Imagine AI tools that understand your data even more deeply than you do, and act on it even faster. That’s the vision guiding what we’re building at Hydrolix. Previously, I explored Hydrolix as the data platform for AIOps. This post will focus on key roadmap themes that integrate AI directly into the Hydrolix platform. Our AI vision aims to turn intelligence into impact in order to tackle real customer challenges. By solving significant problems within the data community and accelerating time to value, Hydrolix is positioned to become a leader when it comes to using AI to get answers from your data.
The roadmap shows three key themes: data ingestion, data exploration and visualization, and automated intelligence. Let’s dive into what each of those really mean.
Data Ingestion
Data ingestion is a critical aspect of collecting and understanding data; without easy and versatile data ingestion, a data platform loses its value. One significant hurdle in this process is managing diverse schemas, which often requires extensive manual effort from highly technical experts. This manual schema creation is not only time-consuming, but also delays the realization of value for end users.
AI can significantly enhance and accelerate the collection of data. Hydrolix already offers flexible schema, enabling the storage of data from various sources within a single table. The integration of AI, particularly large language models (LLMs), will further simplify the transformation of disparate schemas into a unified destination. This will lead to a more automated and accessible process, facilitating self-service ingestion of new data sources with automated schema detection.
Data Exploration and Visualization
Another major theme emerging from the AI boom, and one I believe is here to stay, is the democratization of data. A significant, yet often underestimated, challenge with data is the difficulty in accessing and interpreting it, especially at high volumes. As a result, enterprises often face significant challenges utilizing their data effectively. These hurdles include:
- Lack of SQL proficiency: Many business users lack the necessary knowledge of SQL and other data querying languages, which restricts their ability to use self-service analytics tools and execute complex queries.
- Disparate data systems: Data fragmentation across disconnected systems creates silos, impeding collaboration and data visibility. This often leads to time-consuming and error-prone manual data extraction processes.
- Poor data quality: Issues such as poor data quality, the absence of centralized data governance, and inconsistent standards make it difficult for organizations to trust and extract value from their enterprise data.
- Outdated platforms and legacy systems: Reliance on outdated technologies often means organizations are limited to batch data processing instead of real-time ingest and analytics. The lack of modern connectors results in slow insights and missed opportunities to act quickly on incoming data.
Hydrolix is designed for unifying and preserving high-fidelity, high-cardinality data in its original state. With the integration of AI, our goal is to make data not just accessible, but also easily understandable. The adoption of AI and natural language processing for data interaction has proven to be a transformative advancement. This initiative began earlier this year with the release of our MCP server, which was an experimental step to gauge interest from customers and prospects to help them find new ways of interacting with their Hydrolix data. The enthusiastic response has led us to prioritize expanding these capabilities through LLMs trained on Hydrolix context.
The ability to freely explore data, unconstrained by predefined dashboards or reports, will encourage more users to engage with data. This, in turn, will unlock additional insights beyond planned dashboards and foster data-driven decision-making.
Automated Intelligence
It’s not enough to simply make data available; data users often struggle to make decisions and determine what to do with large datasets. The ability of AI and large language models (LLMs) to offer actionable insights on data can be hugely impactful, quickly helping people understand, process information, and take action. Transforming raw logs into context-rich, predictive, and actionable insights that drive smarter decisions is a game-changer in the data community.
The challenge with identifying insights from data is that you typically need a prior understanding of what you’re looking for, and it can be difficult for humans, even experts, to sift through and interpret huge volumes of data. Automated intelligence allows AI to handle the heavy lifting of identifying what stands out. In this area, Hydrolix will focus on empowering customers’ data to uncover its own learnings. This includes initiatives such as automated AI alerts that can learn and adapt based on data changes, and anomaly and piracy detection agents that can broadly analyze data to find insights.
Next Steps
By becoming AI-native, Hydrolix can help you extract maximum value from your data and solve your business problems. We recognize the immense potential of AI to reduce existing barriers to deriving value from data. If you want to learn more about our latest and upcoming releases that are making it easier to solve the challenges you’re facing with data, contact Hydrolix about a proof of concept or demo.
Learn about using Hydrolix for AIOps.
