Shulou(Shulou.com)06/01 Report--

Today, I will introduce to you what are the seven key points for the successful construction of big data's infrastructure. The content of the article is good. Now I would like to share it with you. Friends who feel in need can understand it. I hope it will be helpful to you. Let's read it along with the editor's ideas.

Big data is an important part of the IT operation of many enterprises today. According to IDC, a well-known research company, big data's market output value will reach 187 billion US dollars by 2019. Big data is a key part of data analysis, and analysis is the basis of machine and human business intelligence and decision-making. Because it is clear that without some kind of infrastructure, you cannot get all kinds of data: big data, small data, or completely correct data, so it is necessary to take a look at several factors that contribute to the success of big data's architecture.

Some of these factors may seem obvious, while others are more subtle. In fact, all factors together have a huge impact on the analysis and actions that your big data system will support.

Of course, it is not that only these seven factors will have an impact on the way big data's infrastructure works. The big data system involves many parts, but these seven factors are worth considering because they are the basis of many other parts and processes.

Right now, you may be using big data, even if you work in a small company. This is thanks to the existing infrastructure parts-many of which are accessible to even the smallest IT departments.

With this accessibility, employees of small companies who do not have their own expertise in data science may feel confused and frustrated. If you are in this situation, the editor will not eliminate all your confusion, but it will allow you to start asking potential service providers and suppliers some specific questions.

Big data is much more than Hadoop.

In normal conversation, big data and Hadoop are often used differently. Such a result is regrettable, because big data is much more than Hadoop. Hadoop is a file system (not a database) designed to transfer data across hundreds or thousands of processing nodes. It is used in many big data applications because, as a file system, it is good at dealing with unstructured data, which is not even like the data around it. Of course, some big data is structured, for which you need a database. But the database is a different factor introduced in this article.

Hive and Impala bring the database into Hadoop

What we are talking about here is a database for structured data in big data's world. If you want to keep the Hadoop data platform in order, then Hive may be just what you need. This infrastructure tool allows you to handle SQL-like operations for Hadoop that is very unlike SQL.

If you have some data that can easily be placed in a structured database, then Impala is a database designed to reside in Hadoop, and it can also use the Hive commands you developed during the conversion from Hadoop to SQL. All three (Hadoop, Hive, and Impala) are Apache projects, so they are all open source.

Spark is used to deal with big data

So far, we've been talking about storing and organizing data. But what if you want to actually process the data? At this point, you need an analysis and processing engine like Spark. Spark is another Apache project, which includes a number of open source and commercial products to do something useful with the data you add to data lakes, warehouses, and databases.

Because it can access any library of data you can imagine, Spark can be used to process all kinds of data stored in various places. It is also open source, so you can modify it at will.

You can SQL big data.

Many people know how to build SQL databases and write SQL queries. In the face of big data, there is no need to waste this expertise. Presto is an open source SQL query engine that allows data scientists to use SQL queries to query databases that reside in any environment, from Hive to proprietary commercial database management systems. It is used by large companies such as Facebook for interactive queries, and the phrase interactive query is the key. Presto is like a tool for performing ad hoc interactive queries against large datasets.

There is a place for online storage

Some big data tasks require ever-changing data. Sometimes, this is data that is added on a regular basis, and sometimes it is changed through analysis. In any case, if your data is written as frequently as read, then you need to store the data locally and online. If you can afford it, you also want the data to be stored on solid-state storage media, as this will greatly accelerate the speed-an important consideration if you are anxiously waiting for the results to return at retail or trading venues.

Cloud storage also has a place.

If the analysis is carried out on a larger aggregate database, then the cloud is the platform for *. Aggregate the data and transfer it to the cloud, run the analysis, and then dismantle the instance. This is the kind of elastic demand response that the cloud is good at. Operations are not significantly affected by any latency problems that may be caused by the Internet. If you combine real-time analysis on dedicated local systems with in-depth analysis running in the cloud, you are close to realizing the full potential of big data's infrastructure.

Don't forget to visualize.

It is one thing to analyze big data and another to show the results in a way that makes sense to most people. Graphics are very helpful to the whole "interpretation" work, so data visualization should be regarded as a key part of big data's infrastructure.

Fortunately, there are many ways to achieve visualization, from JavaScript libraries to commercial visualization packages to online services. What's the most important thing? Select a small part of it, give it a try, and let your users try it.

The above is the whole content of what are the seven key points for the successful construction of big data infrastructure. More content related to what are the seven key points for the successful construction of big data infrastructure can search the previous articles or browse the following articles to learn! I believe the editor will add more knowledge to you. I hope you can support it!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.