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How Does Fabric Solve the Challenges of Data Silos?

Realizing the need for digital transformation is just not enough. It’s time to move on; time to embrace change and optimize digital structure at all layers of the enterprise landscape. Of late, businesses have adopted data fabrics in their management practices and created new data layers for workloads. At this rate, the market value could touch USD 4546.9 Mn by 2026, thereby making the technology accessible to everyone. 

As we all know, fabrics address many challenges. They eliminate the manual dependencies and empower data scientists to focus on other core tasks. One of the major issues solved by fabric is the uncertain volume of incoming data from multiple sources in silos. 

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Alluxio Use Cases Overview: Unify silos With Data Orchestration

This blog is the first in a series introducing Alluxio as the data platform to unify data silos across heterogeneous environments. The next blog will include insights from PrestoDB committer Beinan Wang to uncover the value for analytics use cases, specifically with PrestoDB as the compute engine.

The ability to quickly and easily access data and extract insights is increasingly important to any organization. With the explosion of data sources, the trends of cloud migration, and the fragmentation of technology stacks and vendors, there has been a huge demand for data infrastructure to achieve agility, cost-effectiveness, and desired performance. 

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How Cloud Has Impacted The Centralization vs. Decentralization Of IT

How cloud affects IT infrastructures

Every week, we find ourselves having a conversation about cost optimization with a wide variety of enterprises. In larger companies, we often talk to folks in the business unit that most people traditionally refer to as Information Technology (IT). These meetings usually include discussions about the centralization vs decentralization of IT and oftentimes they don’t realize it, as we are discussing cloud and how it’s built, run and managed in the organization.

Centralized IT

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What Is a Data Pipeline?

You may have seen the iconic episode of "I Love Lucy" where Lucy and Ethel get jobs wrapping chocolates in a candy factory. The high-speed conveyor belt starts up and the ladies are immediately out of their depth. By the end of the scene, they are stuffing their hats, pockets, and mouths full of chocolates, while an ever-lengthening procession of unwrapped confections continues to escape their station. It's hilarious. It's also the perfect analog for understanding the significance of the modern data pipeline.

The efficient flow of data from one location to the other - from a SaaS application to a data warehouse, for example - is one of the most critical operations in today's data-driven enterprise. After all, useful analysis cannot begin until the data becomes available. Data flow can be precarious, because there are so many things that can go wrong during the transportation from one system to another: data can become corrupted, it can hit bottlenecks (causing latency), or data sources may conflict and/or generate duplicates. As the complexity of the requirements grows and the number of data sources multiplies, these problems increase in scale and impact.

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What Are Data Silos?

A data silo is a collection of information in an organization that is isolated from and not accessible by other parts of the organization. Removing data silos can help you get the right information at the right time so you can make good decisions. And, you can save money by reducing storage costs for duplicate information.

How Do Data Silos Occur?

Data silos happen for three common reasons: