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Data analysis is an ever-growing field in the technological arena. The journey of becoming a data architect from a data analyst is expanding the skillset and expertise to a new job role. Here, we will explore the roadmap to become a data architect:
Data architects are IT experts responsible for establishing guidelines, methods, frameworks, and systems for gathering, sorting, keeping, and accessing data for the organization. They are specialists who develop the organization's data strategy, covering data quality standards, data data movement within the organization, and data security.
A Data Architect "crafts a universal business language, articulates strategic needs, sketches out comprehensive integrated solutions to fulfill those needs, and ensures they are in harmony with the organization's overarching strategy and associated business framework."
The terms Data Architect and Data Engineer are often confused and, even more so, used as synonyms. However, the responsibilities of a Data Architect significantly differ from those of a Data Engineer. In the discussion between Data Engineer and Data Architect, while the latter conceptualizes the data framework's design, the former implements this design to establish a data framework.
A Data Architect identifies all data touched in a business and develops a plan to consolidate the data to one point and make it manageable. On the other hand, a Data Engineer is supposed to design and validate sound Data Architectures for the organization’s use and facilitate efficient data searching and locating. The Data Architects work very closely with Data Engineers in designing a good framework for data.
To excel as a skilled data architect, it's essential to have a strong base in data engineering. Data engineering is the core of a data architect's arsenal, offering the essential abilities to construct and upkeep the framework that underpins data systems. Here, we will delve into the roadmap to become a data architect:
Analyzing the data life cycle is critical to its discipline such as data engineering. This is the various stages such as the input, storage, processing, and analysis stages that are likely to be encountered. This conceptual level is possible only when a great amount of work has been done and it is crucial for data architects, as the result gives them an ability to implement the required systems that would manage the sets of data at different stages within the company.
Data architects must possess the skill to create and oversee databases effectively. Courses in data engineering provide a comprehensive understanding of different database systems, enabling upcoming architects to make knowledgeable choices regarding the most appropriate databases for their organization's requirements.
In today's highly connected digital era, the significance of combining data cannot be emphasized enough. Data engineering gives the people necessary skills to combine information which is received from different sources, forming a clear and accurate database. Converging these numerous datasets is key in creating significant knowledge, in addition to making the right decisions.
As companies expand, their data increases as well. Data engineers acquire the ability to create systems that can handle large amounts of data efficiently and improve their performance, essential abilities for a data architect who is in charge of handling extensive data in changing settings.
Data engineering programs highlight the significance of managing and protecting data. Data architects are responsible for making sure the data systems adhere to rules and norms of the sector, all while keeping confidential data secure.
Data architects must become proficient in both traditional relational databases and newer, non-relational databases to create robust and expandable data systems. They design structured relational databases and might also develop NoSQL databases to manage unstructured or semi-structured data from various sources.
They are skilled in the areas of data warehousing to perform large-scale data processing and analysis. This involves the ability to design data warehouses, build ETL (Extract, Transform, Load) data pipelines to merge different data sets, and use online analytical processing for business intelligence.
Data architects keep abreast of the latest application server frameworks and database management systems that organizations use, including Oracle, Microsoft SQL Server, and IBM DB2. They use these backend technologies to enhance database functionality and connectivity.
They are also knowledgeable in user interface and query languages such as SQL, making it easier and more accessible for users to access and interact with the data assets of an organization through user-friendly interfaces, dashboards, and reporting tools.
They tackle the challenge of integrating enterprise applications through middleware like message brokers and XML, allowing different applications, both old and new, to communicate and exchange data seamlessly.
Being agile is crucial for data architects, as it allows them to continuously adapt the data infrastructure to meet the changing needs of the business.
Proficiency in tools like ERWin helps architects visualize both the logical and physical aspects of data environments. Data modeling languages are used to explain complex database structures and processes to stakeholders.
Expertise in ETL tools is essential for the processes that aggregate data from various sources into data warehouses and data lakes, ensuring data transformations and integrity throughout.
Finally, the use of Hadoop-based clusters for big data management has become widespread for cost-effective storage and handling of large volumes of data for analytical tasks. Data architects design and organize these varied environments using the best data platforms and tools for the specific needs of the organization.
The skill of a data architect is to communicate effectively crucial for data architects to collaborate with both business and technology departments within companies. They must be able to clearly present detailed plans for data infrastructure, simplify technical details, and listen attentively and respond to different requirements and viewpoints.
Their ability to analyze and solve problems equips data architects to tackle complex data issues related to governance, quality, security, integrity, and accessibility.
Moving from a Data Analyst role to a Data Architect position involves a path of ongoing education and the enhancement of abilities. It demands a mix of technical expertise, strategic planning, and interpersonal skills.
