You're about to start a career in data architecture. What do you need to know first?

I think to get started with Data architecture, foundation is very important. Understanding of database design, data modelling along with a good command on cloud adoption is very important. Another major factor which I see is staying current. The tech field evolves rapidly, we already have got different flavors of Spark, variety of data warehouses. Follow industry trends, learn new platform, and adapt. Furthermore, communication is key. You'll need to explain complex concepts simply and work with cross-functional teams.

I believe theres no single answer for this, to really breathe data and its architecture to the best here’s what I would do. 1.Begin with Data Modelling & ETL to understand table design, types of tables, constraints & relationships 2.Move on to Canonical Modelling to grasp the concept of ELT in large warehouses 3.As you understand ELT, shift to Data lakes & what benefits they offer 3.Dive into latencies, availability, & scaling for data processing 4.Prioritize designing cost-effective solutions over fancy tech 5.Make the most of Open Source tools whenever you can 6.Don't forget to bounce ideas off other Data Engineering professionals for valuable feedback The key, honestly, is to develop a firm grip over the fundamentals mentioned above.

Starting a career in data architecture demands a robust grasp of core concepts like data types, structures, normalization, and denormalization. Get acquainted with popular database management systems such as MySQL, PostgreSQL, MongoDB, etc., and learn basic operations like modeling and querying. Explore data modeling techniques like ER and dimensional modeling, translating business needs into efficient models. Dive into data integration methods, emphasizing ETL and ELT processes. Understand data governance for quality, security, and compliance. Familiarize yourself with big data tech like Hadoop, Spark, Kafka, and data warehousing concepts such as star schemas and tools like Redshift, BigQuery, and Snowflake.

From my point of view Data Architecture, is a complex but rewarding field and the following skills would help you to succeed. 1. Cloud Architecture. You should pick one public cloud provider, and study hard the solution architectures recommended from each cloud. Let's say you pick AWS, you should read a lot white papers such the pillars, security and others. Deep dive extensively into the cloud solutions architectures, vpcs, iam, event driven, batch, data processing offering. Work examples with terraform to get familiar with Infrastructure as code and you will level up. I would suggest take the solution architect cert, or cloud engineer certifications at least.

Data Architecture in my opinion is a mix of: Software Engineering, Data Engineering, Analytics, Cloud Architecture and Operational side. Go hard with batch processing guidelines on public cloud providers, they provide useful use cases that you can understand the components. Later, move to event streaming, continuous processing, watermarks, etc. This topic is complex, but it is a must to have in your arsenal. Finally, knowledge of distributed processing systems is never enough.

Data-Architects (DA) play a pivotal role in designing, building, and managing the architecture of an organization's data infrastructure. DA(s) are responsible for developing data models, defining data standards, and ensuring data integrity and security. Additionally, data-architects collaborate closely with stakeholders from different departments to understand their data needs and design solutions that align with the organization's strategic objectives. In addition to data-architects, other key roles in data architecture include data engineers, data scientists, and database administrators. Data engineers focus on the development and maintenance of data pipelines and ETL processes, ensuring efficient data flow across systems.

Distributed Processing hands-on: Apache Spark, Flink and others are your best friend. Go inside out of table formats, Change Data Capture, Slowly changing dimensions, etc. Study devops principles, containers, deployment techniques, CI/CD pipelines, versioning, The SRE book from Google is an invaluable resource there. Batch and Streaming processing, spend time here, you need to understand in a blink of an eye when to use each one of them. Dimensional modelling. Who says Kimball's framework is outdated? Go for it. Read the book Design Data-Intensive applications from Martin Kleppmann, it will take you to the next level. Keep yourself updated of the recent architectural publications, documentations, diagrams, will be your best friend.

With my experience, start by acquiring a solid foundation in database concepts, data modeling techniques, and information management principles. Technically, understand the various categories and type of data. Familiarize yourself with various database technologies, such as relational databases (OLTP), data warehousing solutions (OLAP) and NoSQL databases (parallel-processing concepts). Additionally, stay in-touch of industry trends and emerging technologies in data architecture, such as cloud computing, big data analytics, and data governance frameworks. Participate in online courses, workshops, and certification programs to deepen your knowledge and acquire practical skills in data-architecture tools and methodologies.

In my experience; 1. Ensuring data quality and integrity. Data architects must implement robust data validation and cleansing processes to mitigate errors and inconsistencies that can adversely affect decision-making and business operations. 2. Design scalability and performance optimization. As data volumes grow exponentially, data architects must design scalable and efficient data storage and processing solutions to support the evolving needs of the organization. 3. Data security and privacy are also significant concerns to protect sensitive information from unauthorized access, breaches, and regulatory violations. 4. Integrating unequal data sources and systems presents challenges in data interoperability and integration.

Data-architecture hinges on soft skills and attributes such as communication, problem-solving, ownership, and adaptability. Cultivating a problem-solving mindset enables data architects to tackle challenges creatively and innovatively. Being adaptable and open to change is essential in navigating the dynamic landscape of data architecture, where technologies and business requirements evolve rapidly. (Last but not the least,) Maintaining a commitment to lifelong learning and staying curious about emerging trends and technologies will position you for success in your data architecture career. Embrace opportunities for growth, seek feedback, and continuously strive to enhance your skills and expertise.