NewSQL: The Bridge Between SQL and NoSQL Databases for AI/ML Workloads
The rapid rise of artificial intelligence (AI) and machine learning (ML) in recent years is revolutionizing almost every industry and creating huge opportunities for businesses to gain new insights, automate processes, and build intelligent applications. However, the success of AI/ML initiatives depends heavily on the underlying data infrastructure. The volume, variety, and velocity of data required for training and serving AI/ML models is pushing traditional database systems to their limits.
On one hand, AI/ML applications typically require the scalability and flexibility of NoSQL databases to handle the massive amounts of unstructured and semi-structured data used for model training. But on the other hand, they also need the strong consistency and complex query capabilities of SQL databases for transactional workloads and ad-hoc analysis. This has created a pressing need for a new class of databases that can bridge the gap between SQL and NoSQL and support both transactional and analytical workloads in real-time – enter NewSQL.
The Rise of NewSQL for AI/ML
NewSQL databases have emerged as a key enabling technology for AI/ML applications by providing a scalable, distributed SQL database architecture that can handle both online transaction processing (OLTP) and online analytical processing (OLAP) workloads in a single system. This convergence of transactional and analytical capabilities is known as hybrid transactional/analytical processing (HTAP).
According to Gartner, "By 2022, 30% of enterprise OLTP database deployments will use HTAP enabled by in-memory computing and NewSQL databases." The ability to perform real-time analytics and AI/ML inference on live transactional data is becoming a critical capability for many organizations looking to build intelligent, data-driven applications.
Some examples of AI/ML use cases that can benefit from NewSQL HTAP capabilities include:
- Real-time fraud detection and prevention
- Predictive maintenance and anomaly detection for IoT data streams
- Personalized recommendations and ad targeting
- Dynamic pricing and revenue optimization
- Operational analytics and business intelligence
By using a NewSQL database as the single source of truth for both transactional and analytical workloads, organizations can simplify their data architecture, reduce data movement and latency, and enable real-time decision-making powered by AI/ML.
Benchmarking NewSQL Databases for AI/ML
So which NewSQL databases are leading the pack for AI/ML workloads? Here‘s a comparison of some of the top contenders:
| Database | HTAP | ACID | Horizontal Scalability | AI/ML Integrations |
|---|---|---|---|---|
| Google Cloud Spanner | Yes | Yes | Yes | TensorFlow, Spark ML |
| CockroachDB | Yes | Yes | Yes | Spark ML |
| Volt Active Data | Yes | Yes | Yes | Spark ML |
| TiDB | Yes | Yes | Yes | TensorFlow, Spark ML |
| MemSQL | Yes | Yes | Yes | Spark ML |
Google Cloud Spanner is widely considered the pioneer of the NewSQL space and offers exceptional scalability, availability, and consistency for both OLTP and OLAP workloads. It has been battle-tested at Google scale and is a popular choice for AI/ML applications running on Google Cloud.
CockroachDB is an open-source Spanner-inspired database that provides a distributed SQL API with full ACID guarantees. It has a native integration with Spark for distributed ML processing and is a good fit for cloud-native, Kubernetes-based AI/ML deployments.
Volt Active Data (formerly VoltDB) is an in-memory NewSQL database known for its extremely fast ACID transactions and strong consistency. It has recently added native support for ML model scoring in real-time OLTP workflows.
TiDB is another open-source NewSQL database that has gained traction for its horizontal scalability and MySQL compatibility. It offers integrations with TensorFlow and Spark for distributed ML training and inference.
MemSQL (now SingleStore) has long been a leader in the in-memory NewSQL space and offers a distributed SQL engine with columnar storage for fast analytics. It has a native Spark connector for high-performance ML processing.
In terms of raw performance, these NewSQL databases can offer significant speedups over traditional databases for combined OLTP/OLAP AI/ML workloads. For example, Google has published benchmarks showing that Spanner can process over 4 million queries per second (QPS) with 50ms latency for a 50/50 OLTP/OLAP workload. CockroachDB has demonstrated the ability to ingest and analytically process over 1 billion rows of time-series data per second.
The Role of AI/ML in NewSQL
In addition to supporting AI/ML applications, many NewSQL databases are leveraging AI/ML techniques internally to optimize performance, resource utilization, and operational efficiency. For example:
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Google Cloud Spanner uses ML-based workload forecasting and runtime scheduling to automatically provision and scale resources based on predicted demand.
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CockroachDB employs reinforcement learning to continuously optimize its distributed query execution plans based on real-time cluster conditions.
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Volt Active Data uses unsupervised learning to automatically detect and adapt to changing workload patterns.
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MemSQL has an AI-powered query optimizer that uses deep learning to generate more efficient query plans.
By using AI/ML to automate and optimize database management, NewSQL vendors are making it easier for enterprises to deploy and scale mission-critical applications with less manual effort and lower costs.
NewSQL Skills and Best Practices for AI/ML Pros
For AI/ML professionals looking to adopt NewSQL databases, there are some key skills and best practices to keep in mind:
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SQL proficiency – Although NewSQL databases offer more scalability and flexibility than traditional RDBMSs, they still rely heavily on SQL for data definition, querying, and manipulation. Strong SQL skills are a must.
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Distributed systems expertise – NewSQL databases are inherently distributed systems, so experience with concepts like sharding, replication, consensus algorithms, and network partitions is valuable.
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Cloud and DevOps skills – Most NewSQL databases are designed to be deployed and managed in cloud environments using infrastructure-as-code and CI/CD pipelines. Familiarity with cloud platforms, containers, and orchestration tools like Kubernetes is beneficial.
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Data modeling for HTAP – Designing schemas that can efficiently support both OLTP and OLAP workloads requires a different approach than traditional normalized data modeling. Techniques like denormalization, materialized views, and column-oriented storage can help optimize for mixed workloads.
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Integration with AI/ML tools – To get the most value from NewSQL for AI/ML, you‘ll need to integrate the database with the rest of your AI/ML toolchain. This includes ETL/ELT processes for data ingestion, feature stores for feature engineering, and model training and serving frameworks.
As the volume and complexity of data continues to grow, the ability to process and analyze data in real-time is becoming a key differentiator for AI/ML applications. By combining the scalability of NoSQL with the consistency and querying power of SQL, NewSQL databases are well-positioned to serve as the data backbone for the next generation of intelligent applications.
If you‘re an AI/ML professional looking to build cutting-edge applications that can handle both transactional and analytical workloads at scale, now is the time to start exploring NewSQL databases. With the right skills and best practices, you can unlock new insights, drive real-time decision-making, and ultimately deliver more value to your organization.
