How to Transform Big Data to Fast Data?


In this connected world where digital data is constantly increasing from all sorts of sources, methods to make it faster are absolutely necessary. Big data is generated from almost all activities that Generation Z is engaging in. Simply moving around with a smartphone generates data, as well as the sensors that exist in surroundings. But why is it important to transform big data into fast data? Many sectors will use big data in one way or another to improve their services. Some of these are health officials, scientists, corporate leaders, education specialists and – most importantly – government leaders. In this sense, big data must also become fast, besides transparent.

New data architectures

Big data was previously relying on NoSQL databases or similar processing. During these times, architectures were focused on catching and storing information, but not in real time. Data were processed off-line in batches, which took a while. Earlier technologies allow processing data much faster, by reducing the required time frame for writing and extracting data. Real-time processing is now possible through the tools used and architectures selected. If in the past processing times could take hours, they are presently reduced to milli or microseconds.

Data became so easy to manage because of the new data architecture, that has some high-standard requirements. Among these requirements, it’s important to mention different modalities for data ingestion, flexible querying, effective storage, and fast analysis. New architectures rely on reactiveness, resilience, and responsiveness which are strict conditions to transform big data in fast data, that can be used and manipulated in real time.

Fast data infrastructure

In order to ensure fast data, several steps must be adopted. The key principles of a fast data infrastructure include asynchronous data transfer, parallelizing data, flexibility and adopting risks by experimenting. An asynchronous data transfer can be used by adopting distributed-system infrastructures. Backing up such an infrastructure by using a MOM should reduce back pressure so that data can be manipulated much faster.

Parallelizing data can be done by using the right parser. If the conversion between various data formats takes too long, processing big data will be too slow. The optimal parser will help with reducing that conversion time visibly. Including parallel processing is a time-saver for big data, as it removes data duplicates before introducing it into conversion. Experimenting with different infrastructures is required, even though it includes some risks. Keeping the infrastructure flexible and open to new upgrades is also a must. There’s no need to rethink the whole data processing system but finding creative solutions with plenty of capabilities can drastically reduce the times. Improving resource consumption and network transfers through experimentation may lead to great results. The settings that need often modification are the data replication levels and the consistency levels.

Choosing a balanced option will dictate how fast data will become. In memory computing is one of the technologies that brought something mesmerizing to the table – extreme data processing. Gaining instant insights seemed to be unattainable in the past, but in-memory computing platforms instantly outran the obstacles that everyone had to deal with in terms of big data. It transformed big data in fast data by optimizing all features related to data processing: easier installation processes, trained deep-learning models, interactive queries, impeccable data visualization, and the list can go on.

Combining multiple tools

A fast data system should involve both modern and traditional tools. A hybrid system for processing data is the only one that can deliver the fast results that people desire at the moment. The key principles in using data handling tools would be to combine them properly. Traditional disk-based solutions and new in-memory ones should offer optimal results and performance. Combining tools that are developed for certain purposes only will create the perfect environment for fast data processing. Some tools are dedicated to large-scale processing while others focus on real-time computation. By putting them together, the needed performance is obtained and the fast data infrastructure is finally created, but it remains open to potential upgrades in the future.