Big Data has been around for a while, and blockchain technology is currently riding the top of the wave of popularity. What are the potential outcomes of combining these two innovations?
The volume of transactional data stored within various ledgers is getting enormous as cryptocurrencies, and other real-world applications of blockchain technology become more prominent. Using traditional cloud storage providers like AWS or Azure to store these massive data lakes would be extremely expensive.
As a result, blockchain technology and big data have seen a lot of growth because businesses generate many data. At this crucial point, blockchain technology emerges as a reliable, cost-effective, and decentralized ledger to keep anonymous data generated daily.
Big data is a game-changer in today’s tech-savvy world, allowing businesses to use real-time pioneering insights to improve performance. Extensive data services assist governments, organizations, and even small and medium-sized businesses.
Read on to understand how combining these two technologies can create new possibilities for innovation in your business.
Current Challenges of the Big Data Industry
By implementing a big data strategy, organizations can reduce operational costs, shorten the time to market, and develop innovative products. On the other hand, Enterprises face dozens of new significant data challenges in getting initiatives from the drawing board to execution.
Here are some of the most significant problems that arise while dealing with Big Data:
1. Professionals with Inadequate Knowledge
Companies require trained data specialists to run these latest technologies and massive data tools. Data scientists, data analysts, and data engineers will be necessary to make sense of massive datasets and work with the technologies.
One of the most significant challenges of Big Data companies is the lack of highly skilled professionals. Data processing tools have advanced rapidly, but most experts have not.
When employees are not aware of using or storing the data correctly, they might misuse or ignore the data leading to inefficient analytics. They might not even use the database properly for the backup, and these mistakes would lead to data scarcity.
2. Mishandling and Misunderstanding of the Massive Data
Due to a lack of understanding, companies fail to succeed in their Big Data efforts. Employees may be unaware of what data is, stored and processed, or where it originates. Even if data specialists have a clear picture of what’s going on, others may not.
Employees who don’t comprehend the importance of knowledge storage may be unable to keep a backup of critical information, and they couldn’t save data in databases appropriately. As a result, it’s tough to find this crucial information when it’s needed.
The sole purpose of tackling big data and analytics is to understand the massive data and the ability to segregate and use it most appropriately. If the employees lag behind these crucial tactics, the outcome will lead to blunders due to data usage in the wrong places.
3. Issues with Constantly Growing Data
The proper storage of these vast amounts of data is one of the most critical concerns of big data. The amount of data saved in data centers and company databases continually and large data sets are difficult to manage as they multiply.
Documents, movies, audio files, and text files are among the unstructured data sources.
Companies often introduce different data handling tools to acknowledge their growing data. The employees should keep pace with understanding the mechanisms to integrate them correctly. Any misuse in handling the tools or overlooking the data stored at multiple places in multiple formats would lead to incorrect results.
4. Confusion Regarding Big Data Tool Selection
Businesses are confused when selecting the simplest possible big data tool for their vast projects. Is HBase or Cassandra the most accessible system for storing data? Is Hadoop MapReduce sufficient, or will Spark be a far superior data analytics and storage solution?
These issues trouble companies, but they aren’t always able to find solutions. They often make poor selections and use inefficient tech. As a result, resources such as money, time, effort, and work hours are spent in vain.
Updating the tools or introducing new tools to handle the ever-growing data and produce accurate results is never a wrong idea. But, choosing the appropriate tool for your data is always a challenge that troubles organizations and an incorrect tool selection incompatible with your data are of no use.
5. Data Security
One of the most significant issues of enormous data is securing these massive quantities of data. It is common for companies to put data security on the back burner while focusing on interpreting, preserving, and analyzing their data sets.
Unprotected Data repositories can become breeding grounds for malicious hackers, so this is rarely a wise choice. An information breach can cost a company up to $3.7 million.
Data and its security should always go hand in hand. Companies often require highly skilled cybersecurity professionals to handle their data for the following purposes:
1. Data segregation
2. Data Encryption
3. Access Control
4. Identity Control
5. Real-time monitoring
6. Implementing end-point security, etc
What is the relationship between Big Data and Blockchain?
Blockchain has the potential to be a valuable tool for storing data online. In a transaction, different parties store transactional information on different ledgers.
With the help of Blockchain, all of these parties can get access to a single network.
The network can record transactions, and all the involved parties can verify these transactions. It will be simple to get these details because all the information will be kept in the Blockchain.
Users can easily view historical transactions because of the Blockchain technology’s design. It becomes simple to trace the origins of a transaction.
How can Blockchain Transform Big Data?
Here are some of the factors how Blockchain can serve as a driving force behind Big Data:
1. Data Exchange
Thanks to data sharing services like Dock, working professionals may maintain their employment profiles on a single platform rather than working through various profiles on multiple job sites.
Dock also collects credentials and other experiences from various platforms and stores them all on the blockchain, allowing professionals to build comprehensive profiles.
Study results show that 73% of corporate data isn’t used for data analysis. However, blockchain has the potential to alleviate these constraints by making data sharing more secure and straightforward without incurring significant infrastructure expenses.
The high expense is the most significant barrier to incorporating big data analytics into existing infrastructure. Today, blockchain solutions make data analytics tools more accessible by decentralizing the necessary technology.
3. Data Monetization and Sharing
In today’s world, data is the most crucial piece of information, and combining blockchain and big data can help advance the way data analytics is shared and monetized.
Customers can acquire negotiating power over firms, allowing them to choose which organizations have access to their data and which do not.
Benefits of Using Blockchain Technology in Big Data
1. Improved Data Quality
As diverse sources collect data in different formats, data scientists spend most of their work on data integration. By storing data on the blockchain, you can improve the quality of the data because it is well organized and complete. As a result, data scientists may work with high-quality data to make more accurate real-time forecasts.
2. Strengthening Data Security
As the number of devices linked to the Internet grows, the amount of data kept in third-party places such as the cloud multiplies, introducing new concerns, such as data breaches or threats from unscrupulous third parties.
Traditional security solutions, such as firewalls, cannot solve the issue of big data since companies have no control over the data because it is not held within the organization’s network perimeter. The use of blockchain to store large amounts of data has the potential to solve this problem.
The blockchain network’s encrypted and decentralized data storage makes illegal access to the data extremely difficult.
3. Preventing Fraud
To detect fraudulent transactions, existing extensive data systems rely on examining patterns in historical data. As a result, big data will not be able to tackle the problem of fraudulent financial transactions.
Financial institutions can monitor each transaction in real-time thanks to the storage of massive data in the blockchain, allowing them to examine possibly fraudulent transactions on the fly.
As a result, incorporating blockchain into big data can assist financial organizations in preventing fraud and safeguarding their consumers.
4. Streamlining Data Access
By streamlining data access online, the usage of blockchain would simplify the life cycle of big data analytics. Authorized users can access secure, trusted data without going through multiple checks by involving multiple departments in an organization in a typical blockchain.
5. Real-Time Analysis
Since blockchain records every transaction, it allows for real-time big data analytics. The banks and financial institutes can settle the cross-border transactions, including significant sums, in near real-time as the blockchain-integrated big data analytics enables the financial institutes to settle the transactions rapidly.
Banks can also monitor changes in data in real-time, allowing them to make real-time decisions such as stopping transactions.
6. Enhanced Data Sharing
The use of blockchain in conjunction with big data allows service providers to exchange data with other parties while minimizing the danger of data5 leakage.
The blockchain can also reduce the amount of repeated data analysis since each experiment is recorded.
Real-Life Scenarios of Blockchain in Big Data
Blockchain is the technology behind cryptocurrencies such as bitcoin and Ethereum. On the other hand, big data is a more advanced data science notion that involves a larger dataset with greater diversity, quantity, and velocity. We examine these datasets for patterns, associations, and trends of interest.
Blockchain, interestingly, is a form of distributed ledger that irreversibly records transactions. Blockchain has a high level of trust, removing the need for third parties to control transactions and ensuring that data is unchangeable.
Many applications in data science use blockchains to ensure data integrity while doing data analysis and sharing. Following is a list of some of the most popular real-time applications of Blockchain and Big Data:
Storj is an end-to-end decentralized storage project that uses unused hardware and bandwidth to enable peer-to-peer storage contract authentication between providers and users.
It all begins with encrypting client-side files, split into “shards.” These shards are kept three times on the farmer’s side to ensure backups.
Only the client has access to the data, providing greater security than standard centralized cloud services. Renters can check on the farmers’ files and pay for the storage system’s upkeep using the Storj cryptocurrency. Renters only pay for the space they use, with no additional setup costs or user restrictions fees.
Omnilytics is a big data analytics blockchain platform that gives insights into the sales, marketing, and retail industries. It integrates data from many industries using blockchain, big data analytics, machine learning, artificial intelligence, and other technologies.
The platform offers data analytics and related services such as competitor benchmarking, trend research, and pricing analysis for clients. Smart contracts, distributed data fingerprinting, data exchange, and other services use blockchain to track data trends and provide incentives through micropayments.
Provenance is a blockchain platform primarily used in supply chain management that aids in collecting and sharing critical product information in a reliable, safe, and accessible manner.
The six participants in the blockchain architecture are the producer, the manufacturer, the registrar, standard organizations, agents such as certifiers or auditors, and ultimately the clients.
The protocol gives consumers access to information about the items\’ origins, transit through various points in the supply chain, product quality, and environmental effect.
Putting it All Together
However, enterprises should be ready to deal with several drawbacks and complications when implementing a blockchain to improve big data, just as they are with any innovative solution. We have a specialized staff of highly skilled blockchain developers at Parangat who are well-versed in all aspects of this fascinating technology. Contact us, and we’ll gladly help you implement blockchain technology into your extensive data services.
With roll up sleeves, dive in and get the job done approach, it was in the year 2010 when Sahil started Parangat Technologies. Emphasizing a healthy work culture and technology-driven company, he has successfully created a workplace where people love to work and live. He is a software engineer and a passionate blockchain enthusiast.
1 thought on “Big Data and Blockchain: How Are They Related?”
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