On Graph Databases. Q&A with Sudhir Hasbe
Q1. You recently joined Neo4j’s executive leadership team as Chief Product Officer (CPO). What are your responsibilities and expectations?
I joined Neo4j in April 2023, and I am responsible for the product vision, strategy, and roadmap for our native graph database and analytics offerings. I am passionate about graph technology and its potential to revolutionize how organizations solve complex problems and unlock potential by uncovering relationships and patterns in vast data sets. As we continue to grow, my goal is to create a product strategy to meet the evolving needs of organizations worldwide.
Q2. In your opinion how has the graph database market developed in the last years?
The graph database market has experienced significant growth in recent years as early adopters and tech innovators across various industries began to see the many benefits the technology can bring. The growth is driven by expanding workloads from enterprise mission critical applications to analytical and data science use cases.
We also believe that the recent rapid growth of AI and machine learning is a huge opportunity because graph technology helps make generative AI tools less biased, more accurate, and better behaved. According to Gartner, “By 2023, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision-making across the enterprise.” Source: Gartner, Market Guide for Graph Database Management Systems 2022, Merv Adrian, Afraz Jaffri et al., 30 Aug 2022.
The market is expected to continue its growth trajectory in the coming years as more organizations recognize the value of graph databases.
Q3. What is the relationship between graph databases and data science and AI?
Graph databases have a natural connection to data science and AI applications because they are good at representing complex relationships between data points. Data scientists and AI practitioners often use graph databases, a powerful tool to model and analyze complex data sets – mapping relationships between customers, products, and purchases, for instance, in order to identify patterns and make more accurate predictions about future behavior. On the other hand, data science and AI applications provide unique use cases for graph databases, such as fraud detection and recommendations.
Q4. What does it mean, a “native” graph database? Does it mean there are “non-native” graph databases?
A “native” graph database is a database management system specifically designed to store and manage graph data structures, such as nodes, edges, and properties. They are optimized for
processing graph data and typically provide more efficient storage and querying mechanisms for graph data than general-purpose databases.
A “non-native” graph database is a database that can store and manage graph data but isn’t specifically designed for this purpose hence it does not have the same level of performance and functionality as native graph databases. An example is a relational database, which can represent relationships between tables, but is not optimized for querying and analyzing graph traversal.
Q5. You have been quoted saying “The relationships between both data and metadata matter more than ever”. Can you please explain why?
Data is content, and metadata is context. While in the past, data was often analyzed independently of its metadata, it’s becoming increasingly important in modern data management and analysis that we understand both equally, especially as data volumes and complexity continue to grow rapidly and the need to gain meaningful insights is now paramount.
Q6. How do you position Neo4j Aura with respect to the other vendors listed in the Gartner® Magic QuadrantTM for Cloud Database Management Systems?
Neo4j Aura is our managed graph database and analytics offering in the cloud. It is now available on all 3 primary cloud offerings AWS, Google Cloud, and Microsoft Azure. More than 90% of our new deployments of Neo4j databases are on cloud platforms, and Aura gives customers an option to use our fully managed database so they can focus on applications and analytics rather than managing the database infrastructure.
Being recognized by Gartner in their Cloud Database Managed Services MQ places us within the broader landscape of all cloud database management systems and vendors. We believe this is a validation of the category of graph databases. This Magic Quadrant is written for IT buyers and is a highly visible and influential report for enterprise buyers seeking confirmation of their technology purchases. Gartner limits the number of vendors, and this report provides insight into the key players in the market.
Q7. How does your track record on innovation with cloud hyper scalers supposed to help Neo4j?
As someone with experience building massive-scale cloud data services like BigQuery at Google and having worked on Microsoft Azure, I recognize the potential benefits of integrating the Neo4j graph and analytics platform more closely with these cloud services. By taking advantage of the existing partnerships between Neo4j and these hyper scalers, we can focus on technical integrations that enable seamless data movement, improved data analysis, and the creation of new, innovative services on top of these platforms. Ultimately, this could result in mutual benefits for both Neo4j and our customers.
Sudhir Hasbe is Neo4j’s Chief Product Officer. He previously led product management for Google Cloud’s Data Analytics Platform, which includes industry-leading products like BigQuery, Looker, Dataproc, Dataflow, Pub/Sub, Composer, Data Fusion, and Dataplex. Under Sudhir’s leadership, BigQuery grew to be one of the largest analytics platforms with tens of thousands of customers, 110TB of data being processed every second, hundreds of customers with petabyte-scale datasets, and powering more than 700 ISV offerings. Hasbe also led acquisitions of Looker, Dataform, Cask, and CompilerWorks to enhance Google Cloud’s Data Analytics offering.
Sudhir was also an executive sponsor of several of Google Cloud’s marquee enterprise customers and ecosystem partners. Prior to Google, Hasbe led software engineering at Zulily, transforming it into a state-of-the-art data-driven organization. Prior to Zulilly, Sudhir spent seven years at Microsoft, where he led product management for Xbox entertainment services, Azure Data Marketplace, SQL Azure, and BizTalk Server. He is based in Seattle, WA.