How about hive digital currency

1、 Different definitions:

1. virtual currency:

virtual currency refers to non real currency

digital currency is an alternative currency in the form of electronic currency. Both digital gold coin and cryptocurrency belong to digiccy

3. Cryptocurrency:

cryptocurrency is a kind of transaction medium that uses cryptography principles to ensure transaction security and control the creation of transaction units

4. Token (token):

a kind of article whose shape and size are similar to currency, but the scope of use is limited and has no currency effect, and its token is the homonym of token in English

1; It can also be said that virtual currency is personalized currency. In another way, it can also be called information currency

2. Digital currency:

is an unregulated and digital currency, which is usually issued and managed by developers and accepted and used by members of specific virtual communities

cryptocurrency is based on the decentralized consensus mechanism, which is opposite to the banking and financial system relying on the centralized regulatory system

4. Token (token):

usually needs to be exchanged for money, used in shops, playgrounds, mass transportation and other places, as a voucher to use services and exchange goods

extended data

at present, digital currency is more like an investment proct, because it lacks a strong guarantee agency to maintain its price stability, and its role as a value measure has not yet appeared, so it can not be used as a means of payment. As an investment proct, digital currency cannot develop without trading platform, operating company and investment company

digital currency is a double-edged sword. On the one hand, the blockchain technology it relies on has been decentralized and can be used in other fields except digital currency, which is one of the reasons why bitcoin is popular; On the other hand, if digital currency is widely used by the public as a kind of currency, it will have a huge impact on the effectiveness of monetary policy, financial infrastructure, financial market and financial stability

Ha Yu miner, windows can mine with one click, and it hasn't pumped water yet. At the same time, they also have an app that can monitor your machine at any time

linux system can also mine, two-step setting can mine

mining in two steps by Linux system

is such a simple operation

29 well-known companies in the world hold more than US $30 billion in digital assets

recently, a new trend has emerged, because many well-known companies have decided to use bitcoin (BTC) for reserves rather than traditional assets. After MicroStrategy bought $250 million worth of BTC, the trend became popular. Soon after, the company continued to buy more bitcoin. After several purchases, MicroStrategy has increased its holdings to 70470 BTC or. 336% of its supply. After the acquisition of MicroStrategy, square Inc. and other companies began to acquire. And ruffer investments joined the bitcoin buying trend

according to bitpointstudies.org, a portal, many companies hold more than 1.1 million bitcoins or more than $30 billion worth of encryption assets. After MicroStrategy, a billion dollar company, bought $250 million worth of bitcoin in August, the large amount of bitcoin reserves held by well-known companies began to expand. Now 29 companies hold cryptocurrency instead of traditional reserves such as stocks and cash

so far, a total of 29 companies have been listed on bitpointresearches.org, which divides all companies into three different parts. Publicly traded, private and ETF like holders

1. The first step is to log on to the Com2us official website. Then we need to click "login" → "free registration member" in the upper right corner

First of all, we need to understand the Java language and the Linux operating system, which are the basis for learning big data, regardless of the order of learning

java: as long as you know some basics, you don't need deep Java technology to do big data, learning java se is equivalent to learning big data

linux: because big data related software runs on Linux, Linux needs to learn more solidly. Learning Linux well will help you quickly master big data related technology, and enable you to better understand the running environment and network environment configuration of Hadoop, hive, HBase, spark and other big data software, If you learn shell, you can understand the script, which makes it easier to understand and configure the big data cluster. It can also make you learn the new big data technology faster

Hadoop: This is a popular big data processing platform, which has almost become the synonym of big data, so this is a must. Hadoop includes several components HDFS, maprec and horn. HDFS is the place to store data, just like the hard disk of our computer. Files are stored on it. Maprec processes and calculates data. It has a feature that no matter how big the data is, it can run out of data as long as it is given time, but the time may not be very fast, so it is called batch processing of data

zookeeper: This is a panacea. It will be used when installing Hadoop ha, and it will also be used in future HBase. It is generally used to store some mutual cooperation information, which is relatively small, generally no more than 1m. The software that uses it depends on it. For us, we just need to install it correctly and let it run normally

MySQL: we've finished learning big data processing. Next, we'll learn the small data processing tool MySQL database, because it will be used when installing hive later. What level does MySQL need to master? You can install it on Linux, run it, configure simple permissions, change the password of root, and create a database. The main thing here is to learn the syntax of SQL, because the syntax of hive is very similar to this one

sqoop: This is used to import data from MySQL into Hadoop. Of course, you can also export the MySQL data table to a file and put it on HDFS without using this. Of course, you should pay attention to the pressure of MySQL in proction environment

hive: This is an artifact for those who know SQL syntax. It can make it easy for you to deal with big data, and you won't have to work hard to write maprec program. Some people say that pig? It's about the same as pig

oozie: now that you have learned hive, I believe you need it. It can help you manage your hive or maprec, spark scripts, check whether your program is executed correctly, send you an alarm when something goes wrong, and help you try the program again. Most importantly, it can help you configure the dependency of tasks. I'm sure you'll like it, or you'll feel like shit when you look at all the scripts and the dense crond

HBase: This is the NoSQL database in the Hadoop ecosystem. Its data is stored in the form of key and value, and the key is unique, so it can be used for data plication. Compared with MySQL, it can store a lot of data. So it is often used for the storage destination after the completion of big data processing

Kafka: This is an easy-to-use queue tool. What is queue for? Line up to buy tickets, don't you know? When there is too much data, you also need to queue up to process it. In this way, other students who work with you will not shout. Why do you give me so much data (such as hundreds of gigabytes of files) and how can I handle it? Don't blame him because he doesn't work in big data. You can tell him that I put the data in the queue and take them one by one when you use it, In this way, he won't complain any more. He will optimize his program immediately, because it's his business if he can't handle it. It's not the question you give. Of course, we can also use this tool to store online real-time data or HDFS. At this time, you can use it with a tool called flume, which is specially used to provide simple data processing and write to various data receivers (such as Kafka)

spark: it is used to make up for the shortcomings in the speed of data processing based on maprec. It is characterized by loading data into memory for calculation, rather than reading the slow hard disk which is dying and evolving very slowly. It's especially suitable for iterative operations, so the algorithm streams are particularly interested in it. It's written in scala. Java language or Scala can operate it, because they all use the JVM