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Today, the editor will bring you an article about big data. The editor thinks it's pretty good, so I'll share it for you as a reference. Let's follow the editor and have a look.

In 2008, the big data era, written by Victor Mayer-Schoenberg, opened a new chapter in science and technology, making the word "big data" a household name, everyone was talking about big data, and technology companies were introducing big data technology. So, what on earth is big data?

Big data (Big Data) refers to the data set that can not be captured, managed and processed by conventional software tools within a certain time range. It is a massive, high growth rate and diversified information asset that needs a new processing mode to obtain stronger decision-making power, insight and process optimization ability.

In the era of big data, Victor put forward a new concept of big data analysis, which does not use random analysis (sampling survey), but analyzes and processes all the data. After that, IBM also put forward big data's 5V feature:

Volume (mass)

Velocity (high speed)

Variety (diverse)

Value (low price)

Veracity (authenticity)

This may be a bit abstract. For example, we browse the web, shop online, order takeout, browse Douyin, watch online dramas and watch headlines every day. Wherever your fingertips cross, we will leave data. These data are messy, but they imply information such as your behavior habits, purchasing tendency, spending power and so on. How to find hidden associations, mine useful information, and form accurate portraits of users is a very critical step. The algorithm technology derived from this is often called big data technology, including data collection, analysis, mining, derivation, and so on.

In the twinkling of an eye, big data's technology has gone through 10 years, and its popularity is still unabated. Now it has joined forces with artificial intelligence, setting off a new wave of the fourth industrial revolution. After big data's technology is becoming more and more mature, let's look back and see, what on earth is big data?

Big data is an industry with data as the core, a production process that revolves around the life cycle of big data, and a highly complex industry produced by the division of labor and coordination of various industries.

Big data's production process can be divided into the following parts from the transmission and evolution of data life cycle: data collection, data storage, data modeling, data analysis, data realization.

How important is big data? In his book the Intelligent Age: big data and the Intelligent Revolution redefining the Future, Wu Jun mentioned: "in this big data era, whoever finds out the underlying laws from the jumbled big data will master the wealth." This is not only a high summary of the value of big data, but also the extremely present prospect of big data's application in the future.

With the understanding and application of big data's technology, we began to collect data through various software, transmit it through the network, store it through cloud data centers, and model and process it through data scientists or industry experts. finally, we get some knowledge or conclusion through data analysis, and gain an ability to gain insight into the world through data.

As a result, the potential relationship between the originally complex data becomes clearer, a large number of isolated and multi-source data become more interesting, and a large number of seemingly unrelated things show more causality after analysis. these causal relationships enable us to speculate on future trends in more ways, reduce trial and error opportunities, reduce costs and risks, and improve labor productivity. This is the most fundamental value and significance that big data's technology brings to us.

Big data's systematic study materials have been carefully prepared for you. From the Linux-Hadoop-spark-., you can click to enter.

We often find that when shopping online, the products recommended by e-commerce are often of great interest to us, as if we know ourselves better than ourselves. When browsing the news, the home page is often more inclined to read, and everyone seems to have headlines tailor-made for themselves. The reason for these "magical" phenomena is that these App remember our browsing history, they understand our preferences based on this information, and recommend the best match for us.

After understanding the principle of big data's technology, let's take a look at what skills it takes to be a big data engineer. Generally speaking, big data technology includes three directions, big data architecture, big data analysis and big data development.

Big data architecture mainly focuses on the implementation principle, deployment, tuning and stability of Hadoop, Spark, Storm and other big data frameworks, as well as their combination skills with data flow tools and visualization tools such as Flume and Kafka, as well as the commercial application of some tools, such as Hive, Cassandra, HBase, PrestoDB and so on. The combination of dialectical technical point of view to maximize the utilization of software / hardware resources and stabilize the provision of services is the goal of big data to structure talents. Big data architect mainly focuses on architecture theory, data flow application, storage application, software application and visualization application.

Big data's analysis focuses on the establishment of data indicators, data statistics, the relationship between data, as well as in-depth data mining and machine learning, and uses exploratory data analysis to obtain more rules and knowledge. or the ability to predict and predict things in the future. Big data analysis mainly studies database application, data processing, data statistics and data analysis.

Big data development is concerned with server-side development, database development, presentation and visualization, human-computer interaction and other convergence of data carriers and data processing units, and finally achieve the functional landing of users. Big data development mainly studies database development, data flow tool development, data front-end development, data acquisition development and so on.

PS: if you want to be a big data engineer, you might as well work in these directions now.

This is the end of big data's analysis. I hope the above content can be helpful to everyone and learn more knowledge. If you like this article, you might as well share it for more people to see.

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