The Basics Of Blockchain Technology And What It Means To Big Data Analytics

Although blockchain technology only achieved widespread recognition with the advent of Bitcoin, it has been with us for almost twenty years. Likewise, many people are only becoming familiar with big data analytics recently. Despite this, trade in ETH is brisk and Ethereum has 122 million circulating tokens. Similarly, some Japanese banks are using blockchain technology for smart contracts. 

If you feel a bit left behind with all these advances, don’t worry. Many of them, like smart contracts, are blockchain programs that operate automatically. For example, when applying for a bank loan, the outcome can be communicated to you immediately, monies deposited in your account, and repayment monitored without human involvement. However, savvy business managers are realizing the necessity of learning about the latest technological developments and taking advantage before these become mainstream. This gives them a large competitive edge.

Here we help you to grasp the basics of the meeting ground for blockchain and big data. 

An Introduction To Blockchain Technology

Blockchain technology has the qualities of reliability, increased security, immunity to tampering with data (through unchangeable time stamps), cost-effectiveness, and works off a decentralized ledger system. At the same time, it can ensure user control and privacy. Think of it as a ‘new’ way of carrying out transactions and storing that information. This has been useful in its best-known form, cryptocurrency trade, such as buying and selling ETH via a trading platform like https://www.okx.com/markets/prices/ethereum-eth. 

But blockchain technology has many applications that go beyond cryptocurrency. 

Each transaction that takes place on a blockchain ledger is stored in a unique block. This information is validated by all the users currently on the network, thus it cannot be altered. The individual blocks are connected as a chain. Blockchain is highly transparent, preserves the integrity of data, and has heightened security.

An Introduction To Big Data And Big Data Analytics

Big data, essentially, consists of immense sets of data. An example of big data is the amount of information involved in market trends in social media advertising. Or think of the data a bank must store for all its transactions.

Most companies generate tons of data. Storing and analyzing this data can be complex. Traditional systems are not up to the task of processing huge amounts of data, hence, big data analytics is needed. 

Blockchain And Big Data

Due to the volumes of big data companies have, many utilize Cloud-based storage. This is not cheap. Blockchain provides a viable cost-saving alternative for big data storage. Blockchain can also carry out this storage role over very long periods.

 A further advantage of using blockchain is that it allows multiple users. Each user stores data in a different ledger. For example, the customer-facing rep stores information on a CRM. Sales reps store sales in sales ledgers. Distributors have their systems. Finance has yet other ledgers to deal with payments. Because blockchain is decentralized, all these users have access to information about one customer via a single network. 

Blockchain also permits big data analytics.

Hopefully, blockchain, big data, and big data analytics have become more meaningful terms to the reader.