In June, Taiwan ushered in the world’s largest AI event COMPUTEX TAIPEI. Nvidia CEO Jensen Huang’s speech not only set off a wave of people but also defined Taiwan as the starting point of “AI”, foreseeing the future AI in economic development, or the growth of enterprises to play an important role.
For the technology behind AI, Nextlink has analyzed the differences between machine learning and deep learning. Today we will focus on the structure of data, analyzing what is called Data Mesh and it’s differences with the data lake, in order to help enterprises choosing a right data management mode and build their own data structure.
Table of Contents
Table of Contents
What is Data Mesh?
Data Mesh is a decentralized data structure approach, the main goal is to solve the scalability and flexibility problems faced by traditional data platforms. Data Mesh emphasizes data operational efficiency at scale by decentralizing data management responsibilities across business areas across teams. Data Mesh has three core concepts and principles:
Domain-Oriented Design
Data mesh assigns data management responsibilities to various functional domains that are responsible for their own data sets and data products. In this way, data experts in each domain can better understand and manage their data and ensure the quality and consistency of the data.
Data as a Product
In data mesh, each data set is treated as a product and needs to follow best practices for product management. This means that a dataset should have a clear owner, definition, documentation, and SLAs to ensure its quality, availability, and discoverability.
Self-Serve Data Platform
Data mesh emphasizes building a self-service data infrastructure that enables data engineers and data scientists in all fields to easily generate and consume data. This requires a shared set of tools and services to support data pipelines, automation, and governance to reduce the burden of technical details across domains.
From the above, it is clear that the main purpose of data mesh is to transfer data autonomy to the person in charge of each specialized area and maintain it as a product. But how is it different from the data lake we normally know?
Data Mesh v.s. Data Lake？
What is the difference between data mesh and the data lake we are familiar with? In simple terms, a data mesh is a decentralized data platform, but a data lake is a centralized management platform. In addition, there are several differences:
Structure and Management
Data in a data lake is concentrated in one large pool, typically managed in a cloud or ground environment centrally , and managed by a dedicated data engineering team. But the data mesh is “decentralized management and storage,” as mentioned earlier, leaving it to each domain to manage its own data products.
Data Processing and Use
The data processing method of the data lake is to clean, transform and ETL in a centralized space, and data consumers need to extract data from the data lake for analysis and machine learning applications; However, within the data mesh, departments within the enterprise can directly use the data products provided by various fields, reducing the workload of data extraction and processing.
Therefore, from the perspective of data structure management, processing and use, data mesh and data lake represent two different data management methods. The former centrally stores and manages data, which is suitable for large data sets requiring unified storage and centralized processing. The latter decentralizes data management responsibilities and is suitable for business environments that require agility and rapid response. So, what kind of data management method should be chosen to enhance the flexibility of managing these data and maximize its benefits?
How to choose suitable data structure for an enterprise？
Choosing the right data structure is crucial for an enterprise to achieve its business goals.
Through the observation from the daily operation of a company, that enterprises should consider the following 6 keys when choosing a data structure:
Assess business needs and goals
Firstly, companies need to have clear business needs and long-term goals when managing data. For example, does the company need to deal with large-scale data sets? Do you need to respond quickly to a rapidly changing market? Understanding these requirements helps determine the core capabilities that a data structure should have.
Analyze existing data management challenges
Analyze the existing data management systems comprehensively can help find out the bottlenecks and pain points in structure design and management. Often, when organizations adopt centralized data management, there may be issues related to data processing delays and inconsistent data quality. Identifying these challenges allows organizations to choose the right data structure.
Assess organizational structure and technical capabilities
Different data structures have different characteristics when it comes to data governance and compliance, and organizations need to consider their own data privacy, compliance, and security needs. Among them, data mesh emphasizes federal data governance and is suitable for enterprises that need flexible data governance strategies. Data lakes, on the other hand, are more suitable for enterprises that need to strictly control data compliance.
Assess organizational structure and technical capabilities
The organizational structure and technical capabilities of the enterprise are also important considerations in choosing a data architecture. If the enterprise has multiple business units and each department has its own data requirements, data mesh model may be more suitable because of its emphasis on domain-driven design and self-service data platforms. But if you want to centrally manage your data and technology resources, data lake might be a better fit.
Consider data usage and processing requirements
Taking into account the frequency, scope and processing needs of data usage, it is important to truly choose the right data structure, which is also an important key to the alignment with the business objectives of the enterprise. If enterprises need to frequently extract insights from large amounts of data and perform real-time analysis, the centralized storage and processing capabilities of the data lake may be more advantageous. If the enterprise needs different departments to use data flexibly and quickly, the decentralized management model of data mesh can better meet the needs.
In summary, the right data structure should be based on the enterprise’s basic business needs, existing challenges, data governance and compliance requirements, organizational structure and technical capabilities, and data usage and processing needs to find valuable insights from the data.
Nextlink’s professional data analytics team and cloud structure technology evaluate the most suitable data structure for you, deploy the data structure that meets your business benefits, and build all types of data analytics at once!
