Big Data analytics
Updated:03/06/2020 by Computer Hope
Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.
How Big Data analytics works ?
But businesses are not just simply collecting all of this data that we are generating. They’re actually analyzing it, and finding ways to improve their products and services, which in turn shapes our lives and the experiences that we are having with the world around us.Data is always being generated by digital technologies, whether we are using apps on our phones, interacting on our social media, or shopping for products.
All of this information combines with other data sources and becomes Big Data.
Advantages of Big Data analytics
- Data processing features involve the collection and organization of raw data to produce meaning. Data modeling takes complex data sets and displays them in a visual diagram or chart.
- Data mining allows users to extract and analyze data from different perspectives and summarize it into actionable insights. It is especially useful on large unstructured data sets collected over a period of time.
- Fraud analytics involve a variety of fraud detection functionalities. Too many businesses are reactive when it comes to fraudulent activities — they deal with the impact rather than proactively preventing it. Data analytics tools can play a role in fraud detection by offering repeatable tests that can run on your data at any time
- Reporting functions keep users on top of their business. Real-time reporting gathers minute-by-minute data and relays it to you, typically in an intuitive dashboard format.
- It promotes interoperability and flexibility as well as communication both within an organization and between organizations.
Key big data analytics technologies and tools
- Hadoop , which is an open source framework for storing and processing big data sets. Hadoop can handle large amounts of structured and unstructured data.
- Spark , which is an open source cluster computing framework used for batch and stream data processing.
- Cassandra Used by industry players like Cisco, Netflix, Twitter and more, it was first developed by the social media giant Facebook as a NoSQL solution. It’s a distributed database that is high-performing and deployed to handle mass chunks of data on commodity servers. Cassandra offers no space for failure and is one of the most reliable Big Data tools.
- Drill It’s an open-source framework that allows experts to work on interactive analyses of large scale datasets. Developed by Apache, Drill was designed to scale 10,000+ servers and process in seconds petabytes of data and millions of records. It supports tons of file systems and databases such as MongoDB, HDFS, Amazon S3, Google Cloud Storage and more.
- Elastisearch This open-sourced enterprise search engine is developed on Java and released under the license of Apache. One of its best functionalities lies in supporting data discovery apps with its super-fast search capabilities.
- HCatalog HCatalog allows users to view data stored across all Hadoop clusters and even allows them to use tools like Hive and Pig for data processing, without having to know where the datasets are physically present. A metadata management tool, HCatalog also functions as a sharing service for Apache
- Storm Last but definitely not the least, Storm supports real-time processing of unstructured data sets. It is reliable, fault-proof and is compatible with any programming language. Hailing from the Apache family of tools, Twitter now owns Storm as an open-sourced real-time distributed computing framework.
- Oozie One of the best workflow processing systems, Oozie allows you to define a diverse range of jobs written or programmed across multiple languages. Moreover, the tool also links them to each other and conveniently allows users to mention dependencies.