Data Engineering and Data Architecture
Overview
“In the digital age, data is the new oil. But unlike oil, it’s a renewable resource that can be refined, shaped, and molded into invaluable insights. Data architecture and data engineering are the architects and engineers behind this transformation. They design, build, and maintain complex systems that extract, transform, and load data into a usable format.
Imagine a world without data: no personalized recommendations, no advanced medical research, no self-driving cars. Data architecture and data engineering are the unsung heroes that make this digital world possible. Let’s dive into the fascinating realm of data and explore how these critical fields are shaping our future.”
Data Architect vs Data Engineer
In civil engineering, an architect will provide the foundation of a building for his clients. Likewise, a data architect offers a system design of a big-data solution for his clients. This role is key since it is cheaper to carry out changes in the design phase compared to the implementation phase.
See data engineers as construction personnel who lay blocks on the construction site based on the architect’s plan. The data engineers are responsible for building, deploying, and testing data pipelines for big-data solutions.
For one to be an excellent data architect, he must have strong hands-on experience in data engineering since there is a need for data architects to have a strong grasp on system development.
Data Architecture
In this phase of the big data, the following processes will be implemented:
1. Gather the project requirements
The most important thing for a data architect is to gather the project requirements and refine them. Questions like what is the business trying to do? There is a need to understand the business needs. These needs can be AI solutions, data warehouse, and/or visualization solutions. Data architects must evaluate the existing technical environment available at the client’s end (Cloud or on-premises database, tools/services and programming languages (Python, Java, C or others). Data architects must explore and evaluate the data available in the environment (volume, structure or unstructured, velocity, veracity). Data architects must predict the in-flux of data when the product/solution is in use. The Data Architect must discuss the pain points, challenges, risks, constraints, budget, and scope with the stakeholders. Finally, data architecture must document all the project requirements before signing off on the project.
2. Define the High-Level Architecture.
In this phase, data architects must have a vision of how the solution will look like. What kind of data platforms will be used in this project (databricks or others). The supporting services like storage plan, and the data transformation technique needed in this project (extract, transform and loading (ETL) or extract, load and transform (ELT). Get security involved especially when your solution is used in the banking and healthcare sectors.
Avoiding unnecessary risks
There are some ways data architects can avoid investing too many resources on a project while meeting the client’s requirements. They are:
a. Proof of Concepts
Proof of concepts are mock-ups or simple scaled-down versions of the architecture as a “sanity check” that it will meet the requirements. This also involves test key requirements such as row-level security.
b. Pilots & Minimum Viable Products (MVP)
Pick a subset of the solution functionality or business area to develop and deploy an initial phase of the solution. This limits the risks and helps identify problems earlier. It is worth noting that pilots are real deliverables.
Data Engineer
The primary responsibilities of a data engineer are to: develop, test and deploy data pipelines.
Develop Pipelines
This phase entails writing code to get data from sources, land it in storage, clean and transform it, and aggregate it and save it to the solution layer in a form that is usable.
Testing
Running test data through the pipeline and verifying the output matches the requirements.
Deploying Data Pipelines
Automate deployment via the appropriate tools (GitHub Actions, Azure DevOps, Scripts, Data bricks Asset Bundles).
Which is more important?
Data architecture is the foundation upon which data-driven initiatives are built. It provides a blueprint for how data will be collected, stored, processed, and analyzed. A well-designed data architecture ensures that data is accessible, reliable, and secure. It also facilitates data governance, ensuring that data is used ethically and responsibly. By laying a strong data foundation, organizations can make informed decisions, improve operational efficiency, and gain a competitive edge.
Errors in data architecture can be extremely costly to fix. The longer an error goes undetected, the more data can be corrupted or lost. This can lead to inaccurate analysis, flawed decision-making, and even regulatory violations. Additionally, rectifying errors in complex data architectures often involves significant time and resources, as it may require rebuilding entire data pipelines or migrating data to new systems.
The earlier in the process you make errors, the costlier:
Architecture: Errors at this stage can have the most significant impact, as they can fundamentally affect the entire data infrastructure. Fixing these errors often requires extensive re-engineering and potentially rebuilding system parts.
Design: While not as severe as architectural errors, design flaws can still be costly to address. They may involve rethinking data models, data flows, or system components.
Construction: Errors discovered during the construction or implementation phase are generally less expensive to rectify. They often involve specific coding issues or configuration problems that can be addressed more directly.
The key is to identify and correct errors as early as possible. Rigorous testing, code reviews, and quality assurance practices are essential to prevent costly mistakes.“Want to learn more about data engineering and architecture? Subscribe to our newsletter, share this post, or contact us for a free consultation. We can help you build a robust data infrastructure tailored to your specific needs.”
