Voluminous data refers to the huge amount of complex unstructured data. Voluminous data can also be referred to as big data since they provide similar if not same meaning. Big data is defined as data with greater variety that arrives in greater volumes and with greater velocity. This is also referred to as the three Vs.
Simply put, voluminous data, particularly from new data sources, refers to larger, more complex data collections. The size of these data sets makes it impossible for conventional data processing technologies to handle them. However, these enormous amounts of data can be leveraged to solve business issues that were previously impossible to solve.
Big data is essentially the use of extremely big data sets to identify patterns, trends, and insights that support the growth of your firm or business project. Massive data sets from numerous sources must be combined, processed, and analyzed in the modern world in order to make them relevant and valuable. What I mean by “Big Data” and “Big Data analysis” is the capacity to take those complex data sets and make inferences.
Features of voluminous data:
- It covers the major technologies which contribute to realization of internet-scale pattern recognition including parallel computing, artificial intelligence and distributed systems.
- Incorporates two of the major technologies; distributed models and parallel computing which are used in expansion and development of distributed applications in internet-based environments.
- Demonstrate the scalability properties of pattern recognition for information systems.
- Evaluate different approaches for distributed computing like one shot learning and hierarchical approaches.
Voluminous data benefits
With suitable tools, techniques and technologies we can achieve various beneficial outcomes from voluminous data. Some of them are as follows.
- With proper analysis and processing using different tools it makes it easier to obtain complete and accurate answers since there is more data and information.
- Complete and accurate answers lead to better confidence in data and eventually help to explore new and better ways to tackle and solve problems.
Voluminous data challenges
With optimal processing and analysis of voluminous data shows promising results. Though with great results also comes different challenges and it is not different.
As the name suggests voluminous data has huge volume and is always increasing. As you have noticed on an individual level, the amount of data you use and produce in a single day during browsing the internet, capturing photos, through social media, video streaming and so on. Assuming the same amount of data is used and produced by every single person connected through any type of network you can guess the total amount of data that needs to be handled. Although new technologies for data storage have been invented, keeping up with the increasing volume of data is challenging since the volume is increasing exponentially.
That is the first problem to tackle. What happens after storing data? The proper curation, preparation and organization of data is equally important. If the data is unusable there is not any point of storing it. To get good insight and ensure data is relevant to the client or the project it is necessary to clean the data first and perform the required operations on it.
It is also important to notice that big data technologies which are used to handle voluminous data are also evolving rapidly and new technologies are also being developed. Challenges should not outweigh the importance and benefits of the voluminous data. This will give rise to new algorithms and inventions to benefit the whole technological community.
Managing voluminous data
Data has become one of the most important assets for most of the organizations. With exposure of data to more data analytics and data science tools we can achieve what we strive for. As discussed above, it is difficult to maintain an appropriate balance between cost and performance to achieve the highest degree of efficiency.
We will list a few of the tools and technologies that are used to store and analyze voluminous data.
1. Apache Hadoop
Apache Hadoop is developed by Apache Software Foundation initially released on April 1, 2006. It is a collection of open source tools that utilizes the cluster of computing resources to effectively store and solve problems with data with huge volume. Basically, it is based on the MapReduce algorithm.
The base modules of Apache Hadoop Framework are:
It is the collection of basic utilities and libraries that is required by other Hadoop modules. Hadoop offers fundamental functions and services, like the abstraction of the underlying operating system and its file system.
Hadoop Distributed File System (HDFS)
it is a distributed file system meant to run in commodity hardware which provides very high performance and bandwidth across nodes. It is extremely fault tolerant and can be run on low-cost hardwares.
A platform responsible for managing computing resources in clusters and support processes like batch processing, stream processing, interactive processing and graph processing.
an implementation of the MapReduce programming model for large-scale data processing.
An object store for Hadoop which is scalable, redundant and distributed.
NoSQL is highly popular nowadays for storing high volume unstructured data. It provides the facility of sharding of databases over multiple servers which allows to scale the system easily. A new server can be set up and integrate those into the database cluster whenever a requirement arises. Also, enterprises have seen the demand to store unstructured data. NoSQL is more efficient and fast to store and retrieve data in this form than relational databases.
Presto is a distributed parallel query execution engine which is capable of running queries with negligible downtime from gigabytes to petabytes of data. HDFS, MySQL, Cassandra, Hive, and many other data sources can all be processed by a single Presto query. It is built in Java but it is not prone to problems of general Java programs such as memory allocation and garbage collection. Companies like Facebook, Teradata and Airbnb are using Presto.
Voluminous data brings huge possibilities to explore if it can be efficiently handled and analyzed as per requirements. Even now the companies are providing and improving regularly to strive for better services. Google, Meta, Amazon and other companies and even independent individuals are pioneering technological advancements in the field of big data, data mining, data science and artificial intelligence. So, we can expect better use of voluminous data.