Big data is a field that finds ways to analyze and systematically extract information from or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. It takes the form of a collection system and makes it easy to identify and store large data. Big data solutions aid in working with large data sets.
What are Big Data Solutions? Big data solutions are defined as solutions to problems that are directly related to large data sets. Big data solutions can be used to quickly identify and analyze large data sets. These services can also be applied to other types of data sets such as databases and data stores. This type of data management can help solve problems that cannot be tackled through traditional data processing and software.
Companies are attempting to transform and empower their business through the implementation and insights gained from large, structured and unstructured data sets. Simply put, big data solutions are ways to handle these large and complex data sets. These solutions include data collection and storage, data analyses, data mining, machine learning, and data visualizations. Big data solutions help organizations with data problems to identify, map, and classify large data sets. Big data solutions have become a growing area of interest for companies who find increasingly growing needs to extract value from their growing swaths of data. To that end, there are quite a few types of offerings they usually need.
Big Data Solutions: Collection and Storage
Data collection and storage includes extracting data, structured and unstructured, from various sources and storing that data in a secure repository. In order to be fit for use, the data needs to be cleansed to account for missing values, remove duplicates and erroneous data, etc. This is typically a heavy burden for many companies as it is usually a time-intensive effort.
Data analyses is the process of transforming raw data into usable information and decision-making through well-defined queries. This comes in many forms and various tools may be used in order to conduct data analyses. Solutions to these data analyses include many data analytics and analytics solutions to achieve a variety of statistical measures that provide insights into how an organization can improve its performance by performing deep-dive analysis.
Data mining is the practice of examining large databases in order to generate new information and typically employs methods for seeking patterns in the data. For example, data mining can be used for assessing whether movie-goers who like movie X also like movie Y. This can be used to predict the success of a movie by using the popularity rating of the film or rating of the movie by the movie’s critics.
Machine learning is a way for systems to learn from data sets, extensively through a method called supervised learning, without being specifically programmed. In many cases, machine learning is typically used for building data models and making predictions with that data.
Data visualization is a method of representing the data in order to tell a story. Early forms of data visualizations utilized a combination of Microsoft Excel and PowerPoint with bar charts, pie charts, etc. While these tools are still prevalent today, there are now other platforms and tools that can be leveraged to represent data in meaningful ways.
Example Use Cases for Big Data
Use cases vary across industries. Nevertheless, a few examples of challenges address by big data solutions include improving products for manufacturing firms, using clinical information to implement enhanced treatment plans in healthcare, and detecting fraud for credit card transactions.
Big Data Solutions: The Tools of the Trade
Many vendors offer solutions to address big data challenges such as Amazon and Microsoft among others. Solutions range from free to open source platforms to paid tools. There are many tools out there to help you understand the data in meaningful ways, whether it be visualization, video analytics, or even image analysis. These solutions can allow you to identify and visualize the data to create your own unique model, program, or analysis.
AWS Cloud Storage is one of the largest cloud storage solutions available in the world. AWS enables users to manage hundreds of millions of data types for their own private cloud storage solution.
Microsoft Azure is a cloud computing service that leverages Microsoft platforms as well as open source technologies. Azure provides a wide array of storage solutions to enable customers to manage all their data.
Hadoop is a scalable, open source framework for efficiently storing and processing large data sets across computer clusters. Hadoop is especially useful for applications that collect data from multiple disparate sources – which may or may not be in various formats.
Spark is an open source, distributed data processing framework which is considered a leading platform for large-scale SQL, with built-in modules for streaming, graph processing, and machine learning.
The Industry Outlook
The outlook for big data solutions looks promising. This year, companies including IBM, Microsoft, and Google are offering data, storage, and analytics solutions, however, many companies seem to forget how to support their big data platforms- they lack data storage, analytics, or other support in the form of professional services and the data-professionals that most companies actually need to get the job done.
In fact, only a small group of people are actually capable of building big data infrastructure solutions properly, which means only a tiny fraction of the talent is available to deliver good quality and high-performance services for a fraction of the cost.
As a result, there are still some people who may be unable to meet all their business needs in a short amount of time, and this needs to change because the demand for big data solutions has actually grown exponentially.