The Destruction of the Data Center DevOps Bubble Who is the Next Generation Market Winner
If we want to list the relevant major trends that currently affect enterprise data centers, most technical and technical investors may agree on a series of core trends. The core trend list will include technologies such as cloud computing, container and virtualization, microservices, machine learning and data science, flash memory, edge computing, NVMe, and GPU. These technologies are very important for promoting the digital transformation of current business organizations.
However, the more difficult question to answer is: What will happen next? Which emerging technologies or trends are expected to become the next big event in the data center industry? These emerging technologies and trends will in turn affect the hardware and software market. What kind of influence?
At present, an emerging trend has begun to gain wider attention in large enterprises. This is a kind of practice plan called "DataOps". This new term is derived from the notion of a more famous “DevOpsâ€. The concept of “DevOps†emerged ten years ago. Its purpose is to integrate software development (“devâ€). ) and Operational Operations ("ops"). Although the objectives of DataOps and DevOps are consistent in some respects, it is more significant to reflect some of the major changes we see today in the data center industry.
The concept of DevOpsLet's start with DevOps and start our discussion. Back in 2008, when the concept of DevOps was first proposed, it described an IT practice that was designed to maximize automation and repeatability during application build and deployment. Its purpose is: If software developers and operations operations professionals can work closely together, building and deploying applications will be faster and less costly. The goals of the practice include increasing agility and agility, enabling faster time-to-market and continuous application delivery.
Companies such as VMware, Docker, Puppet, and Chef have caught up to this wave of DevOps.
The DevOps bubble burst
Despite the early fanaticism and excitement experienced by software developers, DevOps has stalled. According to a study in 2017, DevOps has not completely fulfilled its initial commitment. In this study, a total of 2,197 corporate IT executives were surveyed, of which only 17% of respondents listed DevOps as having a major strategic impact on the organization they belong to - this ratio is much lower Data (41%) and Public Cloud Infrastructure as a Service (39%). One of the interviewees explained that the DevOps method does not fully consider data-intensive applications.
The rise of dataIf there is already a trend that affects almost all industry organizations, then there is no doubt that the trend is that companies are paying more and more attention to the realization of value through the use of data analysis. According to a study by IDC, by 2020, the amount of data on the global scale will reach 44 zettabytes, compared with 3 exabytes in 1986. Whether it is to improve the customer experience, improve operational efficiency or create new sources of revenue, the data can provide leverage support for enterprises across all industries to enhance market competitiveness.
Why is data so importantIf the use of data has become part of the disruptive business model that companies are currently adopting, then considering how to manage and deploy data-intensive applications has become the core of enterprise organization IT practices. Unlike the lightweight applications that the DevOps approach focuses on, there are a series of new considerations as business organizations begin to discuss data-intensive applications.
Data management practices involve the entire application life cycle. For example, the development of data science and machine learning applications requires the use of large amounts of training data. The operational operations team deploys different applications; for performance reasons, data-intensive applications need to consider the locality of the data, which means that the process needs to be deployed near where data is continuously generated. In addition, as long as different teams within the enterprise organization need to use data, access to the data must be controlled and managed by strict IT security policies.
DataOps for Data-Driven ApplicationsThese new, data-centric ideas have inspired the data center industry's need for more than DevOps restrictions. In short, DataOps is a flexible and agile method for developing and deploying data-intensive applications. This approach is largely encouraged by the development of machine learning and data science teams within the enterprise, which involves the involvement of software developers and architects within the company, security and management professionals, data scientists, data engineers, and operations personnel. The close cooperation. DataOps is a people-plus-process model designed to improve repeatability, productivity, agility, and self-service while enabling continuous deployment of data science models.
In our cooperation with some large enterprise organizations, we discovered that some large enterprises employ thousands of data scientists. I noticed that these companies have correspondingly applied to the types of infrastructure, platforms, and tools used to support DataOps. Variety. Although some of the tools used to support DevOps practices, such as containers and virtualization, are still at the core of DataOps, there are other technologies that need to be forced to use updates, which may suggest market winners for the next decade.
First, at the tool level, the DataOps practice program requires a data science platform that supports relevant languages ​​and frameworks such as Python, R, data science notebooks, and Github. In addition, a strong program should help enforce strict data access and management policies at all stages of the process. Data-as-a-service or self-service data market tools are quite critical.
At the platform level, DataOps needs a unified data architecture that can manage and provide access to large amounts of data, including traditional structured data and updated unstructured and streaming data sets. With a global data architecture, data can be managed across physical locations and data can be processed using a wide range of computing engines, including containerized processes. Finally, platforms that choose to support data-intensive applications must be optimized for data locality.
The next generation of market winnersAs a senior person and student in the software industry, I know that the only constant in the industry is continuous change. Although no one has a crystal ball that can predict the future, I think it is safe to say that data centers in the next decade will be different from data centers in the past decade. DataOps is a trend worth watching. As companies adopt these practices more and more, I predict we will see a corresponding shift in the technology market. The winners of the market will be those companies that can provide tools and platforms to make it easier to develop and deploy data-intensive applications.
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