Browse Definitions :

8 top data architect and data engineer certifications in 2021

Learn what it takes to achieve and accelerate a rewarding career in data architecture and choose from among some of the best data architect and data engineer certifications.

Professional certifications can help people pursuing jobs as data architects and engineers jump-start or accelerate their careers as well as get a leg up on the competition. These certifications measure a person's knowledge and skills against vendor and industry benchmarks to show potential employers that the individual has the necessary expertise to be successful and participate in developing and fulfilling enterprise data strategies.

Certifications indicate that current data architects and data engineers are taking a proactive approach to their careers. Since certified professionals are assets to any organization, certifications give enterprises the incentives to retain those employees, typically with promotions or raises.

Following is a list of some of the top data architect and data engineer certifications.

1. IBM Certified Solution Architect -- Data Warehouse V1

This certification covers the planning, designing, building, governing and securing of a data warehouse with minimal help from support, documentation and subject matter experts. Individuals must pass an exam consisting of seven sections and a total of 62 questions (42 correct answers are required to pass). Individuals must also demonstrate that they understand the concepts and architectural principles of data warehouses, can analyze a customer's business requirements and processes, and can build data models for a data warehouse.

Who should take this course: Data architects, developers, IBM internal employers, business partners and independent consultants selling IBM products.

 Course details  

Building a data model

2. IBM Certified Data Engineer -- Big Data

This certification demonstrates that individuals have the technical skills and knowledge necessary to become IBM big data engineers, including using technologies to solve big data problems and building large data processing systems. Prerequisite skills include an understanding of cluster management, networking, data layers, interfaces, data modeling and translating functional requirements into technical specifications. Individuals must pass an exam consisting of five sections and 53 multiple choice questions (34 correct answers are required to pass). This certification requires candidates to translate functional requirements into technical specifications.

Who should take this course: Big data engineers who work with data architects and developers and provide input to architects on the necessary hardware and software.

Course details

3. SAS Certified Big Data Professional Using SAS 9

This certification demonstrates the ability to use open source and SAS data management tools to prepare big data for statistical analysis as well as the skills to recognize and overcome big data challenges; access, transform and manipulate data; apply fundamental statistical techniques, work with SAS, Hadoop and Hive; and explore and visualize data. Candidates must have at least six months of programming experience in SAS or another programming language to enroll, and they must pass two certification exams to earn this credential: SAS Big Data Preparation, Statistics and Visual Exploration and SAS Big Data Programming and Loading.

Who should take this course: Big data professionals who want to certify that they can use SAS and open source data management tools to prepare big data for statistical analysis.

Course details

4. Google Professional Data Engineer

This certification exam determines whether an individual can design, build, deploy, secure and monitor data processing systems. It also assesses the ability to use, deploy and continuously train existing machine learning models. Each candidate must pass a two-hour exam that includes multiple select and multiple choice questions. There are no prerequisites for this exam, but Google recommends at least three years of industry experience, including at least one year designing and managing tools using Google Cloud Platform.

Who should take this course: Data scientists, data engineers, data architects, DevOps engineers and machine learning professionals.

Course details

5. AWS Certified Data Analytics -- Specialty

This certification confirms an individual's technical skills and experience with data lakes and AWS analytics services. It also determines if an individual can define AWS data analytics services as well as recognize how they integrate with each other. In addition, individuals must know how AWS data analytics services work in conjunction with the data lifecycle of collecting, storing, processing and visualization. To take this exam, an individual should be an AWS Certified Cloud Practitioner or have associate-level AWS certification and have at least five years of practical experience with data analysis technologies and worked two years with AWS.

Who should take this course: Data platform engineers, data architects, data scientists and data analysts.

Course details

6. Cloudera Certified Professional (CCP) Data Engineer

This certification refines data engineering skills on the way to becoming a professional engineer, demonstrates the skills to be a reliable developer and data analyst to help in optimizing data sets for a variety of workloads and provides an understanding of data ingestion, transformation, storage and analysis. It also shows an individual's ability to tackle data and develop a clean, widely used platform for various applications. This exam is scenario-based and presents individuals with large, diverse and unstructured data sets that must be solved within a specific time limit.

Who should take this course: Data scientists, data engineers, data analysts and project managers.

Course details

7. Microsoft Certified: Azure Data Engineer Associate

An individual pursuing this certification should be a subject matter expert in integrating, converting and consolidating data from unstructured and structured data systems into structures that can be used to build analytics tools. This certification demonstrates that an individual can design, develop, implement, monitor and optimize data storage, data processing and data security and uses various Azure data services and languages to store and produce cleansed and enhanced data sets for analysis. The certification requires substantial knowledge of such data processing languages as Python, SQL or Scala, an understanding of parallel processing and data architecture patterns, and passing Exam DP-203: Data Engineering on Microsoft Azure.

Who should take this course: Data engineers, data architects, IT professionals, database administrators and business intelligence professionals.

Course details

8. Arcitura Certified Big Data Architect

The Big Data Architect track consists of the several Big Data Science Certified Professional (BDSCP) modules: Fundamental Big Data, Big Data Analysis and Technology Concepts, Fundamental Big Data Architecture, Advanced Big Data Architecture and Big Data Architecture Lab. The last module is a series of lab exercises that require individuals to apply what they've learned in the previous courses to fulfill the requirements of the project and solve real-world problems. Earning this certification demonstrates that an individual can design, implement and integrate big data tools on premises or in the cloud. There are three flexible exam format options.

Who should take this course: Data scientists, data analysts, data engineers, data managers and IT professionals.

Course details

Next Steps

The top 6 use cases for a data fabric architecture

Data architecture vs. information architecture: How they differ

Dig Deeper on Data and data management

  • ISO 31000 Risk Management

    The ISO 31000 Risk Management framework is an international standard that provides businesses with guidelines and principles for ...

  • pure risk

    Pure risk refers to risks that are beyond human control and result in a loss or no loss with no possibility of financial gain.

  • risk reporting

    Risk reporting is a method of identifying risks tied to or potentially impacting an organization's business processes.

  • Pretty Good Privacy (PGP)

    Pretty Good Privacy or PGP was a popular program used to encrypt and decrypt email over the internet, as well as authenticate ...

  • email security

    Email security is the process of ensuring the availability, integrity and authenticity of email communications by protecting ...

  • cyberterrorism

    Cyberterrorism is often defined as any premeditated, politically motivated attack against information systems, programs and data ...

  • What is risk mitigation?

    Risk mitigation is a strategy to prepare for and lessen the effects of threats faced by a business.

  • fault-tolerant

    Fault-tolerant technology is a capability of a computer system, electronic system or network to deliver uninterrupted service, ...

  • synchronous replication

    Synchronous replication is the process of copying data over a storage area network, local area network or wide area network so ...