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Seth Rao

CEO at FirstEigen

Data Mesh vs Data Lake: Understanding Key Architecture Differences, Benefits, and Trustability

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      Data Lake relies on a centralized data repository, while Data Mesh decentralizes data storage and management. 

      It’s a war between different philosophies of data architecture, but your organization will eventually have to choose sides. So which is better for your business? Which is better for quick decisions and which is better for more reliable and trustable data?

      Quick Takeaways

      • A data mesh decentralizes data storage and management across an organization
      • A data lake consolidates all data into a single, centrally managed repository
      • Data meshes enable speedier data analysis and are easier to scale
      • Data lakes are better for handling large amounts of raw data and are easier to secure
      • In both Data Mesh and Data Lake, Data Trust issues are best addressed way upstream and also validated throughout the pipeline

      What is a Data Mesh?

      Where data lakes are monolithic storehouses for data, a data mesh takes the opposite approach. Instead of centralizing data storage, a data mesh decentralizes data, dispersing it – or, more accurately, generating or ingesting it – in multiple connected nodes. It’s the mirror image of the old client/server model, with every node becoming both a client and a server, enabling all nodes to store, share, and access data from across the organization.

      How a data mesh is constructed.

      A data mesh is especially useful for large enterprises that generate lots of data, as well as large-scale applications that run across multiple organizations. It’s, at least in theory, an easier way to manage data in complex environments.

      In a data mesh, each department or location becomes its own data domain, server, and data platform. Each domain is responsible for gathering, storing, and managing its own data. Data is served from each domain to the other domains in the mesh and shared across the organization. This gives business teams and departments complete ownership and control of their data, which can result in speedier data access and faster decision-making. 

      What is a Data Lake?

      A data lake is a central location that stores data of all types and in all formats. It’s a traditional way to store data, by gathering it all in one easily managed and accessed location. Data in a data lake can come from multiple sources, both inside and outside an organization. 

      The centralized design of a data lake.

      Data is ingested into the data lake from both internal and external sources. This data is seldom in a single standardized format and can be both structured and unstructured. This requires data managers to develop processes and technologies to catalog and index the data so that users can access it. This typically involves the use of artificial intelligence (AI) and machine learning (ML) technologies. 

      Data lakes offer many advantages to the average organization. Data lakes are:

      • Relatively simple to construct and manage
      • Cost-effective for storing large amounts of data
      • Familiar to most IT workers
      • Compatible with a wide variety of data quality and data management tools
      • Easy to access by both in-office and remote workers
      • Easy to secure against breaches and attacks

      The key characteristics of data lakes are that they are both singular and centralized. Companies build data lakes to eliminate departmental and geographic data silos that are more difficult to access, manage, and defend.

      Data Mesh vs. Data Lake: How They’re Different

      Data lakes and data meshes take opposing approaches to storing and disseminating data. A data lake centralizes data whereas a data mesh decentralizes it. They are, in both design and execution, different methods of data architecture.

      For the past several decades, enterprises have gravitated towards centralized data storage, whether in old-school databases or more contemporary data warehouses and data lakes. Companies discovered that multiple data silos were more difficult to manage and secure against outside intrusion, which became more important as the number and severity of cyberattacks and breaches ballooned over time. (According to Statista, the number of data compromises in the U.S. grew from 157 in 2005 to more than 1,800 in 2021.) In addition, the growth of the remote workforce over that same time period led companies to create an easily accessible single source of data stored in the cloud. 

      As a result of these and other trends, centralized data repositories, often in the form of data lakes, became the norm. The global market for data lake solutions and services almost doubled from $1.9 billion in 2016 to $3.74 billion in 2020 and is expected to reach $21.82 billion by 2030. 

      Benefits of Data Mesh

      The winds are starting to shift again, however, as many companies realize the benefits of decentralized data distributed over a mesh. A data mesh can do the following:

      • Be easily managed at a local level
      • Scale more easily and cost-effectively than a data lake
      • Enable decentralized organizations to better manage dispersed data
      • Allow individual locations to store and analyze data unique to their operations more easily

      Benefits of Data Lakes

      Data lakes offer a different set of benefits to organizations. A data lake can do the following:

      • Break up data silos and make all data visible and equally accessible to all employees
      • Assemble all of the enterprise’s data to provide more inclusive and comprehensive analysis and insights 
      • Use IT resources more efficiently by eliminating duplicative efforts
      • Create a centralized repository that’s easier to secure against outside attackers

      Data Mesh vs. Data Lake: Which Should You Choose for Your Company’s Data?

      Given the differences between the two approaches to data architecture, which is better for your organization: a data lake or a data mesh? It depends on what you need.

      Choose a data mesh if:

      • You need real-time reporting and analysis
      • You have multiple disconnected systems, especially in disparate locations
      • You need to quickly grow your data management operations
      • You need to rapidly scale your operations
      • You want the power to monitor Data Trustability at a local level as your requirements may be special

      Choose a data lake if:

      • You own large amounts of raw data
      • You have both structured and unstructured data
      • You want to reduce data storage and management costs
      • You want to store your data in the cloud
      • You want to establish Data Trustability upstream so the data cascading downstream has basic essential hygine and trust that more groups can easily “drink” from

      Then there’s a third approach: combine data lakes and a data mesh into a single solution. This approach involves creating separate data lakes in multiple locations or departments but joining them together in a mesh network. For many organizations, this may be the best of both worlds.

      Clean Up Both Data Lakes and Data Meshes with DataBuck

      Data lakes and data meshes are both prone to frequent data errors. When even the most diligent companies monitor less than 5% of their data, large quantities of unmonitored and unreliable data find their way into centralized and decentralized data storage. 

      Whether your company uses a data lake or a data mesh, to get the most value out of your data, you need to monitor Data Trustability.

      FirstEigen’s DataBuck is an autonomous, low-code, data trustability monitoring solution that uses machine learning to monitor your data in real time. You get higher-quality data, which lets you make more informed operating and strategic decisions. Let DataBuck monitor your data mesh or data lake and turn bad data into good data. 

      Contact FirstEigen today to learn more about data quality in data lakes and data meshes.

      Check out these articles on Data Trustability, Observability & Data Quality Management-

      FAQ

      What is the difference between Data Mesh and Data Lake?

      A Data Lake is a centralized repository where large volumes of structured and unstructured data are stored in their raw format. It offers scalability but can lead to bottlenecks in data management and governance. On the other hand, a Data Mesh is a decentralized architecture where data ownership is distributed across domain teams, making them responsible for managing and governing their data. While Data Lakes focus on centralization, Data Mesh focuses on autonomy and governance across different teams.

      What is Data Lake Architecture?

      Data Lake Architecture is designed to store massive amounts of raw data in its native format. The architecture typically involves a storage layer for housing the data, a processing layer to transform and analyze the data, and a governance layer to ensure proper data management. It supports high scalability and flexibility, allowing organizations to handle various data types (structured, semi-structured, unstructured).

      Which is better: Data Mesh vs Data Fabric vs Data Lake?

      It depends on the organization’s needs. A Data Mesh excels in decentralizing data ownership across teams and is ideal for organizations seeking agile, domain-driven data management. Data Fabric serves as a unified architecture that connects disparate data environments, making it ideal for integrating multiple data sources. Data Lakes are best for centralized storage of large datasets, though governance and management may become challenging as the lake grows. Choosing between these architectures depends on factors like scalability, governance needs, and complexity of data sources.

      What are the pros and cons of a Data Lake?

      • Pros:

        • High scalability for storing large volumes of raw data.
        • Flexibility to handle structured and unstructured data.
        • Supports big data analytics and machine learning use cases.

      • Cons:

        • Can become a "data swamp" without proper governance and management.
        • Increased complexity in querying and transforming data.
        • Data security can be challenging in unstructured environments.

      When should I choose Data Mesh over Data Lake?

      You should consider Data Mesh when data management needs to be decentralized across multiple domain teams, enabling each team to have autonomy over its data. Data Mesh is ideal for organizations that prioritize agile data management, where speed and domain-specific governance are crucial. Data Lake is a better option when you need a centralized repository to store and process large volumes of diverse data types.

      How do Data Mesh and Data Lake solve governance challenges?

      Data Mesh addresses governance by assigning data management and responsibility to individual domain teams, ensuring localized governance with a focus on ownership and accountability. Data Lake, being centralized, requires a separate governance layer to monitor, control, and manage access to the raw data stored within it, which can become cumbersome as data grows.

      Can DataBuck simplify data governance in Data Mesh and Data Lakes?

      Yes, DataBuck offers built-in governance tools that can streamline data management by applying continuous monitoring and data validation across all data domains. Whether you are using a Data Mesh with decentralized ownership or a Data Lake with centralized data storage, DataBuck ensures data integrity and governance are upheld across all environments.

      Discover How Fortune 500 Companies Use DataBuck to Cut Data Validation Costs by 50%

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