After determining that earning a million dollars a year is not an impossible aspiration, the three key engineering difficulties

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The era of big data has arrived as a result of Bentley Microstation the quick development of information technology and the exponential growth in the quantity of data that humans can gather, analyse, and use. Our everyday lives have undergone several changes as a result of the changing features of the times. Engineers have also faced numerous new chances and problems.

For instance, technology like one-code passes and CDE Solution provider trip cards may make social governance goals that were before unreachable a simple data query and processing. However, if technology is not up to par, events like the one-code pass disaster in Xi'an, which caused widespread social unrest, may happen.

What are the real-world technical issues that big data technology has to address? Let's look at what's underneath.

First, massive heterogeneous data processing and storage

One of the most important uses of CDE solution big data technology is the large-scale heterogeneous data processing and storage. As the name implies, the most fundamental characteristic of big data is the enormous volume of data, considerably more than the typical structured database can handle. In addition to structured data, there are several other various sorts of data, including audio, video, text, pictures, vector data, and many others.

Therefore, it becomes a major technical challenge if these data cannot be accessed, processed, or used quickly. The majority of corporations' data volumes currently surpass the TB, PB, and EB levels and are approaching the ZB levels. Large-scale data access and usage are now possible thanks to the great solutions offered by several top-notch Internet businesses.

A good example of this is Google, which first released three systems based on GFS, MapReduce, and BigTable before open source HDFS, Hadoop, Hive/Hbase, and other designs gained popularity. Since then, Google has unveiled Caffeine, Pregel, and Dremel, three new technological frameworks that have successfully processed data at the second-level on a petabyte scale.

Major domestic Internet firms have also been able to effectively address these issues with their own technology, either by enhancing the open-source system or creating brand-new technologies. However, many small businesses can only do specific computer-aided office tasks using conventional database design due to their own technological capacity and data volume. Therefore, a crucial basis for the switch from conventional programs to big data programs is knowing the fundamental technology of large-scale data access processing.

Second, massive concurrent real-time processing

The handling of concurrent users is a key component of big data engineering. For example, the "double eleven" has enabled internet purchasing for more than a billion consumers concurrently.

The industry has created several real-time processing solutions to handle high concurrency as a result of this requirement. Include load balancing, the use of caching, databases with data stored in memory, read-write separation, data storage optimization, databases with active data separated out, the use of distributed technology, microservices technology, a larger message queue, delayed modification, business splitting, and so forth.

Nginx, Redis, Kafka, and the SpringCloud microservices suite are typical examples of load balancing solutions that are frequently used to address the issue of real-time high concurrency system technologies.

But not all adjustments are equal; in reality, partitioning processing and lengthening the processing chain are the two primary ways to address the issue of excessive concurrency. Learn how to use these two techniques. The issue of excessive concurrency is extremely simple to resolve.

Third, processing streaming data

Streaming data processing, which differs from the initial static data processing, is a crucial big data application situation. What exactly is streaming processing then? Simply said, in streaming data processing, a portion of the continuously arriving data is intercepted and sent to be processed since the data is dynamic. This is comparable to a few attractive ladies shopping in malls; their activity is continuous, but they sometimes focus on a particular good, which represents a tiny subset of the continuous data. For instance, the salesperson raced over to offer this lipstick, which is more appropriate for you and covered with the unavoidable impact of the Count of Q La cloud, after noticing a gorgeous woman glancing at the lipstick shelves. Processing streaming data in real-time. It would be too late if the operation of the beauty was seen initially, and then someone went back and watched the video at night and discovered that the beauty is plainly interested in a certain product.

Static data is not particularly helpful, which is another characteristic of streaming processing. Asking how many people are online right now on WeChat, for instance, is not really informative because the figure will change in the space of a single eyeblink. Some mathematical statistical data is what is important. The maximum number of users, the predicted number of users for each period of time, and so forth are a few examples.

Currently, Storm, Spark Streaming, Flink, and other programs are the primary tools used in stream processing.

With Internet+'s continuing expansion, learning new technologies is a good way for many programmers to advance their careers and increase their salary. Therefore, it is crucial for programmers to understand the key application areas of big data technology and related tools. After all, one of the most important aspects of any sector is ongoing learning.