You’ve likely heard the terms data fabric and data lake used in the context of data architecture and data infrastructure. But what is the difference between a data fabric and a data lake? We break it down for you and compare the two below.

What Is a Data Fabric?

A data fabric is a set of services and architectures that deliver reliable capabilities across data environments. The architectural aspects of a data fabric require the standardization of data practices across data storage platforms and devices. A data fabric can help you address rapid changes in technology and reduce disruption. The adaptable nature of a data fabric can help you create a data management strategy with augmented data integration.

A data fabric is data platform agnostic. This means it works on different deployment platforms and with all kinds of data processing methods. It facilitates the use of data as a strategic asset by abstracting complexity. Using a data fabric, you can combine, access, share and govern practically any data on nearly any platform from virtually any location.

This data fabric reference architecture offers a framework for how a data fabric integrates and connects an organization’s data intelligently and efficiently.

What Are the Major Benefits of a Data Fabric?

A data fabric reduces costs and increases productivity, with a faster time to value. Key benefits include:

  • High speed:

Makes trusted data accessible faster. This limits data silos and accelerates self-service data discovery and analytics.

  • Programmable:

Automates data engineering tasks and augments data integration to deliver real-time insights.

  • Quality control:

Leverages active metadata to improve data quality, data curation, data classification, policy enforcement and more.

  • Flexible:

Helps you prepare your jobs for nearly any environment and virtually any data volume with automated workload orchestration.

  • Optimized data workflows:

Allows consumers to find, understand and trust data with automated data discovery and enrichment.

What Is a Data Lake?

A data lake is a centralized repository that stores data regardless of data sources or formats. A data lake enables you to store data in multiple forms: structured; semi-structured or unstructured; and raw or granular.

A data lake helps organizations manage petabytes of big data. In a data lake, companies can discover, refine and analyze data with batch processing. This is useful for artificial intelligence (AI), machine learning (ML) and data science use cases.

This data lake reference architecture blueprint provides a general framework for implementing a data lake to extend existing data architectures and expand analytical capabilities.

What Are the Major Benefits of a Data Lake?

When looking at a data lake, some of the biggest benefits include:

  • Quick and flexible data ingestion:

Nearly every industry has large amounts of unstructured data. A data lake provides the means to quickly and easily store and manage petabytes of disparate data.

  • Scalability: 

Industries that dealt with terabytes of data just a decade ago are now on the verge of dealing with petabytes. Data lakes can handle colossal volumes of data — and, since they live in the cloud, they can expand with the needs of your business.

  • Productivity and accessibility: 

High-quality data and analytics can inform better policies, illuminate opportunities and demonstrate how resources can be efficiently used. Data lakes provide a means to rapidly store unstructured data before it becomes available for analytical tasks. In addition, many data lake technologies are open source, making them generally affordable.

  • Sharper decision-making: 

If you don’t store data, you can’t glean insights from it. Data lakes allow you to make better decisions from insights derived from both structured and unstructured data.

  • Better data science: 

Scientists and engineers need access to data. A data lake gives them more information to work with and analyze than traditional forms of data storage. AI and ML can benefit from pulling data from a data lake, as they rely on the quality of the data input into them.

How Can You Decide Between a Data Fabric vs. Data Lake?

Deciding between a data fabric and a data lake depends on specific data management requirements and use cases.

A data fabric accelerates self-service data discovery and analytics. It does so by making trusted data accessible at a faster rate to your data consumers. It also automates data engineering tasks, data governance / workflow orchestration and the linking of discovered data assets. A data fabric helps you avoid the challenges that come with traditional data management. This includes data dependency, which leads to higher latency. It also may require significant time and effort to clean and re-run any failure(s) during data processing without a data fabric.

If you have large and complex data management needs that include multiple systems, platforms and locations that require faster data-driven decision-making, then a data fabric is a good choice.

A data lake stores a large amount of data from various sources within or outside of the organization for further processing through a data warehouse. Data can also stay in a data lake for future analysis by AI / ML. If the primary goal is to store and analyze large volumes of raw and unstructured data for advanced analytics and data exploration purposes, a data lake may be a more appropriate choice. Data lakes offer flexibility in storing diverse data types and schema-on-read capabilities for agile data analysis.

Can a Data Fabric and a Data Lake Co-Exist?

Data fabrics and data lakes can co-exist and complement each other in an organization’s data ecosystem. A combination of a data fabric and a data lake can help an organization achieve holistic data management as part of its modern data architecture. The data lake can serve as a scalable storage foundation, while the data fabric provides seamless data access through the necessary integration, governance and real-time processing capabilities.

Take a Holistic Data Management Approach Using Data Fabric and / or Data Lake

The comprehensive Informatica Intelligence Data Management Cloud (IDMC) platform helps in building a strong foundation for your specific use cases, whether you chose a data fabric, a data lake or both.

Powered by AI, IDMC connects, unifies and democratizes your data to advance your business outcomes. Visit the Informatica IDMC page to learn how you can accelerate your data management initiatives.