storage costs, and improves data quality.
Types of Data Architecture
Applications, Data warehouses, and data lakes are the only data architecture types that one needs to know as a person within the business or a product owner.
Applications: Application Architecture is a roadmap that describes the patterns and techniques used for developing an application. This type of data architecture helps in building a well-structured app.
Data Warehouses: Data Warehouse is another important type of Data Architecture. They store data in a central location and provide access to it for other systems within an organization. They help to gain insights from the data by providing a single source of truth, which is not possible with other database types such as relational databases or NoSQL databases.
It collects data from different sources, flows it through an ETL data pipeline, and transforms it to meet the data model requirements. Once loaded into the data warehouse, it is used by experts for data science applications and BI.
Data Lakes: Unlike Data Warehouses, this type of data architecture stores both structured and unstructured data. It is later used by developers, data scientists, and engineers for data discovery practices and running machine learning projects. Data Lakes was generated when experts felt that Data Warehouse is not strong enough to manage big data or growing volume.
Today, Data Lakes is used for proofs-of-concept, developing big data analytics projects, and machine learning applications.
Data Architecture Best Practices
Check out the below-listed best practices that one must incorporate while developing a data architecture strategy.
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Drive the process through collaboration
Collaboration helps improve teamwork and enables businesses to make better decisions. With a good data architecture, businesses and their departments or team leaders are able to communicate and share goals in a better way.
It helps decision-makers conclude or decide which data is vital for your organization and has the ability to create a lasting effect on the organization. Based on it, data engineers and architects build a path ensuring that the information is easily accessible by each member.
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Prioritize data governance
Prioritize the high-quality data and align it with your strategy to make effective decisions. Data governance is the process of managing and organizing data so that it can be used efficiently.
There are many benefits to prioritizing data governance, including the increased ability to use data for decision-making and better information for stakeholders. Data management is a tool that all organizations should prioritize and must target the information that focuses on fulfilling the needs of the business.
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Focus on attaining agility.
Today, there is more demand for new technologies. It is important to ensure that the data architecture is not restricted to a specific technology, but is adaptable to the current changes. With the change in the data types, the data architecture must have the ability to accommodate these changes.
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Conclusion
Today, data analytics is widely used in organizations, it’s evident that you will require data-driven architecture to brief on how the data flows. Also, it will help analyze the patterns and lead to strategic business decisions. It is time when technology leaders must start embracing new challenges and adapting themselves to the competitive environment.
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