Data architecture and data engineering are two distinct techniques but have some commonalities. Both methods involve building databases or other data structures that house information for organizational analytics usage. i.e., sales leads or customer data. This post will demonstrate the difference between a data architect and a data engineer.
Data Architect vs. Data Engineer
Data architects define the data structures and make them usable by various departments within a firm. At the same time, data engineering solutions create those structures using programming languages like SQL (structured query language), Python, or Java.
Data architects design/plan analytical solutions and help organizations scale. They work with business stakeholders, and IT teams help them identify the best way to collect, store, manage, and analyze their data.
Meanwhile, data engineers develop/build these systems to facilitate data analytics solutions, scalable storage, secure access, data cleansing, and data categorization. The ICT (information and communications technology) devices also make data engineers conduct routine maintenance and upgradation procedures.
Skills of Data Architect vs. Engineer
1 | Data Architect Skillset
Data architecture is a role that requires you to have a deep understanding of both business operations and technical constraints. Data architects must understand and translate business needs into ICT infrastructure requirements. So, they can optimize data analytics solutions through the standardization activities described below.
- Specifying data storage, processing, and analytical data pipelines.
- Deciding whether these systems must run on top of big data technologies such as Hadoop, Spark, Kafka, etc.
2 | Data Engineer Skill Requirements
Data engineering solutions involve programming languages like SQL, Python, and R to build data operating models. These systems process large amounts of structured and unstructured data from various internal sources such as databases or files.
Data can come from external sources, including web APIs (application programming interfaces) or proprietary applications. i.e., Salesforce CRM or other customer relationship management tools.
It might integrate into the organizational infrastructure through an API gateway written in NodeJS/JavaScript. Consider third-party cloud services like Amazon S3 (simple storage service) and how your team interacts with such platforms.
Deliverables of Data Architect vs. Data Engineer
Data architects work with the business to define and design the data structure that maintains enterprise performance reporting. They ensure that the data gathering is easy and non-ambiguous. Besides, data architects specify security measures necessary to protect it from unauthorized access.
The data architect defines the schema (set of rules) that governs how data structure and storage behave in the data analytics solutions.
Data engineers create tables using SQL commands or MongoDB queries according to the schema. Also, they write code to insert new rows into tables based on user input or delete rows when analysts no longer need them.
Example of Data Architecture with Data Engineering Solutions
Assume data architects have decided which fields your team must include in each database table. Therefore, all your customer records would have uniform information such as name, address, and phone number.
The related schema might also contain fields for distinctive product attributes like color/size or customer birthday dates. Later, data engineers build applications around these schemas. So, the steps in the data analytics solutions become more efficient and optimize the process overheads.
Similarities Between Data Architect vs. Engineer
Skills common to both roles, data architect and data engineer, include knowledge of SQL and several programming languages. Your team also benefits from familiarity with cloud computing platforms. i.e., Amazon Web Services (AWS) or Microsoft Azure.
Both job obligations require a solid understanding of Python, Java, or R. Data analytics solutions rely on the stability of data engineering solutions and oversight by data architects.
Data architects must understand your business models and unique organizational necessities concerning data engineering solutions. Likewise, data engineers require clear communication from data architects to ensure standardization.
Conclusion
While data architects and engineers share similar skills and responsibilities at first, the difference between a data architect and a data engineer is still significant.
For instance, data architects must work with business users on projects related to designing database structures. In contrast, data engineers may not have such direct interaction with the people who use their products or services.
A leading firm in data engineering solutions, SG Analytics, serves enterprise requirements via a scalable and multi-disciplinary analytical ecosystem. Contact us today if you want a resilient and flawless data management and warehousing workflow to enhance business analytics.