HADOOP FOR BIG DATA
HADOOP FOR BIG DATA
Around 80% of data from “big data” is unstructured. With this massive quantity of unstructured data, businesses need faster, more reliable and deeper data insights. Therefore, big data solutions based on Hadoop and other analytics software are becoming more and more relevant. One of Hadoop’s strengths is that it can process and analyze huge amounts of unstructured data – video, audio, social media postings, images, etc. – in ways that were previously impossible. If huge quantities of unstructured data are hampering your company’s data analysis efficiency, you aren’t alone.
Hadoop’s capabilities could go a long way towards helping you resolve your corporate data roadblocks. Hadoop doesn’t replace your existing databases, but rather, adds powerful resources to your data handling toolbox. Hadoop can help companies by allowing them to finally make use of huge stores of unstructured data that were too difficult to access in the past. This is the kind of data that companies want to analyze, but don’t have the time or resources to put into a relational database. These kinds of data handling needs require much higher computing power than the traditional relational database. If your management wants to derive insights from its relational data as well as the unstructured data that is generated from Facebook, Twitter, RFIDs, Sensors, etc., then Hadoop is the solution.
Below is a list of some other open source projects that are related to Hadoop:
- Eclipse: a popular IDE, donated by IBM to the open source community
- Lucene: a text search engine library written in Java
- Hbase: the Hadoop database
- Hive: provides data warehousing tools to extract, transform, load, and query data to then be stored in Hadoop files
- Pig: a platform for analyzing large data sets. It is a high level language for expressing data analysis.
- Jaql (“jackal”): a query language for JavaScript open notation
- Zoo Keeper: a centralized configuration service and naming registry for large distributed systems
- Avro: a data serialization system
- UIMA: the architecture for development, discovery, composition, and deployment of unstructured data analytic
The Three Vs of Big Data
Massive velocity, variety and volume (“The Three Vs”) are the defining properties of big data. The Hadoop framework addresses all of these requirements. It provides a framework that scales-out horizontally to handle large data set volumes, it can handle data at a staggering velocity, and it supports complex jobs to accommodate for a variety of unstructured data types. Despite its ability to manage The Three Vs, the real big data challenge remains in big data analytics.
The Real Value of Big Data
It is no easy task to find a way to analyze and find insights within oceans of unstructured data, let alone use those insights to make key decisions. Hadoop Reporting and Analysis makes big data more presentable and digestible, thereby reducing the amount of time required to uncover its business value. In my opinion, the real value in big data is derived from the ability to quickly refine it into relevant insights and context so that it can be used to support business decisions on a real-time basis.
What about you? What is your opinion? In what ways has the incorporation of big data analytics added value to your company?