By using website you agree to our use of cookies as described in our cookie policy. Learn More

TDWI Upside - Where Data Means Business

Why Blockchain Will Never Kill the Database

A fundamental difference in how data is handled and stored means the technologies are complimentary, not competitors.

Blockchain hype is out of control. Although blockchain is an amazing technology that makes data ecosystems safer, more trusted, and verifiable, it is not a panacea. The blockchain hype includes one false claim in particular, which is that because blockchains can serve as verifiable systems of records, databases are no longer the right technology to serve that purpose. This is misguided. Blockchains and databases are different kinds of systems of record and, in fact, are complimentary.

For Further Reading:

Blockchain and Your Data

Historical Data: From Data Warehouse to Immutable Blockchain

Q&A: Machine Learning, Predictive Analytics, and the Modern Enterprise

Blockchain Benefits and Challenges

There are many different blockchain technologies and networks, and they all share a basic characteristic: a record of "transaction" is not stored in just one database. Instead, a consensus of the transaction is recorded amongst an entire network of participants in an ecosystem.

Blockchain is an immutable, distributed record of transactions. It uses cryptographic algorithms to reach a consensus amongst a group of parties in a secure way, resulting in every party in the chain of transactions having an accurate record of every transaction. There is no central repository secured by a single party that may be enticed to alter the database for its own interest. The blockchain is trustworthy by virtue of its distributed model, how blocks are linked to the chain, and its consensus algorithm that makes the cost of altering it prohibitive.

Blockchains are computationally expensive. By design, the cryptographic algorithms used to derive a consensus require substantial work. As a result, there are many efforts focused on reducing the computational expense, the corresponding cryptocurrency expense, and the power expense. One approach, called anchoring, reduces the amount of data stored on the chain where transactions are batched together, hashed, and organized into timestamped blocks for inclusion into the blockchain. A receipt indicating where on the blockchain the data was anchored is then stored in databases or other durable storage, making any transaction verifiable.

One key aspect of this approach is that the data involved in the transaction is not "stored" in the anchor. Only a cryptographic hash of the data is stored. Anchoring is used to verify the original data against the hash, and to determine when it was committed to the blockchain, but it is not used to store the data. This is really a system of record because it records a hash of the transaction data whose integrity can be verified by anyone at any time. This provides an independent source of trust while maintaining the privacy of confidential data, even on public blockchains.

Blockchain Applications

What applications does a blockchain support? They break down into three categories:

  • Smart contracts ensure the consistent transfer of assets based on pre-determined rules
  • Smart assets ensure that the ownership status of any tokenized asset can be tracked, verified, and settled between parties
  • Smart IoT ensures that signals generated by devices have not been tampered with and reflect the true values sensed

Database Applications

Databases differ from blockchains in that they explicitly store data, not just hashes. Databases power two kinds of workloads: operational workloads and analytical workloads.

Operational databases, called Online Transactional Processing (OLTP) systems, power some applications. For example, consider a fraud dispute resolution system that enables a call center agent to help customers review financial transactions and file disputes about those transactions in one second or less. This requires special data structures and algorithms that can process data by many users simultaneously very fast.

Online Analytical Processing (OLAP) systems review historical transactions and derive insights from them or generate predictive machine learning models. These systems are specialized for sorting the data and computing metrics such as sums and averages. This requires high throughput.

New databases are now emerging that can combine OLTP, OLAP, and machine learning in one platform, called online predictive processing (OLPP). [Editor's note: The author's company, Splice Machine, provides an OLPP platform.]

For example, consider these three use cases:

  • Customer service call centers: call center agents responding to customer inquiries across channels such as phone, Web, or mobile apps often seconds after orders are taken
  • Personalization: machine learning models that predict what action to take with a customer in a moment
  • Predictive maintenance: machine learning models that predict when field equipment is likely to experience an outage

All of these use cases require a database -- a blockchain simply cannot perform these functions.

A Final Word

The death of the database is extremely exaggerated. Blockchains may revolutionize the integrity of transactions, but databases will always remain to power mission-critical applications, analyze those applications, and serve as the heart of AI that learns. Together, they offer many verticals a powerful combination.

About the Authors

Monte Zweben is the CEO and cofounder of Splice Machine. A technology industry veteran, Monte’s early career was spent with the NASA Ames Research Center as the deputy chief of the artificial intelligence branch, where he won the prestigious Space Act Award for his work on the Space Shuttle program. Monte is the coauthor of Intelligent Scheduling and has published articles in the Harvard Business Review and computer science journals and conference proceedings. He was Chairman of Rocket Fuel Inc. and serves on the Dean’s Advisory Board for Carnegie Mellon University’s School of Computer Science.

Pierre-R. Wolff is vice president of business development of Tierion and has spent over 20 years in Silicon Valley as a business strategist, startup advisor/mentor, and people-connector in the tech industry. Prior to Tierion, he ran a consultancy focused on helping financial and technology companies better understand the blockchain and cryptocurrency related ecosystems. In addition to advising several startups, he is an advisor to Science Blockchain, a Santa Monica-based blockchain-focused startup incubator and venture capital fund. Pierre is regularly tapped to be a moderator or a panelist at blockchain and cryptocurrency events and conferences, to discuss regulatory, business-model, and technology suitability issues. He received his MBA from EDHEC in Nice, France, and conducted his undergraduate studies at Carnegie Mellon University in Pittsburgh, PA.

TDWI Membership

Accelerate Your Projects,
and Your Career

TDWI Members have access to exclusive research reports, publications, communities and training.

Individual, Student, and Team memberships available.