Enterprises often proudly proclaim to internal and external audiences that they are “data-driven organizations.” As businesses systematically collect data on consumer, market, supplier, partner and employee activities, the consensus emerges that within the data lies a pathway to superior performance. Organizations that process and analyze the data accurately and quickly may be the first to notice critical developments such as evolving consumer preferences, emergent competitive threats, critical gaps in the supply chain, and many other trends, whether predictable or not. At one time, gathering, processing and acting promptly based upon intelligence from data may have been relatively straightforward for any motivated organization.
But that was before the volumes of data generated by business operations exploded beyond any projection. Nowadays, many enterprises continuously amass vast amounts of structured and unstructured data. This data can originate from diverse sources such as point-of-sale systems, asset tracking and telematics, inventory management systems, employee actions, user-generated content and social media, and external event trackers, among others. A recent study revealed that the average organization uses 7.5 petabytes of storage. And with the escalation of interest in artificial intelligence generating more and more material, and with data often duplicated across multiple repositories, we can reasonably anticipate this massive growth trend to persist.
And in that tremendous scale lies the challenge. Organizations aiming to utilize their extensive and diverse assets face the obstacle of managing the sheer scale of their data estate. This data may reside in legacy relational databases and data marts, as well as in one or several cloud storage providers, data warehouses and data lakes, among other sources. The challenge of gathering, processing and analyzing material from such a broad array of places without compromising quality while maintaining enough speed and responsiveness to provide business value is daunting.
Data Engineering to the Rescue
Organizations seeking to leverage their rapidly scaling data assets require a robust data strategy and a data platform capable of meeting today’s demands and emerging requirements. Data engineering, the discipline focused on collecting, managing and ultimately making data consumable for an organization to use, has surged as data volumes have proliferated. Within data engineering, two critical capabilities stand out as essential.
First, let’s look at data loading, which entails processes that access and transfer data from multiple sources into a single location, such as a cloud data warehouse. Effective data loading tools offer wide[1]ranging connectivity options and the ability to ingest data from diverse sources, including on-premises databases, cloud data lakes and cloud data warehouses, among many others. The most efficient tools also have the advantage of being able to move data with great speed and reliability, but also with little or no coding required.
The second is data replication, involving not just the transfer of data from one location to another but also the duplication and storage of data across multiple locations. Organizations pursue data replication for several reasons: to ensure the constant availability of trusted data for analytics and AI use cases, to reduce data loss risk, or to improve performance, particularly for reducing latency across distant geographies.