What is Big Data Technology? It is an extremely fast internet processing engine. It s built on large-scale internet networks, taking advantage of the real time processing for huge data. Its rich and robust library of machine learning is ideal to use in the space of AI & ML. The popularity of its usage can be gauged from its increasing demand in all sorts of industries.
What is big data technology used for? It is used in all aspects of business intelligence & data management. Data collected by traditional data analysis methods are increasingly complicated. With the advent of big data technology and its availability through the web and mobile devices, data analysis is fast coming to look outdated. Data mining makes it possible to take the complex data and make it easily understandable.
There are four big data technologies available for businesses today – Hadoop, Kafka, Spark and Distributed Data Management (D DM). They are used for various purposes like data cleansing, high-volume data processing, analytical processing, image recognition and speech recognition, text mining and many more. While all these technologies handle different tasks, they all run on common core libraries and framework. The developers of these technologies claim that one can easily develop applications on top of these technologies and create a new class of applications called a Platform.
Is cloud computing better than other big data technologies? It is believed that the best part about cloud is the ability to access it at any place, any time. Another advantage is that the service provider maintains your data on the servers while you are away. The service provider also provides you with real-time streaming data sources. All these factors make cloud computing an ideal platform to support big data analytics.
There are a number of cloud computing providers in the market. Some of them offer both open source and no-SQL databases. We narrowed down the list to five technologies based on features and benefits. These are Consul, Illuminate, Kettl, Thin, and Vapor. While these technologies deliver different capabilities and features, we kept the list simple and ranked them in order of priority. After sorting out the most important features and determining the technologies that offer the key benefits to organizations, we gave the top five spots to the top five contenders.
Consul is one of the few platforms available that offers both streaming and no-SQL databases. It is also one of the few platforms available that provides full integration with social media. At the same time, the Consul is one of the few big data technologies that offers real-time analytics and ingested ingest to make it very useful for data cleansing and business intelligence.
Illuminate is another platform that is popular among social media users and data collectors. The platform is used for both streaming data as well as unstructured data collection. Users can use both streaming and unstructured data via a single interface. Because of its ease of use and ability to combine streaming data with structured data, Illuminate is a perfect platform for data extraction, transformation, and analysis.
Kettl is another cloud computing platform that is popular among large institutions and enterprises. Large companies and businesses that want to leverage the power and convenience of the cloud can do so with Kettl. Through Kettl, companies can ingest large amounts of data sources without worrying about the quality of those data sources or managing the servers. Kettl is also well suited for unstructured data sources because of its support for large amounts of data sources and ingesting and transformations.
IBM’s Open Watson is another popular open-source project. Open Watson includes features such as HDFS, NACS, IML, MLware tools, and more. IBM created Open Watson because it wanted to provide customers and partners access to IBM’s massive data lakes. Data lakes are virtual collections of structured data that are accessible via the Internet and through servers. Open Watson empowers data analysis and provides users with the ability to extract insights from unstructured data sources.
IBM’s Data lake platform is designed to enable business users to achieve an overall data management strategy that enables insights from massive amounts of data. Data management is critical for managing business because it allows insights from massive amounts of data to flow into relevant business applications. Data management solutions are designed to allow business users to gain insights on how people use the Internet, what searches people do, what they look for when searching, where searches lead, and many other key factors. Through these insights, companies can increase user productivity, reduce operational expenses, and increase profitability. However, by only accessing and leveraging the information that is pertinent to a given business, users run the risk of missing out on trends and emerging opportunities that could benefit their businesses.
In addition to what is already mentioned above, the combination of unstructured data processing technologies and social media analytics offers organizations an unprecedented opportunity to exploit the massive amount of available data to create new business opportunities and to improve their bottom line. It has been well documented that traditional business analytics solutions have been limited in their capabilities. Traditional business intelligence solutions have not only been unable to take advantage of the opportunities presented by unstructured data, but have in some instances actually been detrimental to organizations. By combining social media analytics with data processing technologies such as data lakes and IBM’s Data Lake, businesses can eliminate the limitations inherent to traditional Big Data solutions and gain an unparalleled advantage over their competitors.