Look, if you are looking to understand how characteristics of big data capabilities change every year, you must ensure that you have a readily available reference to the key data science terms that influence big data trends.
The Big Data industry keeps throwing up new terms and definitions every now and then.
I have been compiling a list of the top 1000 data science terms that every Big Data professional should know and learn deeply about. Why? These terms are considered key to trends that emerge as part of the characteristics of big data applications.
Algorithms are the structural models built using programming language to define a finite sequence of instructions at a machine level. Algorithms that are used in big data applications are mostly dynamic types that are further classified as Brute force programming, quick sort, traverse search trees, and reverse string. In the recent years, many other types of big data algorithms have emerged to further improve optimization techniques specifically focusing on AI and machine learning.
If you wish to crack big data interviews with some prowess, start learning and deciphering with all kinds of algorithms.
If you looked for Apache a few years ago on any search engine, chances are high that you would never be able to connect this term with Big Data capabilities. But things changed dramatically with the advancement in data management, especially with the maturity of web service application software development and open source DevOps. Today, the Apache HTTP server is one of the basic components for starting with Big Data projects. You would come across many associated terms in the Apache ecosystem, some of which I have listed below:
- Apache Software Foundation (ASF)
- Apache Kafka
- Apache Mahout
- Apache Oozie
- Apache Drill
- Apache Impala
- Apache Spark SQL
- Apache Hive
- Apache Pig
- Apache Sqoop
- Apache Storm
Itâ€™s not possible to explain the whole of Apache’s capabilities in one blog. We will cover the entire Apache domain in a separate article.
Augmented Intelligence (The Real AI)
We have spent way too much time and effort discussing only one aspect of machine level intelligence â€“ also referred to as Artificial Intelligence. But did you know that the characteristics of Big Data in todayâ€™s scenario focus entirely on two major aspects of machine-generated information? These are Augmented Intelligence and Cognitive Intelligence. If you are designing an advanced AI model, you would require high quality Big Data pool that entails developing intelligent machines called supercomputers and a software package called â€œembedded AIâ€. These connect together to deliver a totally new family of applications classified as â€œAmplified Intelligenceâ€ â€“ this is very useful in understanding the working principles of Artificial Brain and Artificial Neural Networking (ANNs). We call this the real AI!Â
So we deal with big data lakes that run into terabytes, petabytes, and now brontobytes (1 followed by 27 zeroes!). But did you know that there are some â€œunattributedâ€ big data projects in open source that suggest working with data that run into googol (a number with 100 zeroes.) We are not far from the moment when Google would finally accept that the size of its database is now in millions of googol and has been thrown open to qualified data scientists from the digital universe for further studies.
Numbers come in all sizes and forms. Thanks to the internet, we are seeing a whole new family of analytics related to behavior, relationships, click patterns, security, and so on.Â Big Data analytics is the mother of all intelligence platforms, especially for those analysts who serve in the marketplaces for e-commerce, social media, and internet advertising and sales.Â
Some of the common analytics that is used in big data projects involve analysis of behavioral patterns, sentiment, text, and speech, eye tracking, pixel tracking, biometrics, clickstream, and so on.
Any data that can be represented in the form of a graphical format refers to visualization. Data streaming is the core of data visualization that demonstrates how well the data has been mined for big data projects.
For those coming as new entrants into the big data field, this basic blog would help them cope with the barrage of terms.