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Data fabrics and data meshes help businesses manage and analyze data more quickly and efficiently. They’re two different approaches to data management that work to make more accurate data available to business users more quickly. Data fabrics are designed to break down information silos, while data meshes are structured to reduce bottlenecks in businesses’ data analysis procedures. While they can both be implemented in a single organization, they have different goals.
This guide analyzes the benefits of data fabrics and data meshes, as well as potential drawbacks and barriers to implementing them.
What is a data fabric?
A data fabric is a data management architecture that uses automated, intelligent systems to connect data stored in multiple places and in multiple formats. By extracting data from multiple storage sources and centralizing it, data fabric allows teams to study the compiled data holistically, providing better insights.
A data fabric is designed to be flexible, standardize data management, analyze information, and help teams make wiser business decisions. For example, a single enterprise might store data in a database, a customer relationship management (CRM) system, and a networked attached storage (NAS) array. Implementing a data fabric would allow teams to get a better understanding of all of that data and prevent silos among the three systems.
Typically, a data fabric will include multiple solutions that work together. A full-featured fabric will integrate more than one provider’s data management or storage solutions—for example, you could use Talend’s Data Fabric platform to integrate data from your MongoDB database and NetSuite platform. It could also pull information from data lakes, data warehouses, and applications.
Data fabric platforms unify information from such disparate sources using application programming interfaces (APis) and other integration technologies to pull data from applications like Google Drive, databases like Microsoft SQL Server, and data warehouses like Amazon Redshift.
How Does a Data Fabric Work?
A full-featured data fabric includes the following features:
- Support for multiple formats of data storage. Ideally, this should include both structured and unstructured data, as most enterprises have both.
- Data ingestion and integration capabilities. Raw data must be collected from all relevant applications and transported to a single location, and then all the data must be integrated for analysis.
- Multiple network pathways. With data moving in so many different directions, it’s important to have more than one path for information to travel or it could bog down the network, creating increased latencies.
Data fabrics use ingestion and integration to gather and analyze both structured and unstructured data. Ingestion technologies include extract, transform, and load processes (ETL) and SQL commands using ingestion tools like Apache Kafka, Databricks, and Amazon Kinesis. Automated ingestion is preferable to manual because it reduces potential human errors.
Data fabrics also need to integrate data, or clean and analyze it all together once it’s been ingested into one core location, like a single warehouse or lake. One of the key components of a data fabric is eliminating silos. If your business has some customer data stored in SAP but other data residing in Salesforce, you might not have an accurate picture of customer demographics until all that data is combined. Potential problems include duplicate data and inaccurate or outdated information.
Learn more about using data fabrics to drive data management.
What is a Data Fabric Used For?
Data fabrics are ideal for businesses that store data in many different locations, particularly large enterprises with multiple databases and other storage systems. Data fabrics can also benefit big data operations because they centralize large volumes of information. For data fabrics, flexibility and agility of data is critical—to quickly analyze information from multiple sources a data fabric must move the data between storage systems efficiently.
One disadvantage of data fabrics is simply the effort required to set them up. It can take months to integrate all of these storage solutions and establish data governance best practices so the data being analyzed is high-quality and accurate. This could be especially challenging for small businesses or organizations with small business intelligence or data teams.
What is a Data Mesh?
A data mesh is a data management architecture that decentralizes data analytics from a single source so it’s readily available to multiple departments. A data mesh:
- Focuses on data as a first-class product. Data should be well stewarded, protected, and valued.
- Categorizes data based on the relevant business sector. This doesn’t automatically mean all human resources data is siloed from other business data, but it does mean that the HR information is together.
- Gives access to the business user closest to the data. CRM data should be readily available to sales teams, for example, while accounting data should be readily available to the finance department.
A business implementing a data mesh might have a single data lake for all structured and unstructured data, but classify the metadata in a way that makes category searches easy. The data should also be regularly examined for accuracy and cleanliness—for example, deduplicated. Each team would have its own account within the business’s data management software, which it could use to search relevant data.
Data access includes analytics that help users understand information relevant to their jobs. For example, in a traditional enterprise data architecture, a marketing team would submit a request for a dashboard to the business intelligence team, and the BI team would build the dashboard when the request came up in the queue—but what if the marketing team needs the data immediately for a critical campaign?
Data meshes make data directly available to the appropriate team so it can make decisions more quickly. Removing the bottleneck caused by having just a single analytics team improves overall efficiently, removing some manual work, simplifying data analysis, and potentially even increasing revenue. The ability to act on data immediately is critical for many sales, web, and technology teams.
Data meshes are also focused on data as a product. This just means that data is treated as a product, rather than a broad or vague concept. Data should be well stewarded, protected, and valued, and straightforward to access and use.
A data mesh needs:
- Clear governance expectations. All teams should know who is in charge of the specific data for their department and how that person will manage it.
- Access controls. For security purposes, only those who truly need to view or edit the data should have access to it.
- Quality assurance. Data should be cleaned and organized so it’s useful and accurate for the teams that need it.
Learn more about the best data governance tools for managing large data sets.
How Does a Data Mesh Work?
Unlike most storage technology, a data mesh is a general approach to enterprise data availability rather than a specific implementation of hardware and software. They will look different depending on individual organizations’ approaches.
First, all teams should have domain knowledge and ownership of data. This takes time to teach and cultivate, but key team members should learn how to read charts and graphs, understand what data is important, and know how to keep the data clean and organized.
Teams should also have secure data access methods. Examples include single sign-on (SSO) and multi-factor authentication (MFA). Enterprises need to set strict access controls so only users that explicitly need data to do their jobs can view or edit it.
How can business users easily find data? In a data warehouse or database, where data is structured, it should be easy and logical to query. In object stores and other unstructured data environments, the metadata should make sense and be easily searchable.
Learn more about security practices for stored data.
What is a Data Mesh Used For?
Data meshes are convenient for all business departments and teams because they remove existing bottlenecks to important information. Teams that can benefit from a data mesh architecture include:
- Sales
- Marketing
- Editorial
- Search engine optimization
- Paid media
- Engineering
- Product
- Social media
- Information technology
Although teams perform different functions within a business, most of them need accurate, organized data to make decisions. Because data meshes approach information as a first-class product, they acknowledge just how important data is to business operations. Data is no longer an afterthought in the enterprise world—it’s a top priority.
One potential drawback of data mesh architectures is data security. If multiple teams have access to company data, that can be dangerous for security protocols and compliance. The more people who can handle sensitive information, the higher risk of a security breach. While methods like strict identity and access management can protect data, it still presents a disadvantage to enterprises—one that can be mitigated but will take time to navigate.
Data meshes are also challenging because they require each team to have members who can manage data and its associated technology. For example, each team might need someone who can create dashboards and knows how to use data cleaning tools. For this reason, data meshes may be challenging for most small and medium businesses to implement successfully, only because they don’t have enough employees.
Bottom Line: What do you Need to Know?
Both data fabrics and data meshes are useful data architectures for businesses. It’s possible for organizations to use both, but they would need to determine when to centralize data (a fabric) and when to distribute it to different teams (a mesh). Each approach is beneficial but requires careful planning and data protection methods. Is your business considering a data management solution? Read more about the best data management platforms next.
