In this special guest feature, Asaf Yigal, Co-founder and Vice President of Product at Logz.io, discusses the intersection of artificial intelligence and big data, and how it will be instrumental in DevOps. Founded in 2014, Logz.io is a leading provider of enterprise-grade ELK. In addition to Elasticsearch, Logstash, and Kibana Logz.io’s software features innovative technology such as the machine learning Cognitive Insights, Live Tail and more. Asaf co-founded Currensee, a social-trading platform, which was later acquired by OANDA in 2013. Prior to Currensee, Asaf held executive roles at Akorri -developing an end to end performance monitoring platform and at Onaro-developing a storage resource management platform. Both Akorri and Onaro were acquired by NetApp. Prior to Onaro, Asaf headed a research team in the Israeli Navy, taking an artificial intelligence system to military deployment. Asaf holds a B.S. from the Technion and is an Instrument-rated private pilot.
If you work in the technology industry, it’s nearly impossible to escape the vast impact of AI and Big Data analytics. Often thrown around in the same context, at times it is difficult to figure out where one technology begins and the other takes over. While it is clear there is a connection between the two, understanding the manner in which AI and Big Data work together to solve business and operational problems is a key part of using the technologies effectively.
But before we delve into it, let’s understand the basics of each technology and examine how they complement one another.
What is Big Data?
We cannot stop talking about Big Data. The seemingly unlimited use cases for the technology are displayed nearly everywhere we turn. There’s Big Data for marketing, sales insights, security, business intelligence, and everything in between. But the more we hear about Big Data, the more elusive the term seems to become.
So what exactly is Big Data? Big Data refers to diverse sets of information that are produced in incredibly large volumes and must be processed at high speeds. The data collected can be analyzed to understand trends and make critical predictions. Since there is such an immense amount of data, it is ideal for statistical analysis.
For this reason, the potential for Big Data knows no bounds. Businesses use it to tackle problems such as targeting, predicting customer churn, discovering security breaches, and more. But as such large data sets are accumulated, the inevitable result is the need for analysis. Yet, generally speaking, humans cannot efficiently analyze and monitor the data at a pace that is quick enough to be useful. Hence, the growing need for artificial intelligence.
What is Artificial Intelligence?
Robots and self-driving cars are some of the most well-known applications of artificial intelligence. But at its core, AI is simply a technology which enables machines to perform tasks similar to humans. Machine learning, a type of AI, is a technology in which machines learn through algorithms.
Though the technology is extremely complex, in essence, machine learning entails using algorithms to effectively facilitate the making of critical decisions by machines. There are two main types of machine learning: supervised machine learning and unsupervised machine learning. Supervised machine learning trains the machine through knowing the final output. For example, if you are separating triangles and circles, the machine would know exactly what defines both a triangle and a circle in order to differentiate one shape from the other and place them in the proper category.
In unsupervised machine learning, the algorithm simply enables the machine to separate shapes into groups that are similar. It would notice that all triangles have three sides and three corners whereas circles are all rounded and possess neither sides nor corners. After noticing these different characteristics, the machine would then separate all of the shapes into groups baring similar features. Silicon Valley’s famous “Is it a Hot Dog?” app is a perfect example of this technology in action.
The Big Data Problem
Now that we have established the definition of both AI and Big Data, what is their connection? Let’s start with their practical application. Both DevOps teams and IT operations professionals use Big Data. Since their machines produce millions of data sets and logs on a regular basis, being able to manage and analyze this data becomes extremely complex. Furthermore, the widespread move to the cloud makes IT environments all the more difficult to manage.
To add to the complexity, open source platforms such as the ELK Stack and new innovations such as serverless computing and Docker make IT environments even more dynamic than ever. Just ten years ago, IT departments bought servers and placed them in a data center. But today, they typically run hundreds servers in public clouds such as AWS or Azure. While these recent developments improve workflow, they also add more data and infrastructure to work around.
So why bring in these new technologies? Think about it like a smartphone versus a flip phone. The flip phone is far less complex, but what you can accomplish with a smartphone is infinitely greater. Same for new technologies within IT environments. They solve an array of problems but create others at the same time.
So now that IT operations departments have to manage extremely complex issues stemming from Big Data, how do they attempt to solve them? As you probably guessed, Big Data problems are solved through AI. This combination of Big Data and AI, often referred to as AIOps, go hand in hand and work together to solve many of our most complex problems.
AI to the Rescue
Simply put, there is no AI without Big Data and no Big Data without AI. Many technologies today, such as self-driving cars for example, depend on the combination and intertwining of these two technologies.
AI cuts through the clutter. Since humans lack the ability to manually process and analyze millions of data-sets quickly and efficiently, AI fills in the gap by automatically processing the information and giving it meaning. What machines can do in five minutes might take days or weeks for humans to compile while still being prone to human error. Needless to say, the machine will not get tired from such tedious labor. Rather, it can continue to process terabytes of data all day, everyday.
To sum it all up, Big Data has tremendous potential to take business initiatives to the next level. Through this technology, companies can better monitor their systems, make predictions and implement data-driven strategies throughout their organization. But the tremendous benefits of Big Data do not come without a catch — and in this case, the catch is added complexity.
AI breaks down this complexity so your team doesn’t have to. So where’s the intersection between the AI and Big Data? The answer is AI completes the task that Big Data started. Without AI, Big Data would be overwhelming and chaotic. But by incorporating AI into Big Data analytics the technology becomes useful, lucrative, and has the ability to drive businesses into the future.
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