Big Data Analytics - A Complete Introduction
What is Big Data Analytics?
Big data analytics definition: Big data analytics helps businesses and organizations make better decisions by revealing information that would have otherwise been hidden.
Meaningful insights about the trends, correlations and patterns that exist within big data can be difficult to extract without vast computing power. But the techniques and technologies used in big data analytics make it possible to learn more from large data sets. This includes data of any source, size and structure.
The predictive models and statistical algorithms of data visualization with big data are more advanced than basic business intelligence queries. Answers are nearly instant compared to traditional business intelligence methods.
Big data is only getting bigger with the growth of artificial intelligence, social media and the Internet of Things with a myriad of sensors and devices. Data is measured in the “3Vs” of variety, volume and velocity. There’s more of it than ever before — often in real time. This torrential flood of data is meaningless and unusable if it can’t be interrogated. But the big data analytics model uses machine learning to examine text, statistics and language to find previously unknowable insights. All data sources can be mined for predictions and value.
Business applications range from customer personalization to fraud detection using big data analytics dashboards. They also lead to more efficient operations. Computing power and the ability to automate are essential for big data and business analytics. The advent of cloud computing has made this possible.
A Brief History of Big Data Analytics
The advent of big data analytics was in response to the rise of big data, which began in the 1990s. Long before the term “big data” was coined, the concept was applied at the dawn of the computer age when businesses used large spreadsheets to analyze numbers and look for trends.
The sheer amount of data generated in the late 1990s and early 2000s was fueled by new sources of data. The popularity of search engines and mobile devices created more data than any company knew what to do with. Speed was another factor. The faster data was created, the more that had to be handled. In 2005, Gartner explained this was the “3Vs” of data — volume, velocity and variety. A recent study by IDC projected that data creation would grow tenfold globally by 2020.
Whoever could tame the massive amounts of raw, unstructured information would open a treasure chest of insights about consumer behavior, business operations, natural phenomena and population changes never seen before.
Traditional data warehouses and relational databases could not handle the task. Innovation was needed. In 2006, Hadoop was created by engineers at Yahoo and launched as an Apache open source project. The distributed processing framework made it possible to run big data applications on a clustered platform. This is the main difference between traditional vs big data analytics.
At first, only large companies like Google and Facebook took advantage of big data analysis. By the 2010s, retailers, banks, manufacturers and healthcare companies began to see the value of also being big data analytics companies.
Large organizations with on-premises data systems were initially best suited for collecting and analyzing massive data sets. But Amazon Web Services (AWS) and other cloud platform vendors made it easier for any business to use a big data analytics platform. The ability to set up Hadoop clusters in the cloud gave a company of any size the freedom to spin up and run only what they need on demand.
A big data analytics ecosystem is a key component of agility, which is essential for today’s companies to find success. Insights can be discovered faster and more efficiently, which translates into immediate business decisions that can determine a win.
Big Data Analytics Tools
NoSQL databases, (not-only SQL) or non relational, are mostly used for the collection and analysis of big data. This is because the data in a NoSQL database allows for dynamic organization of unstructured data versus the structured and tabular design of relational databases.
Big data analytics requires a software framework for distributed storage and processing of big data. The following tools are considered big data analytics software solutions:
- Interactive visual analytics platform that can process massive multi-source datasets in milliseconds.
Apache Kafka
- Scalable messaging system that lets users publish and consume large numbers of messages in real time by subscription.
HBase
- Column-oriented key/value data store that runs run on the Hadoop Distributed File System.
Hive
- Open source data warehouse system for analyzing data sets in Hadoop files.
MapReduce
- Software framework for processing massive amounts of unstructured data in parallel across a distributed cluster.
Pig
- Open source technology for parallel programming of MapReduce jobs on Hadoop clusters.
Spark
- Open source and parallel processing framework for running large-scale data analytics applications across clustered systems.
YARN
- Cluster management technology in second-generation Hadoop.
Some of the most widely used big data analytics engines are:
Apache Hive/Hadoop
- Data preparation solution for providing information to many analytics environments or data stores. Developed by Yahoo, Google and Facebook.
Apache Spark
- Used in conjunction with heavy compute jobs and Apache Kafka technologies. Developed at the University of California, Berkeley.
Presto
- SQL engine developed by Facebook for ad-hoc analytics and quick reporting.
Big Data Analytics Explained
Bigger Data, Better Insights
Big Data Analytics Examples
The scope of big data analytics and its data science benefits many industries, including airlines, banking, government, healthcare, manufacturing, retail, etc. See how analytics shape these industries and more in our full list of big data analytics examples.
Best Practices for Big Data Analytics
Big data analytics basic concepts use data from both internal and external sources. When real-time big data analytics are needed, data flows through a data store via a stream processing engine like Spark.
Raw data is analyzed on the spot in the Hadoop Distributed File System, also known as a data lake. It is important that the data is well organized and managed to achieve the best performance.
Data is analyzed the following ways:
Data mining
- Uses big data mining and analytics to sift through data sets in search of patterns and relationships.
Big data predictive analytics
- Builds models to forecast customer behavior.
Machine learning
- Taps algorithms to analyze large data sets.
Deep learning
- An advanced version of machine learning, in which algorithms can determine the accuracy of a prediction on their own.
Big data analytics takes business intelligence to the next level. Business intelligence relies on structured data in a data warehouse and can show what and where an event happened. But big data analytics uses both structured and unstructured datasets while explaining why events happened. It can also predict whether an event will happen again.
Read more about BI Tools in our complete guide.
Is Big Data Analytics Important?
Big data analytics are important because they allow data scientists and statisticians to dig deeper into vast amounts of data to find new and meaningful insights. This is also important for industries from retail to government in finding ways to improve customer service and streamlining operations.
The importance of big data analytics has increased along with the variety of unstructured data that can be mined for information: social media content, texts, clickstream data, and the multitude of sensors from the Internet of Things.
Big data analytics is necessary because traditional data warehouses and relational databases can’t handle the flood of unstructured data that defines today’s world. They are best suited for structured data. They also can’t process the demands of real-time data. Big data analytics fills the growing demand for understanding unstructured data real time. This is particularly important for companies that rely on fast-moving financial markets and the volume of website or mobile activity.
Enterprises see the importance of big data analytics in helping the bottom line when it comes to finding new revenue opportunities and improved efficiencies that provide a competitive edge.
As more large companies find value with big data analytics, they enjoy the benefits of:
Cost reduction
- By discovering more efficient ways of doing business.
Decision making
- Fast and better decisions with the ability to immediately analyze information immediately and act on the learning.
New products
- Using data to understand customers better gives companies the ability to create products and services that customers want and need.
Learn more about big data analytics use cases with these free whitepapers:
