Data architects design and manage huge data infrastructures and databases to handle and organize Big data. A state-of-the-art data architecture smoothens the way for both the infrastructure and its features to align with business objectives. Data architects examine the facilities’ existing data infrastructure and propose a plan to combine an existing system with a future model that possesses the resources within, optimized data structures, and the finest methodologies for handling big data. That’s why, best data architects, just like their other data science counterparts, must have rich technical knowledge and skillset.
Who is a Data Architect?
Data architects are senior-level experts. They use the finest technical design crafts to develop data infrastructure for organizations to collect, analyze and mine big data for deep insights. To do so, data architects must have problem-solving and critical thinking approaches to utilize a variety of tools, programming or scripting languages, and methodologies to find and propose modern solutions to handle big data.
Data Architect Job Role
A database architect and works in collaboration with data engineers to develop data infrastructure that can help an organization in handling and analyzing big data. Data architects must possess soft skills to work with high-end managers and stakeholders in a company or project to sustain the primary objectives of the company. Staying updated with trends, data architects design a platform that can help data analysts in examining insights.
Data Architect Job Description
- Communicate with different stakeholders to acknowledge needs and objectives.
- Evaluate existing data infrastructures
- Design and program new data architecture
- Integrate existing databases during the transition from old to a new model.
- Discover new methodologies to capture data from various resources
- Evaluate new infrastructure and generate efficiency reports.
- Responsible for security of the database, and develop procedures to ensure that the system is not vulnerable
- Document all observations and behaviors of the data model
Data Architect Skills Requirement
Data architects are professionally trained experts, familiar with a variety of programming languages and tools. They have to pay strong attention to even small details, as any problems in design or code can lead to disastrous consequences for a business model or project. A good data architect must be a hybrid of both soft and hard skills.
Hard skills involve:
- Strong understanding of Applied math and statistical data handling
- Data visualization
- Data migration methodologies
- Database management system such as SQL
- Distributed computing
- Hadoop methodologies, like MapReduce, Hive, and Pig
- Data management on different platforms
- Machine learning and deep learning
- Data mining
- Programming languages, especially Python and Java, as well as C/C++ and Perl
- Operating systems, especially UNIX, Linux, and Solaris
Successful data architects also possess several soft skills. They must be creative in the sense of problem-solving and critical thinking, ready to find solutions and welcome change in an era of continuously evolving technology and varying needs. They must have strong communication skills to establish transparent connections with data engineers and database administrators. They must be proficient in sharing ideas and discussing the situation with co-workers having poor technical backgrounds.
Data Architect Salary Package
According to Payscale, a data architect earns an average salary of $111,139 per annum. The most attractive salary packages need data architect to have expertise in Apache Hadoop. Data architects with experience of more than a decade can enjoy much higher salary packages.
Data Architect Job Outlook
The role of data architect is continuously changing as technology is evolving rapidly, and the best data architects adopt this change by staying updated with modern methodologies and programming languages and libraries. Immense quantities of data are generated every single second, and the need for data architects is continuously uprising around the globe to handle these huge amounts of data regularly.
With time, many companies are welcoming the reality that data architects are key agents in sustaining big data needs. All business domains have now realized that data architects are crucial in providing solutions and infrastructures that data scientists and analysts can utilize to mine big data for powerful business insights and foreseeing upcoming challenges.
How to Become a Data Architect ?
MAINSTREAM DEGREE PROGRAM:
Bachelor’s degrees in computer science, data science, IT, or other relevant field are highly recommended to make debut in this career. Data architects must have a rich background in computer programming, software development, and database management. It is recommended to choose course roadmap with these subjects.
ENTRY-LEVEL TRAINING:
Data architecture is not an entry-level job role as you’ll have to gain enough hand-on experience early to prepare yourself for launch. Search for internship programs in the IT field that can help you enhance your technical skills in application development, database management, and software development.
ON-JOB EXPERIENCE:
You can’t crack the position of Data architect directly as you’ll likely have to gain approximately 4 to 5 years of job experience and list of successful project completions on your account before becoming a data architect. Start from applying for entry-level jobs of a computer programmer or database administrator. Enhance your skills in database management, application development, and data warehousing.
OCCUPYING THE FIRST OFFICE:
After years of on-job experience, you’ll be ready to launch yourself in a career of your choice as a data architect. Search for relevant job roles in finance, healthcare facilities, business platforms, and other organizations that use to deal with large amounts of customer data.
BEST DATA SCIENCE CERTIFICATION PROGRAMS FOR DATA ARCHITECTS:
The best Data Science certification sessions include important aspects of Mathematics, Statistics, Python programming using various libraries, Deep learning, etc. There are different basic to professional level certifications for data engineers willing to master their skills by attending Data Science Training sessions. The 14 best data science certification programs for data architects are:
- Amazon Web Services (AWS) Certified Big Data Specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP)
- Google Professional Data Engineer
- HDP Apache Spark Developer
- HDP Certified Developer Big Data Hadoop
- Hortonworks Certified Associate (HCA)
- IBM Certified Data Architect
- MapR Certified Hadoop Developer 1.0
- MapR Certified Spark Developer 2.1
- Oracle Business Intelligence Foundation Suite 11 Certified Implementation Specialist
- SAS Certified Big Data Professional
- SAS Certified Data Scientist Using SAS 9
GRADUATE DEGREE PROGRAM
MS degree in data science or computer science with more courses related to machine learning and computer vision is highly recommended for upper-level experts in data architecture for a career boost.
CONCLUSION:
After the rebirth of machine learning, recent advancements in GPUs and supercomputing devices, availability of various tools, and language platforms to sustain and explore big data, it is now possible to implement statistical methodologies to uncover deep insights of big data. Data architects are responsible for designing infrastructures using these tools and methodologies and assist data engineers in developing data infrastructure that can hold big data.
Computer Science background is a common trait of data architects and data scientists, but both roles differ from each other in the sense that Data architects are responsible for designing and developing sustainable data infrastructures whereas data scientists use these infrastructures as a tool to discover insights and trends, hidden in the vastness of big data.
To master the data architecture field, you require years of experience in designing and implementing data science procedures, database management, and cloud storage, as well. You need to boost your skills in Hadoop, Spark, and the open-source big data platforms with rich programming skills in Python and Java.
