Three Ways Generative AI Is Overhauling Data Management
AI empowers a broad set of users to discover new and exciting insights. Here are three ways AI is changing how enterprises manage their data.
- By Nima Negahban
- December 6, 2023
Generative AI is reshaping the landscape of data management, heralding a disruptive era marked by data democratization, heightened pattern discovery, and a fundamental reimagining of data platform construction and maintenance. By democratizing data access and analysis, generative AI empowers a broader audience to uncover novel insights. It revolutionizes our ability to identify new patterns and trends within vast data sets and is prompting a radical reevaluation of data engineering.
Here are three ways AI is having an impact on data management.
Trend #1: Natural language is the new structured query language
In 2024, natural language to SQL (NL2SQL) technology is set to profoundly revolutionize the way we harness and interact with data. NL2SQL represents a significant advancement in the field of artificial intelligence; it enables individuals with limited or no knowledge of traditional SQL programming to query databases and extract insights using plain language.
The democratization of data through NL2SQL holds immense promise for a wide range of industries and professions. With this technology, business analysts, marketers, field personnel, healthcare professionals -- virtually anyone -- can independently access and analyze data without relying on data scientists or SQL experts. NL2SQL will promote inclusivity by breaking down the language barrier between data and users. Non-technical staff can articulate their data needs directly, eliminating the need for translation by IT specialists. This empowers decision-makers to make more informed choices and enhances the agility of organizations.
When embarking on an NL2SQL initiative, keep several crucial tips and considerations in mind to ensure success. First, prioritize accuracy in SQL generation. NL2SQL has come a long way in understanding natural language queries, but some large language models (LLMs) are better than others in dealing with nuanced or complex questions.
Second, ensuring efficient query execution on ad hoc questions is paramount. Historically, interactive querying in a data warehouse environment meant gathering requirements in advance and engineering the data through caching, denormalizing, and other techniques. Generative AI has changed expectations -- users now want immediate answers to novel questions. New compute paradigms have emerged and will enter the mainstream that remove the need to pre-engineer the data in advance to support conversation-style data interactions (see Trend #3).
Finally, security is of utmost importance. NL2SQL interfaces may inadvertently expose sensitive data if not properly controlled. Robust access controls, encryption, and user authentication are essential to safeguard against unauthorized queries and data breaches. Many companies are already securely architecting NL2SQL capabilities by using a native LLM inside the database perimeter versus using an API call to a public LLM.
Trend #2: Vector search for structured enterprise data gets serious
Vector embeddings are poised to become a game-changer in the field of data warehousing. In 2024, their popularity among data warehouse practitioners is set to soar.
The shift towards vector embeddings is driven by the realization of the remarkable benefits they bring to storing and searching both structured and unstructured data as vectors. The core advantage of vector embeddings lies in their ability to represent complex data in an efficient format. By converting data into high-dimensional vectors, it becomes possible to capture the semantic relationships, context, and similarities between different data points. This enables more sophisticated search and retrieval mechanisms because it allows for similarity-based searches and advanced analytics, which are challenging with traditional data storage and querying methods.
Structured data can be represented as vectors to facilitate complex querying and pattern recognition. This enables data warehouse practitioners to find hidden correlations and insights within their structured data more effectively. Moreover, the same vector-based approach can be extended to unstructured data, such as text and images, making it possible to create a unified data warehousing system capable of handling diverse data types seamlessly.
Evaluating vector databases across multiple critical dimensions is imperative for making informed decisions in data management. Data latency, measuring the delay in data ingestion and updating, is crucial for ensuring real-time or near-real-time data freshness, which is particularly vital in applications demanding up-to-the-minute insights. Query latency assesses the speed and responsiveness of the database in retrieving vectors. Enterprise capabilities, such as robust security measures, are non-negotiable because they safeguard sensitive data and ensure compliance with regulatory requirements. Additionally, support for SQL is essential for compatibility and ease of integration with existing systems, including the aforementioned NL2SQL capability.
Trend #3: GPUs expand their role into data management
Graphical processing units (GPUs) have gained industry-wide recognition as the pivotal technology that has fueled the AI revolution, accelerating complex neural network computations and enabling breakthroughs in machine learning and deep learning applications. The impact doesn’t stop there. We are witnessing a significant shift in the world of data management with the rise of GPU database architectures. These systems are gaining momentum for several compelling reasons that are reshaping the way organizations handle and interact with their data.
One of the primary drivers behind the adoption of GPU database architectures is their remarkable speed and efficiency. GPUs are purpose-built for parallel processing and handling large data sets, making them exceptionally efficient in handling data-intensive tasks. Recent hardware breakthroughs by NVIDIA, including faster PCI buses and more VRAM, have addressed a key bottleneck that has improved overall system performance and responsiveness.
Furthermore, GPU databases support interactive querying without the need to build extensive data pipelines. Traditional data processing often requires complex extract, transform, and load (ETL) processes that can be time-consuming and resource-intensive. In contrast, GPU databases allow users to query and analyze data using matrix calculations, reducing the need for these lengthy data preparation steps designed to overcome the performance limitations of traditional parallel databases. This means that organizations can gain insights from their data more rapidly, facilitating quicker and better-informed decision-making.
The prevalence of GPU database architectures in the cloud is another key factor driving their adoption. Leading cloud service providers are integrating GPU capabilities into their infrastructure, making it easier for organizations to harness the power of GPUs without the need for large capital investments in on-premises hardware. This democratizes access to GPU-accelerated databases, enabling businesses of all sizes to leverage their benefits and stay competitive in the data-driven landscape.
When evaluating a GPU database, several crucial considerations come to the forefront:
- The ability to scale efficiently with a distributed architecture to handle vast and growing data sets
- Enterprise capabilities, including robust security measures, tiered storage, high availability, and connectors to popular tools
- Compliance with industry standards such as PostgreSQL ensures compatibility with existing systems
- Strong partnerships with industry leaders such as NVIDIA can provide access to cutting-edge GPU technology and engineering resources, further enhancing the performance and capabilities of the database
A Final Word
Generative AI is having a transformative impact on the world of data management, spearheading data democratization, enhancing our capacity to unearth fresh patterns and insights within extreme data sets, and prompting a profound rethinking of the conventional approach to building and managing data platforms.