Topic: AI's deep revolution in data and analytics.
Introduction: In today's data-driven era, artificial intelligence (AI) is gradually reshaping every aspect of data processing and analysis. The revolutionary changes in the data field, from open source to commercialization, from the rise and fall of big data platforms to the evolution of business intelligence tools, and the relationship between performance and performance, will be led to a higher level by AI. This article will delve into these aspects and present a comprehensive picture to the reader, using Kyligence's practices and observations as examples.
Open Source Commercialization Path:
Open source, as a market tool rather than a business model, faces challenges in commercialization. However, the authors emphasize through Kyligence's experience that successful commercialization requires a deeper understanding of customer needs. Converting open source users into paying users is not a simple process, but requires the establishment of effective commercialization means. The article mentions that business is business, and in order to be successful, it is necessary to have a deep understanding of why customers are willing to pay. We should not only focus on the technology itself, but also pay attention to the product design, service guarantee and continuous innovation ability behind the technology.
The Rise and Fall of Hadoop:
Hadoop used to be the representative of big data, however, in 2021, with the privatization of Maper** and Cloudera, Hadoop gradually declined. The article reviews the glory days of Hadoop and the reasons for Hadoop's decline. The community's ** and conservative business strategies were pointed out as one of the main reasons. At the same time, big data platforms are developing in the direction of cloud-native architectures and data lakes. The authors emphasize that this transformation will usher in great challenges in technology and market, and herald a profound change in the field of big data.
Evolution of BI tools:
Business intelligence tools play a key role in data analysis, however, as AI evolves, visualization tools may be replaced by more advanced AI analytics. The article delves into the impact of AI on BI tools, pointing out that the emergence of AI has made data consumption more popular, no longer limited to leaders, analysts, and professional users, but to everyone. The rise of AI will change the evolution of BI tools, making them smarter and easier to use.
Performance vs. Performance:
Performance has always been a key challenge in OLAP scenarios. The article emphasizes the relationship between performance and performance, and proposes to shift the focus from query performance to more comprehensive performance. The authors believe that in the AI era, performance will become the new key indicator, and more attention will be paid to the overall performance of the system and the support for the user's business than the traditional query performance. The article points out that in the modern business environment, companies need to focus more on performance than just on improving technical performance.
AI devours the world:
The advent of Generative AI marks a revolution in the way data is processed. The article details how AI is changing the process of data and how it can replace humans to complete repetitive and automatable tasks. AI makes data processing more efficient and no longer relies on manual processing of complex and repetitive tasks. The article mentions that AI has revolutionized data analysis, reducing the burden on human analysts and making analysis faster and more efficient.
Conclusion: AI is driving a deep revolution in data and analytics. This paper presents a comprehensive picture by analyzing the path to open source commercialization, the rise and fall of Hadoop, the evolution of BI tools, the relationship between performance and performance, and the world of AI devouring. Kyligence's practice supports this change, but it also raises a range of questions and challenges. In the AI era, the field of data and analytics will still face more changes, requiring industry practitioners to actively respond and innovate. The future of this industry is full of opportunities and challenges, which is worthy of our in-depth thinking and development. We hope that readers will have a better understanding of the profound impact of AI on data and analytics, so that they can better cope with future developments.
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