Home References Big Data Technologies

Big data is basically a large volume of data that an organization can use to take useful business decisions. Now, the next question that comes to someone’s mind is how this data can be processed so that organizations can get meaningful insights from it.

Herein lies the importance of big data technologies, which help businesses make meaning out of the gigabytes of data available to them. The top 10 big data technologies available in the market today include:

Predictive analytics

Predictive analytics is a part of advanced analytics, which intends to make predictions of what will happen in future through various techniques like statistics, modeling, artificial intelligence, machine learning, data mining and others. This can be done through technology, which uses all these methods to predict the uncertainties of the business. This helps organizations to improve business performance and mitigate risk.

NoSQL Databases

Often known as non-SQL, non-relational or not, only SQL database, NoSQL database provides a platform for businesses to store and retrieve data by finding relationships between various data groups. NoSQL is used increasingly for analyzing big data mostly due to the simplicity of their design and provides better control over available solutions.

Search and Knowledge Discovery

This technology collects information and knowledge from various sources like databases, file systems, streams, APIs and various other platforms and delivers useful insights based on a specific context or requirement. The information is collected through extensive search in these platforms and then intelligent search results are delivered. The simplicity and usability of this technology have made this technology very popular among organizations.

Stream Analytics

Stream Analytics is an event processing engine where the event can be fed in real-time from a single stream of data or multiple streams. The data is then filtered and analyzed to get multiple outputs which can be used in business. The data can be fed from multiple sources also like applications, devices, sensors, operation system, websites and a number of other sources.

In-memory data fabric

In memory, data fabric is basically infrastructure software that that slides between applications and data sources. It processes enormous amounts of data very fast to deliver real time results. In memory data fabrics are extremely good for high-performance transactions, real-time streaming, comprehensive data processing and a variety of other tasks. In most cases, in-memory data fabric distributes data between dynamic random access memory, flash and SSD of a computer system.


Distributed file stores

This is where data is stored in various layers which are often similar in structure to ensure higher performance and lower redundancy. In most cases, they are location agnostic and hence help to improve reliability and reduce the complexity of different layers of data.

Data Virtualization

Data virtualization is a technique by which data is retrieved and manipulated without accounting for the technical detail about the data like where is it located or how was it found. In data virtualization, the data is processed in real time or near real time basis.

Data Integration

In this technology, data are combined using technical and business processed from different sources to bring out meaningful information. The information output is often termed as ‘trusted’ because of the way the same is processed. This method uses solutions like Amazon Elastic Map Reduce (EMR), Apache Hive, Apache Spark, Couchbase, Hadoop etc.

Data Preparation

This technology manipulates data and makes it suitable for further processing and analysis. The manipulation is done through various tasks which are both manual and automated. Most of the preparation work is tedious, time-consuming and routine. The main intention of this technology is to reduce the burden of shaping and cleaning a diverse set of high volume data for the analysts.

Data Quality

The last and final technology in our list of top 10 is data quality. Data quality refers to cleansing a large volume of data to ensure the output is of high quality. The high-quality data can then be used for operations, decision making, and planning.

Big data is here to stay and is growing faster than ever. It has been predicted that by the end of 2020, 1.7 m3egabytes of new information will be created every second of every human on earth. And by then, the total accumulated data will grow ten times from today to 44 zettabytes. Every second, 40,000 search queries are made on Google alone, which amounts to 3.5 searches per day and 1.2 trillion searches per year.

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