In the last decade, industries have been stumbling across increasing use-cases for blockchain technology. More and more industries are finding that implementing blockchain technology will either take them to the next level or end up replacing them. Blockchain and big data have emerged as the technologies that can forge a symbiotic relationship.
The rise of cloud storage led to the exponential growth of data generated from nearly all corporate systems, IoT devices, and the internet as a whole. However, merely having the data does mean that companies can generate meaningful insights from the data, which is the main objective of Big Data. Getting valuable insights from data depends on data integrity which is a significant concern in Big data. The solution to this problem is found in a somewhat unexpected source: Blockchain Technology.
Before we go further, let’s understand the basic concepts of Blockchain Technology and Big Data.
Basics of Blockchain Technology
According to LeeWayHertz, Blockchain is a time-stamped series of immutable records of data managed by a cluster of distributed nodes not owned or governed by any central authority. Each of the blocks of data is secured and bound together by cryptographic principles.
Besides the many properties of Blockchain, blockchain technology has three properties that significantly supplement big data:
Decentralization: Blockchain technology is not controlled or governed by any central authority. Any data collected is distributed throughout the network, and all participating nodes receive a copy of the data. This ensures the security and privacy of the data.
Transparency: Since the blockchain ledger is a distributed database, all nodes with the necessary authorization can access the data stored on the Blockchain. This maintains a consistent ledger across the network, making it much more difficult for malicious and fraudulent transactions to occur.
Immutability: Validated data on the Blockchain is appended in the form of structured blocks linked to other blocks cryptographically. As such, once a transaction has been recorded, it is almost impossible to alter the data.
What is Big Data?
According to a video by UpGrad, Big Data can be described as high-volume, high-velocity, and variable information assets that demand cost-effective, innovative forms of information processing for enhanced insights and decision making. However, in simple terms, Big Data is data that is so large, fast, and complex that it’s difficult or impossible to process it using traditional methods.
The act of accessing, storing, and analyzing large amounts of data has been there for a long time. However, it wasn’t until the early 2000s when Doug Laney introduced the term Big Data and articulated the now-mainstream definition of big data as the three V’s:
Variety: Variety of data refers to the various types of Big Data, unstructured, structured, and semi-structured data gathered from multiple sources. In the past, data collection was limited to spreadsheets and databases. Today, data is collected from a vast array of sources such as emails, PDFs, Social Media, videos, and so much more.
Velocity: Velocity refers to the speed at which data is created in real-time.
Volume: Big Data indicates the enormous volume of data generated per day from a plethora of sources. According to IBM, businesses around the world generate almost 2.55 quintillion bytes of data per day.
Types of Big Data
Structured data: This refers to any data that can be stored, accessed, and processed in the form of a fixed format. A good example is table data stored in a database.
Unstructured data: This is any data with an unknown form or structure, such as the Google search output.
Semi-structured data: This form of data contains both forms of data, such as data represented in an XML file.
Examples of Big Data
According to Guru99, here are some examples of Big Data:
- The New York Stock Exchange market which generates about 1 Terabyte of new trade data per data
- Facebook generates over 500 Terabytes of data per day. The data is primarily in terms of photos, videos, messages, comments, among other sources.
- A single jet engine on the other hand generates ten-plus Terabytes of data in just 30 minutes of flight, with many flights globally, the data reaches up to various Petabytes.
Biggest challenges of Big Data
As a result of the speed, variety, and enormous volume of data generated, Big Data implementation has multiple challenges:
- As the name suggests, Big data deals with huge volumes of data. Even with modern technological advancements, the sheer amount of data generated is not only growing exponentially but also very large to process. As such, it becomes challenging to draw significant insights from the data.
- Security and fraud detection are also enormous challenges since cleaning the data is a labor-intensive task. As such, data analysts are prone to missing some security issues.
- Big Data is acquired from all devices across the internet; therefore, the data’s integrity is also questionable.
- Today, centralized cloud providers like AWS and Google are in charge of storing Big Data, which are centralized entities. If a company shuts down, then all that data will be lost.
- Keeping up with Big Data technology is also a challenge as it’s constantly evolving.
When Blockchain meets Big Data
The integration of Blockchain and Big Data has the potential to deliver various exciting opportunities and solve some of the biggest challenges facing Big Data. There are several ways Blockchain can help, especially in terms of immutable entries, consensus-driven timestamping, audit trails, and confidence in the origin of the data. This means that with the Blockchain, businesses and organizations can capture, store, analyze and generate valuable insights from Big Data.
Below are some of the ways Blockchain can enhance Big Data:
Ensuring trust by enhancing data integrity
Blockchain technology ensures trust by maintaining a distributed and decentralized ledger. Any data recorded on the Blockchain must be vetted and approved by other nodes participating in the network through a consensus mechanism. Moreover, Blockchain is a transparent ledger, and any authorized node can read or write onto the Blockchain, enhancing data quality as all nodes can be held accountable for any malicious transactions.
When collecting and processing Big Data stored on the Blockchain, an organization can also determine the data’s origin and verify the data is from a trusted source. It provides a seamless way to conduct integrity checks and audit trails since it ascertains the data through linked chains.
Just like other types of data, blockchain data can also be analyzed to reveal valuable insights. As such, since Blockchain is an immutably linked ledger, organizations can use Big Data stored on the Blockchain to predict future business decisions or processes with great accuracy. With Blockchain, organizations that require real-time data analysis on a large scale can consistently observe changes in the data and make quick, efficient decisions.
Moreover, Blockchain provides structured data gathered from various devices, individuals, and organizations. Due to its distributed nature and high computational power, Blockchain allows smaller organizations to access crucial business data easily for analysis and decision making.
Blockchain enhances security in Big Data
The most significant benefit blockchain technology serves to Big Data relates to the security of the data. Blockchain is a decentralized ledger meaning that the data stored on the Blockchain is not governed by any central authority but rather the nodes participating in the network. Additionally, once a block is appended onto the chain, it cannot be altered without the network’s approval.
In the financial sector for example, Big Data has not been able to solve cases related to fraud as it relies on historical data to predict future cases. With Blockchain, financial institutions can track, evaluate risk and identify fraudulent activities in real-time and stop them before occurring.
Efficient Data Sharing and Data Access
Blockchain technology can power up Big Data and analytics by streamlining data access and sharing processes. Since Blockchain provides a distributed platform, various organizations and departments can be part of the Blockchain, where they can access relevant data and take part in data analysis. This makes the process of data access and analysis seamless and efficient.
By storing data on a blockchain, the nodes can also create various access levels depending on the type of Blockchain. For instance, Under Permissioned or Hybrid Blockchain nodes create several authorization signatures to limit access to the data. Therefore, any crucial data or analytical report is shared with nodes that are authorized and trusted.
Possibilities of Real-Time Analytics
Since Blockchain is updated after every transaction, a whole new field is emerging around accessing data and processing it in real-time. The integration between Blockchain and Big Data can help companies make real-time analytics much more achievable and reliable.
The integration of Blockchain and Big Data significantly reduces cost
Blockchain distributes data across all participating parties; therefore, instead of purchasing data from central servers’ organizations can access the data whenever they want, from their nodes. Additionally, Big Data stored on the Blockchain is immutable and linked to historical data. Hence small organizations do not have to purchase an enormous amount of data for analysis. They can accomplish this on the Blockchain.
Real-world platforms using Blockchain and Big data
While Big Data is not a new technology, the relatively new Blockchain technology has proven that it is here to stay. The integration between these two technologies will revolutionize how organizations use Big Data. Below are four companies at the forefront of creating blockchain tools for Big Data:
One of the big players in this space is Storj, an open-source, decentralized file storage solution that uses cryptography, sharding, and harsh tables to help store files on a peer-to-peer network. Storj has a community of various distributed nodes (known as “farmers”) which utilize their extra or spare hard drive space to provide storage for the platform.
Storj utilizes its native token STORJ to fuel its ecosystem and compensate farmers for their storage and bandwidth.
Omnilytics is a blockchain Big Data startup that aims to combine Blockchain and data analytics. The platform uses AI and machine learning as part of the analytical process with marketing, financial due diligence, auditing, trend forecasting, and other applications across various industries. Data providers/partners can view how their data is performing and its pricing based on usage.
Datum is a decentralized storage network driven by DAT, the Data Access Token. Datum allows users to have complete control of their data and monetize the data in an open and honest marketplace instead of being exploited by large corporations. Blockchain ensures that their data is securely stored and there are no breaches.
Provenance aims to use the Blockchain to build trust into the journey of a product. Customers get to know verified information about what the product is made of, where it came from, and its impact on the environment—producers and retailers benefit from better product-tracking and by empowering their customers with this new information.
Over time, as the data builds, producers and retailers also get insight into exactly what customers want and tailor their goods and services accordingly. Provenance’s core is creating transparency throughout the supply chain.
While blockchain technology is still in its infancy when it comes to sectors outside cryptocurrencies, its impact can already be felt on how Big Data is being processed and analyzed. From the examples above, it is clear that blockchain technology provides viable solutions to centralized Big Data challenges. With enhanced security, improved data quality, and real-time analysis, just to mention a few, Blockchain has the potential to change how Big Data is processed and analyzed fundamentally.
In the coming years, we will likely see further progress and more concrete use cases of the partnership between Big Data analytics and Blockchain. With real-time data collection, it will be interesting to see the applications of a Blockchain-Big Data ecosystem. Sure enough, Blockchain and Big Data are a match made in heaven.