What is meant by big data?

The diversity of big data makes it inherently complex, resulting in the need for systems capable of processing its various structural and semantic differences. 

Big data requires specialized NoSQL databases that can store the data in a way that doesn’t require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze seemingly disparate sources of information to gain a holistic view of what is happening, how to act and when to act.

When aggregating, processing and analyzing big data, it is often classified as either operational or analytical data and stored accordingly.

Operational systems serve large batches of data across multiple servers and include such input as inventory, customer data and purchases — the day-to-day information within an organization.

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Analytical systems are more sophisticated than their operational counterparts, capable of handling complex data analysis and providing businesses with decision-making insights. These systems will often be integrated into existing processes and infrastructure to maximize the collection and use of data.

Regardless of how it is classified, data is everywhere. Our phones, credit cards, software applications, vehicles, records, websites and the majority of “things” in our world are capable of transmitting vast amounts of data, and this information is incredibly valuable.

Big data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems. Companies and organizations use the information for a multitude of reasons like growing their businesses, understanding customer decisions, enhancing research, making forecasts and targeting key audiences for advertising.

Big Data Examples

• Personalized e-commerce shopping experiences.
• Financial market modeling.
• Enhanced medical research from data point compilation.
• Media recommendations on streaming services.
• Predicting crop yields for farmers.
• Analyzing traffic patterns to lessen city congestion.
• Retail shopping habit recognition and product placement optimization.
• Maximizing sports teams’ efficiency and value.
• Education habit recognition for individual students, schools and districts.

Here are a few examples of industries where the big data revolution is already underway:

Big Data in Finance

Finance and insurance industries utilize big data and predictive analytics for fraud detection, risk assessments, credit rankings, brokerage services and blockchain technology, among other uses.

Financial institutions are also using big data to enhance their cybersecurity efforts and personalize financial decisions for customers.

Big Data in Healthcare

Hospitals, researchers and pharmaceutical companies adopt big data solutions to improve and advance healthcare.

With access to vast amounts of patient and population data, healthcare is enhancing treatments, performing more effective research on diseases like cancer and Alzheimer’s, developing new drugs, and gaining critical insights on patterns within population health.

Big Data in Media & Entertainment

If you’ve ever used Netflix, Hulu or any other streaming services that provide recommendations, you’ve witnessed big data at work. 

Media companies analyze our reading, viewing and listening habits to build individualized experiences. Netflix even uses data on graphics, titles and colors to make decisions about customer preferences.

Big Data in Agriculture 

From engineering seeds to predicting crop yields with amazing accuracy, big data and automation is rapidly enhancing the farming industry.

With the influx of data in the last two decades, information is more abundant than food in many countries, leading researchers and scientists to use big data to tackle hunger and malnutrition. With groups like the Global Open Data for Agriculture & Nutrition (GODAN) promoting open and unrestricted access to global nutrition and agricultural data, some progress is being made in the fight to end world hunger.

Along with the areas above, big data analytics spans across almost every industry to change how businesses are operating on a modern scale. You can also find big data in action in the fields of advertising and marketing, business, e-commerce and retail, education, Internet of Things technology and sports.

Big Data Tools 

Understanding big data means undergoing some heavy-lifting analysis, which is where big data tools come in. Big data tools are able to oversee big data sets and identify patterns on a distributed and real-time scale, saving large amounts of time, money and energy. 

Here’s a handful of popular big data tools used across industries today.

Apache Hadoop 

A widely used open-source big data framework, Apache Hadoop’s software library allows for the distributed processing of large data sets across research and production operations. Apache Hadoop is scalable for use in up to thousands of computing servers and offers support for Advanced RISC Machine (ARM) architectures and Java 11 runtime.

Apache Spark 

Apache Spark is an open-source analytics engine used for processing large-scale data sets on single-node machines or clusters. The software provides scalable and unified processing, able to execute data engineering, data science and machine learning operations in Java, Python, R, Scala or SQL.

Apache Storm

Able to process over a million tuples per second per node, Apache Storm’s open-source computation system specializes in processing distributed, unstructured data in real time. Apache Storm is able to integrate with pre-existing queuing and database technologies, and can also be used with any programming language.

MongoDB Atlas

With a flexible and scalable schema, the MongoDB Atlas suite provides a multi-cloud database able to store, query and analyze large amounts of distributed data. The software offers data distribution across AWS, Azure and Google Cloud, as well as fully-managed data encryption, advanced analytics and data lakes.

Apache Cassandra

Apache Cassandra is an open-source database designed to handle distributed data across multiple data centers and hybrid cloud environments. Fault-tolerant and scalable, Apache Cassandra provides partitioning, replication and consistency tuning capabilities for large-scale structured or unstructured data sets.

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