Solana: The Next in Blockchain

Blockchain, a term synonymous with Bitcoin or Dogecoin, disrupted the global equity market when first launched. A highly hyped terminology, blockchain is nothing more than a digital system for recording transactions and related data in multiple places at the same time.

It is a type of distributed ledger technology, where every transaction in the ledger is authorized by the digital signature of the owner. This makes ledgers tamper-proof. Hence the information in the digital ledger is highly secure.

Now, its application has expanded to many areas. From supply chain and logistics to BFSI, from manufacturing to entertainment, blockchain has helped streamline processes and increase efficiency.

It is a common belief that blockchain and cryptocurrencies like Bitcoin, and Solana are the same. But in reality, cryptocurrencies rely on blockchain technology to be secure.

What makes Blockchain so popular?

Highly Secure

As blockchain technology uses digital signatures it is almost impossible to corrupt or change one user’s data by the other user without a specific digital signature.

Decentralized System

There is no need for regulatory bodies like the government or banks to approve transactions. In blockchain, transactions are done with the mutual consensus of users resulting in safer and faster transactions.

Automation Capability

It is programmable and can generate systematic actions, events, and payments automatically when the criteria of the trigger are met. So validating transactions is completely automated.

How Does Blockchain Technology Work?

Blockchain is a combination of three leading technologies:

Cryptographic keys

A peer-to-peer network containing a shared ledger

A means of computing, to store the transactions and records of the network

Each individual has two cryptographic keys – A private key and a Public key. The data is digitally signed using the Private key and can be verified using the public key.

Also if user-1 wants to send some transaction data to user-2 then he/she will hash the data with user-2’s public key, so only user-2 can confirm the transaction using his/her private key.

The digital signature is merged with the peer-to-peer network; a large number of individuals who act as authorities use the digital signature to reach a consensus on transactions.

Blockchain users employ two cryptography keys to perform different types of digital interactions over the peer-to-peer network.

Secure hashing in blockchain

Blockchain technology uses hashing and encryption to secure the data, relying mainly on the SHA256 algorithm.

Secure Hash Algorithm-256(SHA-256) is a cryptographic hash function designed by the United States National Security Agency (NSA). SHA 256 produces fixed size 256 bits output for variable-size input.

The sender’s private key and public key, the receiver’s public key, and the transaction are hashed using SHA256 and transmitted all over the network, and added to the blockchain after verification. The SHA256 algorithm makes it almost impossible to hack the hash encryption, which in turn simplifies the sender and receiver’s authentication.

What is Solana?

Solana is a blockchain platform designed to host decentralized, scalable applications.

Founded in 2017 by Anatoly Yakovenko, and co-founded by Raj Gokul (COO at Solana), Solana (Solana’s cryptocurrency is SOL) is currently backed by experiences from top organizations like Google, Microsoft Intel, etc.

It is a web-scale blockchain that provides fast, secure, scalable, and decentralized apps. The system currently supports 50,000 TPS (Transactions per second) and 400ms Block Times. The overarching goal of the Solana software is to demonstrate that there is a possible set of software algorithms using the combination to create a blockchain. So, this would allow transactions to scale proportionally with network bandwidth satisfying all properties of a blockchain: scalability, security, and decentralization. Furthermore, the system can support an upper bound of 710,000 TPS on a standard gigabit network and 28.4 million TPS on a 40-gigabit network.

The core innovation that underlays the Solana Network is Proof of History, — a proof of historical events. Utilizing Proof of History creates a historical record that proves that an event has occurred at a specific moment in time. Whereas other blockchains require validators to talk to one another to agree that time has passed, each Solana validator maintains its clock by encoding the passage of time in a simple SHA-256, sequential-hashing verifiable delay function (VDF).

One of the most difficult problems in distributed systems is agreement on time. I believe Proof of History provides this solution and Solana is built using blockchain-based on it.

Nodes in the blockchain network which is a distributed system can’t trust an external source of time or any timestamp that appears in a message. There are solutions like Hashgraph which verify if the timestamp in a message is accurate but these methods are very slow.

What if instead of trusting the timestamp you could prove that the message occurred sometime before and after an event? When you take a photograph with the cover of the Times of India, you are creating a proof that your photograph was taken after that newspaper was published, or you have some way to influence what Times of India publishes. With Proof of History, you can create a historical record that proves that an event has occurred at a specific moment in time.

Proof of History

The Proof of History (POH) is a high-frequency Verifiable Delay Function. A Verifiable Delay Function requires a specific number of sequential steps to evaluate, yet produces a unique output that can be efficiently and publicly verified.

For a SHA256 hash function, this process is impossible to parallelize without a brute force attack.

We can then be certain that real-time has passed between each counter as it was generated, and that the recorded order of each counter is the same as it was in real-time.

Verification in POH

While the recorded sequence can only be generated on a single CPU core, the output can be verified in parallel.

Each recorded slice can be verified from start to finish on separate cores in 1/(number of cores) time it took to generate.

Architecture on how to interact with Solana

Client programs are exposed to users through web applications or CLI. Client code is language agnostic. It can be written in programming languages such as Python, Rust, JavaScript, C++, etc. The client program makes requests to JSON RPC. JSON RPC routes data to the Solana program that is on the chain. Solana currently supports writing programs in Rust and C/C++. The program modifies the state of the blockchain which is called Account. JSON RPC is the middle layer that routes objects sent by clients to the Solana program. These objects are called transactions(tx). This program further processes the transactions to modify the state of the account.

Clients can also request the data. Data that was written into the account can be requested by the user using JSON RPC.

Goal of Solana programming

As discussed before, the goal of the Solana program is to take in user input to modify the chain state.

https://github.com/solana-labs/example-helloworld.git is a GitHub link to a simple Solana project.

This project comprises:

A simple on-chain hello world Solana program written in Rust.

A client program is written JS using Solana web3.js SDK. The client program can simply send “hello” to an account and get back the number of times “hello” has been sent.

Now let’s look into one of the use cases of the Solana blockchain in the health insurance sector

Blockchain in health insurance to simplify Claim settlement process

Claim process can be divided into three main phases

Phase 1: Insurance Providers register on Public Blockchain

In the first phase, the process will be more or less as defined below:

The main stakeholders involved in the first phase are Insurance Providers, Insurance Brokers, and policy portal admins. Every stakeholder involved in the process would have their private keys to add records to the blockchain. Insurance Providers who provide different types of insurance can add the policy details on the public blockchain. For example, if a health insurance provider has to add the plans, they would save details like claim bonus, types of treatment covered, network hospital details, etc. on the public blockchain.

Insurance Brokers will be accessing the details saved by insurance providers on the public blockchain and can rate the insurance policies in the blockchain. The rating provided will help insurance companies and consumers to make informed decisions. Policy portal admins will fetch the insurance plans from the blockchain and add them to their portal. Using blockchain, policy portals like “Policybazaar” spend less time and manual effort contacting insurance providers like “care health insurance”.

Phase 2: Consumers Search and Buy Policies

The stakeholders involved in the second phase are Consumers and insurance companies. Consumers search for the specific insurance policy using their mobile app or website. A list of relevant policy details saved on the public blockchain will be fetched and displayed.

After a customer selects the insurance plan from a specific insurance provider, the next step is to buy the policy. So, the consumer would have to upload necessary documents such as address proof, income proof, etc. to the distributed database. These documents will have their addresses hashed and stored on the private blockchain.

Insurance Companies get notified as soon as the consumer buys the insurance. Insurance companies start verifying the consumer’s details and add the consumer to their private blockchain after validation. Acknowledgment is sent by insurance providers to the consumers about plan activation because the records of transactions stored on blockchain are immutable and traceable, there will be no insurance fraud chances.

Phase 3: Claim Request

The stakeholders involved in the third phase of the blockchain insurance process are:

Consumers, who require a claim in case of any damage, loss, medical treatment, or accident.

Loss Adjuster/Auditor, who verifies if the consumer is liable to get the claim amount or not.

Insurance Company, which provides the claim to the consumers.

In the case of medical treatment, a consumer requests the claim amount from the insurance provider. For example, suppose some consumer is diagnosed with some illness and wants to undergo treatment with a cashless claim. Consumers would have to share the documents supporting evidence on the private blockchain such as scan reports, doctor’s advice, etc.

The documents will be saved in a private blockchain which will be visible to the insurance company. The insurance company verifies the documents and sends the claim account’s breakdown to the consumer. The claims amount is automatically transferred to the consumer or hospital (cashless claim) with the help of smart contracts.

Current challenges faced in Health Insurance

The healthcare insurance industry is one of the most inefficient, fraud-prone sectors today. It faces multiple challenges with which blockchain technology can help significantly.

With blockchain technology, healthcare insurers can:

Maintain patient privacy
Give data sharing controls to patients
Store time-stamped medical records with cryptographic signatures on a shared ledger.
Enable fine permission settings to ensure regulation compliances

MedRec

Introduced by MIT, MedRec is a decentralized medical records management system that indexes healthcare records on the blockchain and allows access to authorized individuals. It helps to ensure the privacy of patients, along with easing the information verification process.

The first implementation of MedRec was done by using the Ethereum blockchain platform. The code is open-source, and the developers of MedRec are working with new healthcare IT center to develop a deployed network.

In a nutshell

Blockchain is a highly secure decentralized system that eliminates regulatory authorities. This makes transactions made using blockchain secure and fast compared to traditional approaches. Apart from cryptocurrency Blockchain technology can be used in multiple domains like Insurance, real estate, money transfer, manufacturing, etc.

Solana has solved the problem of timestamp verification using Proof of History. It can support up to 50k transactions per second because of POH which is faster than “proof of work” used in bitcoin or “proof of stake”.

As we saw in the blockchain-based insurance use case, blockchain and Solana can revolutionize the insurance industry by streamlining time-consuming insurance processes. Blockchain solves a lot of practical problems that exist in the current health insurance sector, this includes maintaining patient privacy and storing time-stamped medical records with cryptographic signatures which are tamperproof.

About the author:

Imran is a Sr. Software Engineer at Mantra Labs working on AI/ML-related projects. A passionate technologist, he has worked in the field of NLP and Computer Vision. Apart from tinkering with new technologies like blockchain, his interests are playing Badminton and chess.

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.