Data Mesh vs. Data Lake: Which to Choose

A proper data management strategy is critical for businesses that want to gain valuable insights into customer behavior and improve operational efficiency. IBM reports that about 80% of an organization's data is unstructured. As a result, many choose data lakes because of their flexibility. But what if you need more control over the data access? A data mesh might make more sense. 

However, finding the right approach—data mesh versus data lake—isn't that straightforward. You must evaluate the strengths and weaknesses of each model. At the same time, you need to consider your organization's data volume and variety, structure, budget, team expertise, and future goals, among other factors. But you're in luck. This post will guide you through everything you need to consider to make the right decision. 

Let's start by exploring the differences between data mesh and data lake and how they compare to one another. 

What Is a Data Lake?

A data lake is a central hub that stores all the raw data a business generates, regardless of which department, instance, or action the data comes from. This data is ingested into a standardized and centralized framework, where it stays until you're ready to process it for analysis. 

Key Characteristics

A data lake is built on four fundamental elements: 

• Support for various sources and formats: It can store anything from customer feedback from social media to audio recordings from customer service calls. All the data—whether it's unstructured data, binary data, or any other type—coexists in the central lake in its original format.
• ELT process: Organizations can pull source data into a data lake through batch processing, streaming, or direct loading when the dataset is simple or small. Transformation happens afterward in a governed fashion.
• Lack of hierarchies: It doesn't follow a schema-on-write approach (i.e., there's no predefined structure on the data upon ingestion). Instead, the schema is defined after the data is stored, at the time of use.
• Universal accessibility: It serves all users, including operational users who require basic reporting and KPI checks, as well as data engineers and scientists who perform deeper analyses.

Advantages

Due to their centralized architecture, enterprise data lakes benefit organizations working with large volumes of data. They provide the following abilities and benefits: 

• enable real-time data ingestion and analysis, facilitating quick decision making
• offer unlimited querying methods, allowing users to run queries directly on data without moving or transforming it first
• help eliminate data silos with a unified view of data across the organization
• keep costs down with cloud-based solutions that reduce hardware expenses and simplify the storage of raw data

Challenges

Despite their flexibility and scalability, implementing a data lake poses a few challenges: 

• can lead to the accumulation of low-quality information, impacting the accuracy and reliability of subsequent analyses
• hinder insights for users with limited technical expertise
• risks turning into a data swamp without proper management, making data inaccessible or unusable due to disorganization

What Is Data Mesh?

Now that you understand the basics of a data lake, let's compare it with a data mesh. 

A data mesh divides stored data across business areas, promoting domain ownership. An internal team of experts manages one or more data domains and establishes standards for workflow and data delivery for end users. 

Key Characteristics

Data mesh emphasizes structure over technology following four principles: 

• Decentralized data management: Teams define what constitutes a domain within their company and create interfaces with clear contracts for sharing data.
• Dynamic topology: Each domain oversees its own data and governance while operating under broader company policies.
• Data as a product: Data is considered a product (accessible and high-quality datasets) that can be consumed by third parties (other teams or departments).
• Interoperability: Teams use a self-service platform to manage their resources independently, featuring encryption and version control of products and schemas to facilitate data handling.

Benefits

Data mesh helps companies break the traditional practice of treating data and its architecture as short-term projects. Let's see some of its benefits. 

• Facilitates scalability because it distributes the workload among specialized teams, allowing each to use the tools that best fit their evolving needs
• Reduces bottlenecks as domain teams ensure data reliability, which minimizes delays and the risk of data silos
• Empowers users to access all relevant data through a self-service model, eliminating support tickets and wait times for information
• Improves data security through distributed ownership

Potential Drawbacks

The implementation of a data mesh comes with its fair share of challenges: 

• Ensuring data quality, since inconsistencies in quality practices across different domains can affect the overall integrity of the data
• Can require a change in organizational culture toward greater collaboration and shared responsibility in most enterprises
• Addressing the cost and complexity of building a data platform that supports distribution and autonomous access, which can be especially challenging for businesses working with different architectures, data models, and consumption requirements

Comparing Data Lake and Data Mesh

There are many differences between a data lake and a data mesh. Let's explore these among essential aspects including architecture, data governance and data ownership, scalability and flexibility, and suitability for different business environments. 

Architecture

A data lake architecture is quite simple: it features a flat and centralized structure built on cloud platforms like AWS or Azure, though sometimes it can be on-premises. All data is stored in a single location, allowing anyone in the organization with permissions to tap into the system and use the data they need. 

In contrast, in a data mesh architecture, data is distributed across various business areas (marketing, sales, customer service, etc.). The owners are responsible for creating data products that can then be shared with other domains. Think dashboards, reporting tools, or APIs with documentation. 

Governance and Data Ownership

Data lakes involve a traditional centralized governance, so there's only one team in charge of the data, permissions, quality, and compliance. 

On the other hand, data mesh promotes decentralized ownership. Each designated team controls their data, how they model it, and how to share it. However, it works under a federated governance, so while teams have the freedom to manage their data, all domains follow a set of shared governance policies. 

Scalability and Flexibility

In a data lake, you can easily add storage to scale, but more often than not you'll run into performance bottlenecks when data volume increases and query times slow down. Additionally, its centralized nature limits flexibility. 

In a data mesh, teams scale their data products independently, so they can respond faster to changes whenever business priorities shift. 

Suitability for Different Business Environments

Data lakes work best in companies with unstructured data coming from multiple sources, like social media, IoT devices, and CRMs. To implement a data mesh, any organization is valid as long as it has distributed data and cross-functional teams. It's mostly used by tech startups like Airbnb because they provide more agility and adaptability than data lakes. 

How to Choose between Data Mesh and Data Lake

So, now it's time to actually pick one! Going for a data mesh is a good option if your organization 

• Deals with various data types and sources,
• Seeks agility and responsiveness,
• Has skilled teams with domain expertise and strong backing from executives,
• Promotes collaboration among teams or wants to drive significant organizational change,
• Has modern cloud infrastructure, and
• Has the capacity to invest in new technologies and training, and to restructure existing teams and processes.

Otherwise, it's best to stick to a data lake if your organization 

• Manages great volumes of semi-structured and unstructured data,
• Wants to maintain a single source of truth for reporting and compliance,
• Has limited financial resources to invest in new technology,
• Isn't ready to invest in cultural changes, and
• Relies on legacy systems that aren't ready for complete overhaul.

Improve Your Data Management Strategy With Data Observability

A data mesh is ideal for organizations that clearly understand their critical data and how they intend to use it. In contrast, data lakes are ideal when you have large volumes of potentially valuable data but aren't sure how to leverage it yet. However, you won't be able to maximize your data's potential without a framework to monitor the health, accuracy, and reliability of your data pipeline. 

Acceldata Observability Platform delivers extensive insight into your data pipelines, regardless of the architecture you choose. It enables automated detection of anomalies, root cause analysis, and predictive analytics throughout the entire lifecycle of your data to ensure quality and keep your operations running smoothly. 

FAQs

What Is a Data Mesh Good for?

A data mesh is ideal for large organizations with complex data ecosystems where different departments generate different types of data. For instance, marketing and sales teams can independently manage their customer insights. 

Is a Data Mesh a Data Warehouse?

No. A traditional data warehouse follows a unified approach, while a data mesh promotes the independence and autonomy of teams when managing data. 

What Is the Difference Between Databricks and a Data Lake?

Databricks is an analytics platform for processing Big Data and machine learning. On the other hand, a data lake is a storage repository. 

‍This post was written by Tatiana Barrie. Tatiana is a technical writer with a background in law and software development. Combining her in-depth knowledge of the technology industry with a flair for communication, she excels in producing accessible, user-friendly, and data-driven content for software, hardware, and scientific research sectors.