The nature of digital information these days can be described in two words: big data. The kind of data that inundates many businesses today is large in volume and generated almost instantaneously from various sources. From all the posts that people send out on social media platforms and every online transaction performed by online shoppers to all the logistical data generated by ships and planes plying trade routes as well as weather patterns that are monitored by weather forecasting bureaus—all these comprise information that are generated virtually every second, round-the-clock.
The way that such immense data is being handled has changed the way businesses and organizations operate because traditional methods typically can no longer cope with the demands. While in the past, decision makers enjoyed the luxury of time in collecting data and analyzing it for business forecasting or other planning purposes, today the name of the game is speed in terms of business intelligence.
Data Replication Solutions
For such information to be sorted out and analyzed quickly, an equally efficient method of copying data has to be used. This task is important because taking data at its source is not only difficult but also potentially risky in terms of disrupting normal business operations or organizational processes. To address this need, modern data replication software no utilize techniques such as log-based change data capture or CDC.
With this solution, real-time data is replicated almost instantaneously and transferred to a separate business intelligence database, where it can then be freely processed, used, manipulated, or sorted for analysis. Log-based CDC is the preferred technique for achieving almost real-time data replication, because it is more adaptive and efficient compared to other options such as event or process-based triggers.
A log of changes is used to update the database and to give users a historical background on such updates. Real-time data replication does not necessarily entail copying primary data itself—just points of update such as log changes. This way, replication is much faster and accurate as well.
Handling and Storing Data
In traditional data management systems, information generated from sources are generally treated historically and stored in data warehouses. As needed, this information is then accessed and run through analytics in order to come up with reports that are used by business owners and management in deciding directions for everything from sales and marketing to supply chain management and customer service.
Obviously, this set-up is no longer very effective with the nature of big data these days. Imagine, for example, how streams of information can is created and received in a smart city that is reliant on the Internet of Things or IoT, which is the interconnectedness of various devices, machines, and computers. In any given moment, there is an interplay between analog information from sources such as surveillance, assets monitoring, or data observation and the need to translate these into actionable decisions and processes.
Data Lake Solution
These are the primary considerations in businesses adopting a new way of handling and storing data, through “data lakes.” As opposed to traditional data methods, which are highly structured, tedious, and historical in nature, data lakes are more flexible and responsive to data analysis needs. A data lake is able to retain all types of data direct from various sources, even non-traditional data such as web server logs, social network activity, texts, and images. These are then accessed by users who may use them for certain analytical purposes.
Techniques such as log-based CDC are vital in refreshing the data in the lake to ensure that users have the latest information at hand from various sources on the business operations side. Change data capture also gives users an understanding of the changes that have occurred to the data over time. These aspects are important in generating more accurate and useful insight for business analysis and decisions.
The Future of Data Management
The data lake is such an apt metaphor for the nature of data in this digital age and IoT—a body of information that people can easily dive into or gaze at in order to glean all the necessary information that they need to know, in order to make more effective and efficient business decisions in the fast-paced consumer and business environment today. By all indications, data lakes are the future of data handling, storage, and analysis that all businesses and enterprises should utilize.
