What is Big Data Analytics?
Big data analytics is the use of large datasets contained in big data sources in the data analysis process. In this context, analytics means the tools, methodologies, and processes used to uncover and dig deeper into information for use in business actions and decisions. Mainly, it encapsulates turning information into business intelligence.
Let’s Start at the Beginning: What is Big Data?
Big data refers to datasets of both structured and unstructured data that are so complex that it is difficult to process using traditional databases or data processing applications. The concept of big data became mainstream in 2001 when analyst Doug Laney defined data growth as being three-dimensional (volume, velocity, and variety), and those dimensions became known as the “Three V’s” models that are used today to describe big data.
Three V’s Model
- Volume: The scale or quantity of data generated and stored. IBM estimates that 2.3 trillion gigabytes of data are generated every day, and many US companies store at least 100 terabytes of data.
- Velocity: The speed at which data streams move and the processing needs that are needed to match demand. In one trade session, the New York Stock Exchange captures 1 terabyte of data.
- Variety: The many different formats of data captured by different systems. Data formats come from many industries with many different formats such as social media tweets or videos around the internet.
Why is Big Data Important?
The International Data Corporation (IDC) estimates from 2005 to 2020 the amount of data created will increase by 300 times leading to 40 trillion gigabytes (40 zetabytes). Advanced analytics can ensure you reap the business value of big data through business intelligence tools. The use of the tools can help you make smart business decisions or optimize product development– whatever the use big data can do it.
How is big data stored?
There are two main methods of storing big data: a NoSQL database or Hadoop.
NoSQL databases allow for non-relational data storage and storage of disparate data types referred to as big data. They differ from relational databases in that they are scalable, agile, and fault tolerant. NoSQL databases differ in how they store data such as graph-based, key-value and column stores, and document based. NoSQL databases often require the use of ETL into a relational data warehouse for the use of analytics such as business intelligence tools.
Apache’s Hadoop is an open source framework written in Java for distributed storage and processing of large datasets (also known as big data) spread across many commodity hardware such as servers. It can handle large volumes of structured, semi-structured, and unstructured data such as images, audio or video files. Hadoop is not a database and is far from structured so it does not use query languages like SQL like many relational databases use. Instead, it uses MapReduce which allows users to retrieve data. Hadoop is deployed as a software through many companies such as Cloudera that often provide a SQL-like language for ease of use.
The Challenges of Big Data Analysis
The development of big data was in many ways a reaction to the growing amounts of information being collecting by modern enterprise-level business applications. And although big data systems helped alleviate some of the performance issues when compared to traditional data storage systems, one of the main challenging big data analytics today is still the volume of data collected, and how much structure that data has.
The fact that different systems collect different information in different ways means that even when combined into a single data store (i.e. a big data source) the information can be hard to analyze, particularly for things like pattern recognition because what was structured data from disparate systems, turns into unstructured data when combined.
The Benefits of Big Data Analysis
Although there are challenges to best utilizing large amounts of unstructured data, there are also many benefits. For example, the performance gains when using a big data source to store lots of data when compared to traditional sources is quite substantial. The ability to then leverage big data analytics can lead to greater business insights which can help identify potential efficiency gains across an enterprise.
Big data analytics are used by anyone from a non-technical end user up to a data scientist, developer, or statistician depending on the application and intended use. Applications which collect large amounts of data that is unstructured but fairly uniform in nature. For example, a telemarketing application which may collect data about hundreds of thousands of calls a day may have very large data sets that may not have a specific structure to them, but may also be fairly uniform in the type of data that is collected. This type of use case may not need a very technical person to analyze the data if the front end analytics application is user-friendly enough.
But an enterprise level system which takes in data from a variety of disparate data sources and which has no structure or uniformity may need someone with more technical expertise available to fully take advantage of the data. The type of analysis used can also affect what type of end user is required, deep statistical modeling and predictive analytics can require a more knowledgeable and technical end user.
Using Big Data with JReport
JReport is an analytics solution designed with embedding in mind. JReport supports a variety of big data sources including MongoDB, Vertica, Hortonworks, Cloudera, Hadoop, Hive, MapR, and Amazon Redshift as well as traditional relational databases and even flat files. With JReport, you can empower your users to utilize your application’s data no matter what the source. JReport’s scalable architecture also allows you to scale up and down as your users’ demands change.