Introduction to Cloud Data Architecture

Businesses leveraging cloud data architectures accelerate data insights while reducing IT cost. These advantages, and more, reflect the potentialities of cloud systems. Therefore, as a business owner, or data professional, advocating for cloud data architecture is vital.

For instance, cloud data architectures provide exceptional flexibility. As a result, it becomes easier for your company to create new data platforms or scale current systems. Furthermore, cloud data infrastructures ensure improved security.

When done right, cloud data architectures help your business grow. Hence, the rest of this article examines the basics you need to know.

WHAT IS CLOUD DATA ARCHITECTURE?

Cloud data architectures comprise housing rules, policies, and models dictating an organization’s data collection, storage, usage, and management. The systems function synchronously to execute proper data flow, processing, and distribution.

With a cloud data architecture, distributing data across in-house stakeholders becomes seamless. Hence, activities like reporting, analytics, enrichment, etc., progress efficiently.

The world’s digital connectivity and access open companies to large data volume. In addition, data collection is accessible across different channels. However, traditional data platforms have problems scaling up to meet storage, processing, and management demands.

Also, the complexity and speed of needed applications call for machine learning and Artificial Intelligence. Traditional data systems can’t provide the required infrastructure for such applications.

Hence, the need for cloud data architectures.

ADVANTAGES OF CLOUD DATA ARCHITECTURES?

Cloud data architectures can scale a business’s operations, revenue, profit, and impact. More specifically, here are the roles cloud data architectures can play in a company.

Improvement of a company’s data services – cost reduction, data localization, tooling, etc.
Improvement of a company’s data security
Cloud data architectures are better time-managed
Ease of scaling data security tools.
Workflow acceleration and improvement of business agility

TIPS ON BUILDING DATA ARCHITECTURES IN THE CLOUD

While many businesses are new to cloud infrastructures, the place of best practices cannot be understated. Here are some tips to help set up optimal cloud data architectures:

Solve Business Problems

When setting up cloud data architectures, ensure you’re addressing a particular business problem. First, there needs to be a business use case; then, you go to the cloud components you need. Finally, ensure that each of the cloud components actively creates value. Doing this makes it easy to evaluate the cost and advantages of cloud migration.

Test and Experiment

Numerous cloud data architecture components and combinations exist. Hence, there’s no best setup system. What works for another company may not work for yours. So, you should be willing to test, experiment, and reiterate.

When you’re testing fast and shutting things down, it’s an inexpensive process towards finding the best data architecture. It’s better to try different approaches than investing in a wrong system bound to fail.

Control Unstructured Data

Traditional data infrastructures always fall behind in managing unstructured data. Cloud data architectures solve that problem. Examples of unstructured data include; social media feeds, XML documents, etc. It becomes easy to define, sort, and manage data sets with cloud systems.

Focus on Workflow

To maximize cloud-based data platforms, ensure that they improve data workflow. Focus on enhancing workflow rather than management tools and technologies. Keep reiterating on processes that can reduce data cycle time.

Ensure that cost matches data requirements

If you don’t take much care, the cost of running data infrastructures can transcend control. You must ensure that your systems are running at an optimal level. Know what is needed and what will give the highest returns on investment. Churn every irrelevancy.

ISSUES OF MANAGING HYBRID CLOUD DATA ARCHITECTURES

Data management goes beyond knowing what a data marketplace is and how to maximize them. Nor is it only about learning how to enrich your database.

Scale, real-time processing and leverage, improved workflow, etc. These are the things expected from modern data systems. Hence, the need for cloud architectures.

However, it’s possible that your company can’t execute total cloud data migration at once. In this case, implementing hybrid cloud architecture is advised. Here are the factors to consider when deploying and managing a hybrid cloud.

Data Governance

It becomes more expedient to take data governance and security seriously when merging on-premise and cloud data systems. Your systems are prone to attack. Hence, ensure high-level encryption for data sources, services, and storage facilities. Increase data privacy standards, and establish rigorous gatekeeping.

Increased Complexities

On-premise data facilities and cloud infrastructure have peculiar functionalities. Shuttling between two infrastructures becomes complex with time. Hence, improving the conduction of due diligence. Document everything; data dimensions relative to volume, query volumes, response times, data latency, data structure complexities, etc. Focus on improving data quality.