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Data Lake vs Data Warehouse vs Data Fabric? Understanding Cloud Data Terms

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Our shift to a digital world is fueling the creation of massive data reservoirs with almost unlimited potential. With this increase comes new data terms and technologies for managing and analyzing data. This digital transformation shift can result in companies generating more data than they can manage or utilize with their current infrastructure and resources if not adequately conceptualized.

Currently, we find ourselves firmly in the Zettabyte Era, a term coined back in 2016 recognizing the changeover to measuring the world’s data in terms of zettabytes. A zettabyte is a unit measurement for computing storage capacity, and it represents a whole lot of data. To provide some context, the world’s data is estimated to be just under 100 zettabytes in 2022. While in 1998, for comparison, the world’s data was estimated at just a few thousand petabytes. A zettabyte equals one million petabytes!

As digital transformation and the growth of data have become the norm, business executives must gain a broad understanding of the data landscape in order to take advantage of the business intelligence possibilities. Data management infrastructure can be complicated, and while there is no need for business leaders to become experts in data management, more knowledgeable leaders make better IT investment decisions.

Data Terms: Data Lake vs Data Warehouse vs Data Fabric

Gaining an awareness of data infrastructure terms like data lakes, data warehouses, and data fabric is a great place to start. A big picture overview of these data management technologies can only help in making more informed choices about your firm’s IT infrastructure.

What is a Data Lake?

A data lake is a centralized repository for storing enormous amounts of structured, semi-structured, and unstructured data. Data can be brought into a data lake from multiple and disparate data sources, validated, and optimized to improve access, connectivity, and analytics.

The main benefits of using a data lake are that it allows for cost-effective storage of large amounts of data without having to worry about the data’s format and can improve the functionality of data from multiple sources.

One pitfall of a data lake is that along with the unlimited data consolidation capabilities of the data lake, without the development of an adequate framework for enrichment and enhancement, data within a data lake is no more usable than before.

What is a Data Warehouse?

With a data warehouse data flows in from transactional systems, CRM, operational systems, and other sources, typically on a regular cadence. Business analysts, data engineers, data scientists, and decision-makers access the data through business intelligence tools and other analytics applications.

One key advantage of using a data warehouse is that it enables businesses to consolidate structured data from multiple sources into a single, centralized location to improve reporting and dashboards.

Having clearly defined and robust data governance policies is a requirement for getting the most out of a data warehouse.

What is Data Fabric?

Data fabric is a flexible data architecture that enables the integration of data from a variety of sources and cloud environments. In a sense, it knits together all the data of an organization regardless of the location or infrastructure providing a unified view of an organization’s data, making it easier for businesses to reduce data silos and better manage their data. Additionally, data fabric can help companies save money by reducing the need to duplicate data in multiple systems and providing flexible, agile, and scalable solutions for accessing and using data.

A Simplified View

Among the main differentiators among the three data structures is that data lakes can store raw data, while data warehouses only stores processed and refined data, and data fabric connects one or more of the other structures for better connectivity.

It’s About Business Intelligence

Data lakes, warehouses, and fabric are data technologies that can help businesses reduce silos and provide actionable data necessary in today’s data-driven business environment. Painting with a broad brush, they store (or can access) data in a centralized location, help businesses better understand their data, and reduce the need to duplicate data in multiple systems. Still, they have specific benefits and challenges that must be weighed against your organization’s requirements and business goals.

As with many things, there is no one-size-fits-all solution to data management and how best to gain the business intelligence (BI) needed to increase revenue, improve outcomes, and reduce the total cost of ownership.

Reach out to connect with our technical experts to discover how to optimize and utilize your data for better decision-making. Coretelligent has years of experience building and supporting customized IT infrastructure and solutions utilizing tools like Microsoft Azure, Power BI, Tableau, and other BI tools designed and built around our client’s business goals.

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