Dominic Jainy is a veteran IT professional whose career has been defined by navigating the complex intersections of artificial intelligence, machine learning, and blockchain. As enterprises grapple with the massive data exhaust produced by modern AI agents, Jainy’s insights into infrastructure optimization have become essential for organizations trying to balance innovation with fiscal responsibility. Our conversation dives into the shifting landscape of log analytics, exploring how the surge in telemetry is forcing a total rethink of storage architectures. We discuss the technical nuances of AWS’s latest OpenSearch engine, the hard realities of migrating legacy systems, and how the industry is moving away from the dangerous practice of discarding operational data to save on costs.
Enterprises often exclude a significant majority of log data to manage infrastructure costs. How does this “going blind on purpose” impact long-term operational security, and how is AWS attempting to pivot this trend with its new OpenSearch engine?
When an organization is forced to exclude an average of 86% of its log data just to keep the lights on, they are essentially flying blind through a storm. This “intentional blindness” is a desperate reaction to infrastructure costs that have spiraled out of control, leaving security teams without the telemetry needed for effective incident response or compliance audits. You cannot investigate an unanticipated security breach if the evidence was never recorded because it didn’t fit the budget. AWS is stepping in with a new engine for its managed OpenSearch Service that claims to slash storage costs by a staggering 70%. By leveraging Apache Parquet for storage and maintaining Lucene search indexes, they are providing a way for companies to keep those critical details without the financial penalty that used to come with high-volume retention.
AI and agentic applications are notoriously “talkative.” Could you elaborate on why traditional observability architectures are failing under the weight of these modern workloads and what specific technical shifts in the new engine address this?
The reality is that AI workloads have driven a 93% increase in log volume over just the last year, which has effectively broken the traditional economic model of general-purpose observability. These agentic applications are incredibly talkative, performing constant background queries that general-purpose engines simply weren’t designed to handle at scale. The bill gets too big, and the performance degrades until the system is no longer viable for real-time monitoring. To fix this, AWS is using Apache Calcite to parse and optimize queries, routing them through Apache DataFusion for analytical operations and Lucene for search predicates. This technical pivot allows search and analytical aggregations to run within the same query, providing a level of price-performance that keeps the infrastructure from buckling under the weight of AI-driven data floods.
While the promise of seventy percent lower storage costs is enticing, analysts point toward significant migration friction. What are the practical hurdles an engineering team faces when moving to this optimized engine, especially regarding existing query languages?
Despite the excitement around cost savings, we have to recognize that this isn’t a simple “lift-and-shift” operation for most engineering teams. One major hurdle is that this optimized engine cannot be added to an existing domain or enabled on individual indices, meaning teams must stand up entirely new domains and migrate their ingestion pipelines manually. Furthermore, the lack of support for Domain Specific Language (DSL) is a significant pain point for enterprises that have spent years building their dashboards and alerts around it. You are looking at a scenario where developers may need to rewrite their entire automation workflow in SQL or Piped Processing Language (PPL). This creates a heavy lift that might keep organizations tethered to infrastructure they’ve already outgrown simply because the transition is too labor-intensive.
Beyond the immediate financial savings, how does consolidating log data into a single, more efficient engine affect the broader IT ecosystem, particularly concerning tool sprawl and team efficiency?
Consolidating your telemetry into a single, efficient engine is about more than just the monthly cloud bill; it’s about eliminating the “tool sprawl tax” that plagues modern IT departments. When you fragment your observability across five different vendors to chase cost arbitrage, you end up paying a heavy price in integration overhead and the headcount required to maintain five different dashboards instead of one. By centralizing this data, CIOs can reduce the incentive for this fragmentation and allow their teams to focus on a single source of truth. This leads to faster incident investigations and better compliance support, as the data is no longer scattered across disconnected silos. It essentially simplifies the mental model for the engineering team, allowing them to spend less time managing tools and more time analyzing the actual health of their applications.
What is your forecast for the future of log management as AI workloads continue to scale?
I expect we will see a permanent shift away from the era of “sampling” and “log dropping” as specialized engines like this become the industry standard. As AI agents become more autonomous and complex, the telemetry they produce will become the primary way we debug and govern these systems, making 100% retention a non-negotiable requirement for enterprise safety. We will likely see more architectural convergence where search and heavy-duty analytics happen in the same environment, finally aligning infrastructure costs with the reality of data-heavy AI applications. Ultimately, the successful enterprises will be the ones that stop treating logs as a liability to be deleted and start treating them as a high-fidelity asset for operational intelligence. The era of choosing between visibility and the bottom line is finally coming to an end.