Big Data refers to very large and often complex data sets, so massive in size that they’re beyond the capability of managing with traditional software tools. The bulk of Big Data is composed of unstructured data types such as video, photos, audio, webpages, and multimedia content.
At the highest level, working with big data entails three sets of activities:
Integration: This involves blending data together – often from diverse sources – and transforming it into a format that analysis tools can work with.
Management: Big Data has to be ingested into a repository where it can be stored and easily accessed. Most Big Data is unstructured, which makes it ill-suited for traditional relational databases, which require data in tables-and-rows format. Transforming unstructured data to conform to relational-type tables and rows would require massive effort.
That’s why non-relational databases such as MongoDB Atlas -- which are inherently designed to handle unstructured inputs -- are a great fit for Big Data, especially in the cloud. Typical cloud environments supply the kind of concurrent processing capabilities and elastic scalability required for efficient Big Data processing.
Analysis The return on the Big Data investment is a spectrum of valuable business insights including details on buying patterns and consumer preferences. These are uncovered by analyzing humongous data sets with tools powered by AI and machine learning.
Companies collect the Big Data they need in a myriad of ways, such as:
Big Data has three distinguishing characteristics:- volume, velocity and variety. These are known as the three V’s of big data.
Data isn’t “big” unless it comes in truly massive quantities. Just one cross-country airline trip can generate 240 terabytes of flight data. IoT sensors on a single factory shop floor can produce thousands of simultaneous data feeds every day. Other common examples of Big Data are Twitter data feeds, webpage clickstreams, and mobile apps.
The tremendous volume of Big Data means it has to be processed at lightning-fast speed to yield insights in useful time-frames. Accordingly, stock-trading software is designed to log market changes within microseconds. Internet-enabled games serve millions of users simultaneously, each of them generating several actions every second. And IoT devices stream enormous quantities of event data in real-time.
Big Data comes in many forms, such as text, audio, video, geospatial, and 3D, none of which can be addressed by highly formatted traditional relational databases. These older systems were designed for smaller volumes of structured data and to run on just a single server, imposing real limitations on speed and capacity. Modern Big Data databases such as MongoDB are engineered to readily accommodate the need for variety – not only multiple data types, but a wide range of enabling infrastructure including scale-out storage architecture and concurrent processing environments.
Big Data can address a range of business activities from customer experience to analytics. Here are some examples:
Machine learning: Big Data is a key enabler for algorithms that teach machines and software how to learn from their own experience, so they can perform faster, achieve higher precision, and discover new and unexpected insights.
Product development: Companies analyze and model a range of Big Data inputs to forecast customer demand and make projections as to what kinds of new products and attributes are most likely to meet them.
Predictive maintenance. Using sophisticated algorithms, manufacturers assess IoT sensor inputs and other large datasets to track machine performance and uncover clues to imminent problems. The goal is determining the ideal intervals for preventive maintenance to optimize equipment operation and maximize uptime.
Companies and organizations across all fields and industries are flooded with immense quantities of information every day. These data stores represent a treasure trove from which sophisticated analytics can unearth game-changing answers, insights, predictions, and projections. Here are just some of the many benefits Big Data can bring to companies and individuals:
Quickly find the root causes of equipment failures and problems
Learn who your best customers are and what they want
Generate focused and targeted campaigns geared to customer buying preferences
Strengthen customer relationships and loyalty
Rapidly reassess portfolio risk
Out-compete much larger businesses
Quickly adjust product pricing to changing customer demand
Make supplier networks function more efficiently
Facilitate accurate, detailed electronic health records
Speed the processes of medical and pharmaceutical research
To hone their edge in low-margin competitive markets, manufacturers utilize Big Data to improve quality and output while minimizing scrap. Government agencies can employ social media to identify and monitor outbreaks of infectious disease. Retailers routinely fine-tune campaigns, inventory SKUs, and price points by monitoring web click rates that reveal otherwise hidden changes in consumer behavior.
Big Data means new opportunities for organizations to create business value — and extract it. The MongoDB NoSQL database can underpin many Big Data systems, not only as a real-time, operational data store but in offline capacities as well. With MongoDB, organizations are serving more data, more users, more insight with greater ease — and creating more value worldwide.
Read about MongoDB's big data use cases to learn more.
Selecting the right big data technology for your application and goals is important. MongoDB offers products and services that get you to production faster with less risk and effort.