If digital ecosystems had a globe, it would be covered completely by an ocean of data. For some, the continuous stream of newly generated data is a goldmine of valuable information. On others, big data weighs like a horde of locusts that eats up their resources. There is, however, a lot of space in between these two extremes.
You might not be able to transform every byte of your data into valuable information, but you don’t have to drown under streams of data. Read on to what big data really is and how you can use it to your advantage.
What Is Big Data?
According to Gartner, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
The term big data applies to huge amounts of data that are too complex and large for traditional data processing applications.
The big data field was developed in response to the need for better methods of managing big data, including:
- Data capture—the process of identifying objects, then collecting and entering related data into the system. Automatic Identification and Data Capture (AIDC) methods are used for automating the data capture process.
- Data storage—the process of recording data in a digital, machine-readable medium such as on-premise data centers and cloud-based Virtual Machines (VM). A cloud migration strategy serves organizations looking to move data from one storage location to another.
- Data analysis—the process of evaluating raw data (which is often meaningless) for the purpose of extracting meaningful information. Predictive analytics and user behavior analytics systems provide data analysis insights that cut down the analysis time.
- Data visualization—the process of creating and studying visual representations of data. Data visualization tools such as spreadsheets, graphic design software, and interactive data visualization software enable the creation of visual aids such as statistical graphics, plots, and infographics.
In addition to the four main functions explained above, the big data field makes use of advanced capabilities for searching, sharing, transferring, updating, and securing the data.
The Five Vs of Big Data
Each of the following terms is used to assess the big data’s level of complexity. The five Vs are factors that need to be taken into consideration when assessing the needs of the project. The first three were originally conceived by Gartner’s Doug Laney. Value and veracity are relatively new factors that emerged in the past few years.
The amount of data. There is no specific number that classifies the data as big. However, big data often starts in the realm of tens of terabytes. Each user generates approximately 1.7 megabytes per second. Companies like Facebook, which host billions of users, can easily reach the zettabytes or brontobytes.
The speed of the data as it is received (read) and/or as it moves (write) from one location to another. Big data can be analyzed as it streamed in real-time or in increments. The speed at which the data is read and written into the system will influence the analysis capabilities. Big data tools help organizations handle the growing amount of data generated by connected devices and integrated systems.
The type of data. Traditional databases were designed for structured data such as MySQL. Today, there are many more types of architectures, including NoSQL, which handles both structured and unstructured data. Big data projects take into consideration the type of data that needs to be processed and analyzed.
The reliability of the data, measured by its accuracy, consistency, and trustworthiness. Big data ecosystems often take in data from a variety of sources. The data source isn’t always reliable. There are many factors that may corrupt a data source, such as a software bug, abnormalities in sensors, human errors, malware, and misinformation.
The quality of the data. Each big data project should be evaluated according to the needs and means of the organization. For one organization, it might make sense to store and analyze every single byte of data. For another organization, placing part of the data in a data warehouse while analyzing only specific investigative metrics would make more sense, and cost less funds.
Top Trends for Data Storage with Big Data
1. Storage tiering for big data
Storage tiering is the practice of using two or more types of storage mediums. The goal is to cut expenses while ensuring that the needs of the project are met. The process of storage tiering involves the prioritization of data, while taking into account the five Vs explained above. You can then use distributed processing frameworks, such as Hadoop, to distribute the data according to your priorities.
2. Multi-cloud storage for big data
Multi-cloud storage architecture is composed of two or more cloud computing and storage services. Multi-cloud strategies help organizations avoid vendor lock-in while tiering their storage. You can store archival data in a data lake, store and analyze real-time user data in a dedicated cloud, store data in cloud warehouses, while using a dedicated cloud backup service to ensure business continuity.
3. Big data analytics in the cloud
The global economy is in the midst of a digitization process. To reduce the costs associated with managing on-premise data centers, many companies choose to move their digital operations to the cloud. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), are in the process of responding to the needs of their customers, adding features such as real-time analysis, cloud-based data lakes, and scalable event ingestions.
4. Machine learning for big data
Machine learning is the process through which machines learn how to operate—learn and perform tasks—autonomously. During the learning process, machines require huge amounts of data. That makes machine learning a perfect fit for big data, which needs to be processed and analyzed in huge amounts. Today, there are machine learning services and platforms that can be integrated into existing cloud ecosystems, such as Amazon SageMaker and Google’s Cloud Machine Learning Engine.
Big data is as cumbersome as it is useful, and increasingly, the use of large amounts of data is becoming a competitive necessity for many businesses and an essential tool for public organizations and authorities. However, while the information extracted can expand your insights and help improve both public services and private projects, you need to find a way to store it.
There are a number of solutions that have popped up to respond to this demand for big data storage, typically in some sort of cloud environment. These cloud storage solutions provide various options for hot and cold data storage, providing you with the necessary flexibility and scalability to store large amounts of data, while integrating with big data tools such as analytics and machine learning.