With the increasing maturity of data analytics technology, research institutes should actively utilize data analytics tools to improve research efficiency.
Big Data
The application of big data is just like the use of credit cards. The better you use it, the greater the income. On the contrary, can enterprises bear the cost of mistakes in big data? This article describes 6 major mistakes and solutions.
With the continuous improvement of big data infrastructure, data analytics and business intelligence tools will gradually become the mainstay of big data. Therefore, the big data industry will develop toward these trends in the coming years.
Data Lake is a term that has emerged in the past decade to describe an important part of the data analysis pipeline in the big data world.
What we can predict is that the future of big data technology will continue to evolve along the direction of heterogeneous computing, cloudization, AI convergence, and in-memory computing.
Python as a recognized language suitable for big data, want to do big data development and big data analysis, not only to use Java, Python is also very important a core.
Companies tend to make their Big Data projects large in size and scope when implementing them, but the truth is that most Big Data projects usually end up in failure.
Low-latency analytics is a technology that enables processing and analyzing big data in real time or near real time. It is critical in big data processing because it allows organizations to extract insights from data faster.
To ensure that your organization's big data plan is on track, you need to eliminate the following 10 common misconceptions. Let's look at them together.
When it comes to big data, many people can say some, but if you ask what are the core technologies of big data, it is estimated that many people will not be able to say