Successful scaling strategy for your analytics product
According to estimates from the International Data Corporation, the global data volume will reach a staggering 175 zettabytes by 2025. To help you better understand the magnitude, it is 175, followed by 21 zeros! Not all of this data is useful, and extracting business information from huge amounts of data is like looking for a needle in a haystack.
Managing large data workloads is a complex challenge. This involves data collection methods, very specific big data tools, selecting the right tools for your application, adhering to local and international guidelines, performance bottlenecks resulting from hardware and software limitations, and ‘other security concerns.
However, the picture is not all gloomy. Companies have accelerated their innovation and development processes to get the most out of their analytics tools and platforms. However, having the most innovative product at your fingertips, the key is to scale as the business and data grows. Failure to grow your infrastructure with increasing data causes major bottlenecks in big data and analytics workloads. Migration to different infrastructures is seen as an alternative. However, this is a complicated and lengthy process that can lead to significant downtime and costs. Businesses therefore need to invest in selecting the best infrastructure that will evolve as data grows.
Focus on scalability
While scaling is difficult, it is necessary for the growth of a data-driven business. Businesses need to implement scalability when performance issues snowball and start to impact workflow, efficiency, and customer retention. The most common performance bottlenecks include high CPU usage, low memory, high disk I / O, and high disk usage.
There are two common ways to scale your data analytics solution:
Vertical scaling involves replacing the server with a faster server with more powerful resources such as processor and memory. It is generally used in the case of the cloud because scaling dedicated servers is a relatively difficult task. Alternatively, bare metal servers are a type of dedicated server with additional features that provide the ability to scale up and down from a single UI platform while ensuring downtime. minimum stop.
Another method is scaling or the type of horizontal scaling. It basically refers to using more servers for parallel computing and is considered the most suitable for real-time analytics projects, as it allows companies to design a suitable infrastructure from scratch and add more servers in the future. Horizontal scaling tends to reduce costs in the long run.
These scaling methods have various advantages. For example, horizontal scaling allows the power of several machines to be combined into one, thus improving performance. Horizontal scaling also offers built-in redundancy and ensures cost optimization. Vertical scaling, on the other hand, maximizes existing hardware, better handles resource upgrades, and lowers energy costs.
Everything about XOps
Scalability has four main components: processes, automation, people, and leadership. Processes and automation, in particular, fall within the larger framework of the overall workflow. Such workflows are often governed by a framework or set of guidelines that cover the end-to-end process. Come in, XOps. The term XOps has gained popularity and acceptance across all industries and businesses. Earlier this year, it was also named one of Gartner’s Top Ten Data and Analytics Trends for 2021.
XOps can be seen as the natural evolution of DataOps in the workplace that enables AI and machine learning workflow. XOps aims to include DataOps, MLOps, PlatformOps and ModelOps to create an enterprise technology stack that helps automation and scalability and avoids duplication of technology and processes. Therefore, talking about and understanding XOps is very important when discussing the scalability of data analysis.
XOps enables data and analytics teams to operationalize their processes and automation early on rather than dealing with it after the fact. Here, the term “operationalize” refers to the orchestration of processes in such a way as to achieve measurable and defined goals aligned with business priorities.
Examples of successful scale-up
Netflix, which started in 1997 as a DVD rental company, has now grown into one of the world’s largest companies with over 214 million subscribers. The pace and smoothness of its scalability is one for the books. Netflix has developed and invested in its big data and analytics tools to run a very successful business model.
Besides Netflix, social media giant Twitter is also worth mentioning. Two factors have helped Twitter grow quickly, according to one article: the use of schemas to help data scientists understand petabyte-scale data stores, and the integration of multiple components into production workflows.
Companies like Netflix and Twitter have survived and thrived amid fierce competition as they grew quickly, sustainably and responsibly – a good lesson for businesses that gamble for the long haul.
This article is written by a member of the AIM Leadership Council. The AIM Leaders Council is an invitation-only forum for senior executives in the data science and analytics industry. To check if you are eligible for membership, please complete the form here.