Businesses today face an overwhelming “data tsunami” — a surge of data that, while valuable, often hampers application performance and strains architectural scalability. The more data an organization manages, the more challenging these issues become. With AI workloads adding to data management complexities, enterprises are looking for ways to achieve scalability, maintain performance, and meet evolving data demands.

CIO Dimension spoke with Bhanu Jamwal, Head of Presales & Solution Engineering, APAC, TiDB, to explore how a modern and innovative approach to databases is helping businesses overcome these challenges and innovate. TiDB – an open source distributed SQL database developed by PingCAP – tries to provide some answers. 

How is PingCAP’s TiDB positioning itself in the competitive database market, particularly considering the rapid growth of digital native businesses in India?

We’ve witnessed a remarkable surge in digital native businesses across India, spanning e-commerce, logistics, fintech, gaming, SaaS, and more recently, the emerging healthcare and Gen AI sectors. This explosive growth, however, comes at a cost – a massive influx of data.

Their increasing reliance on data creates a greater demand for scalable and reliable data solutions. Traditional databases often struggle to scale to meet this kind of growth and volume. Imagine a scenario where an e-commerce platform, during a peak sales event experiencing a huge surge in transactions. Traditional databases, such as MySQL or PostgreSQL, may encounter performance bottlenecks under such pressure.

Businesses are therefore compelled to either implement workarounds or adopt modern database solutions that can address these challenges. And these challenges now go beyond just scale. High availability is paramount; downtime is simply not an option today. Real-time analytics is another critical factor businesses. For instance, a bank needs to be able to detect fraudulent transactions in real-time to safeguard customer funds. So, it needs, scalability, availability and analytics- all at the same time. AI introduces further complexities and challenges to this mix. 

Recognizing these evolving needs, TiDB was born in 2015. We strive to address these challenges with our distributed architecture, which allows businesses to scale as their data grows. Distributed SQL databases have in fact emerged as an effective answer to modern data challenges. This approach has been gaining traction, as evidenced by Amazon’s recent entry into the distributed SQL market, acknowledging the growing demand for such solutions. 

How do you view the SQL vs. NoSQL debate, and how does TiDB position itself in this context?

The SQL vs. NoSQL debate has been ongoing for some time with both playing pivotal roles in enterprise environments. While NoSQL databases offer flexibility for specific use cases, they often require significant development effort to maintain data structures. SQL, on the other hand, remains the de facto standard for data access, with a vast pool of skilled developers. 

It’s interesting how many NoSQL databases, which originally distanced themselves from SQL, are now adding SQL layers to their platforms. It shows that SQL remains a simple and effective way to access data. 

TiDB’s aim is to bring best of both by offering a distributed SQL solution. Our Hybrid Transactional/Analytical Processing (HTAP) capabilities are designed to bridge the gap between SQL and NoSQL databases and handle both transactional and analytical workloads. 

How is AI and ML, particularly the rise of LLMs, changing the game for distributed databases, and how is TiDB capitalizing on these trends?

The rise of vector database is a trend worth mentioning in this context. Vector is becoming a powerful engine for today’s AI workloads. Though it has been around for a while, the adoption has rapidly scaled in recent years. These databases are becoming increasingly crucial for storing and processing the data generated by AI/ML models. 

But, this also makes the environment quite complex for enterprises. They often manage multiple databases for different workloads—a transactional database, a separate one for analytics, and yet another for vector storage. Managing databases is a complex process. 

At TiDB, we see an opportunity to simplify this process by offering a unified solution. Our vision is to provide a single database that handles all these workloads, eliminating the need for multiple systems. TiDB supports vector as a form of storage with TiDB serverless, enabling organizations to store and process both traditional data and vector data within a unified platform. We believe the integration of vector search capabilities into a traditional database will be a game-changer for businesses looking to leverage the power of AI and ML. 

Can you share some successful case studies of TiDB implementations in India and globally?

Absolutely! We have a number of success stories across various industries. In India, a leading e-commerce giant was struggling to manage the increasing complexity of their database infrastructure as they scaled, relying on a multitude of single-node databases. By migrating to TiDB, they consolidated their infrastructure, significantly improved performance, and gained greater operational efficiency. 

Delhivery, a leading logistics company that delivers a million packages a day, migrated to TiDB to power their real-time analytics and data marts use case, enabling them to handle thousands of requests per second with less than 100ms latency. 

Pinterest is another flagship customer that leveraged TiDB to modernize their data architecture. Globally, we’ve seen tremendous success in the fintech sector. A major Japanese fintech company, for example, experienced a 10x increase in throughput and a 30% reduction in latency after adopting TiDB for their high-volume wallet application. 

We’re also seeing significant growth in the SaaS market, where TiDB helps companies manage the complexities of multi-tenant environments and scale their databases to support growing user bases.

What are the key market opportunities for TiDB in India?

India is a dynamic and rapidly growing market with a thriving startup ecosystem. We see immense potential here. The sheer volume of data generated by India’s digital-native businesses and startups, coupled with their ambitious growth plans, creates a strong demand for distributed database solutions. India’s thriving Web3 community, its vast base of skilled developers are all opportunities that TiDB will be actively tapping into. 

The post Why Distributed SQL is the answer to modern data challenges: Bhanu Jamwal, TiDB  appeared first on CIO Dimension.

The Generative AI (GenAI) wave is upon us. Every enterprise is buzzing about its potential, and investments are skyrocketing. But amidst the excitement, a crucial question remains: What truly separates successful GenAI projects from the rest?

In a recent visit to India, Jihad Dannawi, Regional VP – APAC, DataStax (a leading real-time vector database company) sheds light on the hidden complexities of AI projects. This exclusive interview with CIO Dimension delves into:

• The power of the right database in unlocking the true potential of Large Language Models (LLMs)
• Why developers are the backbone of GenAI and how to empower them
• India’s unique advantage in leading the GenAI revolution

Edited excerpts: 

DataStax positions Astra DB as the ‘go-to’ database for GenAI applications. Can you explain why Astra DB stands out as the best choice for GenAI applications compared to your competition?

For starters, we are a 14-year-old company that has built the most widely used enterprise-grade database. It has been delivering high scale, high performance, and high availability — even before Generative AI came in. 

We are at a stage where a lot of organizations are scaling GenAI from PoC to production. At PoC level, organizations are not looking at scale or performance. But when they move to production, they suddenly start to have performance issues. They need to be able to pull in massive amounts of data to increase the relevancy and accuracy of the models, which means that the database must be bigger.

But volume often comes in the way of performance at the production stage. When organizations move beyond entry level scenarios to more mature use cases, they need to have a database that can handle their speed, scale, and performance requirements      – all at a reasonable TCO. A lot of databases in the market struggle to balance these aspects. 

An industry benchmark clearly demonstrates that DataStax’s Astra DB Serverless (the cloud-native database service built on Apache Cassandra) significantly outperforms the competition in throughput, latency, relevance, and TCO.  

Another key offering from DataStax is RAGStack, which claims to reduce the complexity associated with Gen AI for developers? Could you elaborate? 

When it comes to generative AI, one of the key issues that the developer community faces – especially when they start building the big stuff for production – is that they don’t know where to start. Because there are so many components and tools available in the market, it’s hard to decide what to use –  especially when dealing with vector databases.   

That’s why DataStax built a layer for the developers called RAGStack. This helps them build production-grade retrieval augmented generation (RAG) Gen AI applications in a simpler and faster way – without the developers having to learn everything from      scratch. It’s fully open source, connected and improves developer productivity significantly. It drastically simplifies the process of implementing RAG – the process of providing context from outside data sources to deliver more accurate LLM query responses. 

RAGStack is part of our vision is to enable and empower developers to build GenAI applications in production more efficiently. We are helping developers and companies to quickly innovate.  Developers do not come with AI skills; they don’t know how to build LLMs. We firmly believe that the GenAI journey of an organization starts from the developer, and not the AI team. With RAGstack, they can start right away, without waiting to acquire new skills. 

Another major concern that organizations must deal with is the AI hallucination. How is DataStax working to ensure fewer hallucinations? 

Hallucination, or in other words the “context” for an LLM, is the tricky bit. Now technology allows you to launch GenAI very quickly, but GenAI is not always accurate. This is why retrieval augmented generation (RAG) is so important for enterprises building GenAI applications. They must be able to augment an LLM with relevant, up-to-date data so it can provide relevant, factual responses.

More data means better context. And this will also bring in the big competitive differentiator into the models and the eventual outcomes for businesses. This further emphasizes the point that many databases in the market that claim to be vector do not have the capacity to perform high volume data. 

Can you share some examples of how Astra DB has helped customers achieve significant business outcomes?

Through Astra, we have started to discover a lot of customers with smaller needs, who can move very quickly through the GenAI journey. When customers begin to deploy impactful GenAI in their business processes, we believe that we stand to gain.  

A great case in point is Skypoint, the Portland-based Gen AI healthcare company. They leverage Astra DB’s vector search to enhance their services, offering users real-time, contextual, and personalized AI agent solutions. 

Another key customer for us is India’s leading edtech player, Physics Wallah. This unicorn’s app has over 15 million downloads as of January 2024. Its personalized AI bot is built on Astra DB and assists students with their academic and support queries in a personalized manner. The database’s stability helped the company handle 50x traffic increase with zero downtime during a live demo with 1 million students.  

A lot of organizations are still in the experimentation stage. Our study shows that only 4% of GenAI projects have moved to production, so we are in the absolute beginning of the wave, a lot of the     projects in PoC will move in     to production this year.      

India’s GenAI market is predicted to reach a massive $17B by 2030 and India is reportedly ahead of the US in AI adoption by businesses. What will be the catalysts for this growth and what are DataStax’s plans to capitalize on this opportunity?

AI will impact the top line and bottom line of every business out there. India has a strong community of developers and GenAI starts with developers. So, India has the skills and the mass to innovate and drive more use cases. They      also have the right mindset – the ecosystem here loves to build and that’s a very positive mindset to have when it comes to AI. Because AI is not something you buy and turn on, it’s something you build. And with the right mindset, India will be faster to innovate. The GenAI wave is poised to trigger a paradigm shift, comparable to the impact of mobile and web technologies. India has the skills, capacity and the mindset to really benefit.  

From a DataStax point of view, India is a big market and we are definitely investing here. We are expanding our team to meet to the growing needs.  

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