While operational and analytical data gets rigorous treatment in terms of the pillars of good architecture such as purview, privacy, security, governance, encryption at rest and in transit, aging, tiering and such others, DevOps tasks comprising Extract-Transform-Load, backup/restore and such others, is often brushed aside but never eliminated for the convenience they provide. This is inclusive of the vast vector stores that have now become central to building contextual copilots in many scenarios.

One of the tools to empower access of data for purposes other than transactional or analytics is the ability to connect to it with a client native to the store where the data resides. Even if the store is in the cloud, data plane access is usually independent of the control plane command-line interfaces. This calls for a creating a custom image that can be used on any compute to spin up a container with ability to access the vectors. For example, this Dockerfile installs clients:

FROM python:3.13-latest-dev

USER root

RUN apt-get update && \

    apt-get install -y ksh \

    ldap-utils \

    mysql-client \

    vim \

    wget \

    curl \

    libdbd-mysql-perl \

    libcurl4-openssl-dev \

    rsync \

    libev4 \

    tzdata \

    jq \

    pigz \

    python3-minimal \

    python3-pip && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    pip3 install s3cmd

RUN apk add --no-cache mariadb mariadb-client

RUN pip install azure-storage-blob requests

RUN pip install requests

WORKDIR /app

COPY custom_installs.py .

RUN mysqldump --version

RUN mysql --version

ENTRYPOINT ["python", "custom_installs.py"]

 

This is a summary of the book titled “How the Future Works: Leading Flexible Teams To Do The Best Work of Their Lives” written by Brian Elliott, Sheela Subramanian and Helen Kupp and published by Wiley, 2022. In this book, the authors examine one of the most profound transformations in modern business: the rapid and irreversible shift toward flexible work. Written in the aftermath of the COVID-19 pandemic, the book argues that what began as an emergency response has evolved into a durable and preferable way of working—one that challenges long-held assumptions about productivity, leadership, and the role of the traditional office.

Before the pandemic, flexible work arrangements were rare and often reserved for elite performers. Most organizations relied on physical offices, fixed schedules, and direct supervision as the foundation of productivity. Many leaders believed that innovation depended on employees sharing the same space, learning through proximity, and being visibly present. The idea of managing a distributed workforce seemed risky, if not impossible. Yet when offices abruptly closed in 2019, companies had no choice but to test those assumptions at scale.

What followed surprised many executives. Productivity did not collapse; in many cases, it increased. Employees reported greater autonomy, improved focus, and stronger work–life balance. Creativity and innovation continued, and in some organizations even flourished. As the authors note, flexibility turned into a powerful advantage in recruiting and retaining talent, particularly in a highly competitive labor market. The authors conclude that a full return to rigid, office-centered work is both unlikely and undesirable.

Central to the book’s argument is the idea that traditional measures of productivity were flawed long before remote work became common. Managers once relied on visible activity—attendance, desk time, and “management by walking around”—as proxies for performance. These methods fail in distributed environments and, more importantly, never truly measured the quality or impact of work in the first place. Seeing employees at their desks does not reveal whether they are engaged, effective, or producing meaningful outcomes.

To help organizations adapt, the authors outline seven interrelated steps for retrofitting companies for the future of work. The first is to operate according to a clear and shared set of principles. Because flexibility introduces complexity and uncertainty, principles act as a compass for decision-making. Rather than imposing uniform rules, leaders should prioritize team-level autonomy, recognize that different functions require different approaches, and adopt a digital-first mindset that treats remote participation as the default rather than the exception.

Principles alone, however, are not enough. Organizations must also establish behavioral guidelines that translate values into everyday practices. These “guardrails” ensure fairness and prevent the emergence of “faux flexibility,” where policies appear progressive but still constrain employee autonomy. Examples such as Slack’s “one dials in, all dial in” rule demonstrate how simple norms can reinforce inclusion and equity across hybrid teams.

A defining theme of the book is collaboration rather than control. The authors caution against top-down mandates and instead encourage leaders to co-create flexible work policies with employees. Teams that are already working effectively should be studied and learned from, and flexibility should be formalized through team-level agreements that clarify expectations around schedules, communication, accountability, and relationships. This participatory approach builds trust and ensures that flexibility works for both individuals and the organization.

Because no universal blueprint exists, experimentation is essential. Leaders must accept uncertainty, support pilot programs, and view trial and error not as failure but as learning. Over time, patterns emerge that reveal what truly supports performance and well-being. The authors emphasize that there is no perfect data point or benchmark—only continuous improvement guided by experience and feedback.

The book also challenges the belief that culture depends on physical proximity. While companies once invested heavily in office campuses, the authors argue that connection and belonging can be cultivated virtually—and sometimes more inclusively than before. Research cited in the book links flexibility to stronger feelings of belonging, higher job satisfaction, and improved well-being, undermining the assumption that creativity depends on shared physical space.

Leadership, however, must evolve. The shift to flexible work has exposed weaknesses in managers who rely on control rather than trust. The authors advocate developing managers as coaches—leaders who communicate clearly, show empathy, and focus on outcomes instead of activity. Training initiatives like Slack’s “Base Camp” illustrate how organizations can intentionally build these capabilities.

The authors contrast two management paths: the “doom loop” of constant surveillance and the “boom loop” of trust and accountability. Excessive monitoring erodes morale, increases anxiety, and drives attrition, while goal-based management fosters engagement and performance. Tools such as the RACI matrix help organizations track progress without resorting to intrusive oversight, reinforcing the principle that results—not hours—matter most.

Flexibility is not a temporary accommodation but a defining feature of modern work. Employees want and need it, and organizations that embrace it thoughtfully gain a lasting competitive advantage. While flexibility is not a cure-all, the authors argue it is a decisive step toward healthier, more resilient, and more human workplaces when implemented with intention and trust.

#codingexercise:CodingExercise-02-12-2026

 In continuation of previous posts of exemplary video analysis stacks on AWS, we focus on Azure today. The most explicit lineage of “well architected” drone and video analytics on Azure starts with Live Video Analytics on IoT Edge and evolves into more general edge to cloud platforms like Edge Video Services. Live Video Analytics (LVA) was introduced as a hybrid platform that captures, records, and analyzes live video at the edge, then publishes both video and analytics to Azure services. It is deliberately pluggable: we wire in our own models—Cognitive Services containers, custom models trained in Azure Machine Learning, or open source ML—without having to build the media pipeline ourself. Operational excellence is baked into that design: the media graph abstraction gives us declarative topologies and instances, so we can version, deploy, and monitor pipelines as code, while IoT Hub and the Azure IoT SDKs provide a consistent control plane for configuration, health, and updates across fleets of edge devices. (LVA)

Reliability and performance efficiency in LVA come from pushing the latency sensitive work—frame capture, initial inference, event generation—onto IoT Edge devices, while using cloud services like Event Hubs, Time Series Insights, and other analytics backends for aggregation and visualization. The edge module runs on Linux x86 64 hardware and can be combined with Stream Analytics on IoT Edge to react to analytics events in real time, for example raising alerts when certain objects are detected above a probability threshold. That split honors the reliability pillar by isolating local decision making from cloud connectivity, and it improves performance efficiency by avoiding round trips to the cloud for every frame. At the same time, Azure Monitor and Application Insights provide the observability layer—metrics, logs, and traces across IoT Hub, edge modules, and downstream services—so operators can detect regressions, tune graph topologies, and automate remediation in line with the operational excellence pillar.

Edge Video Services (EVS) takes those ideas and generalizes them into a reference architecture for high density video analytics across a two or three layer edge hierarchy. In EVS, an IoT Edge device on premises ingests camera feeds and runs an EVS client container that fans frames out to specialized video ML containers such as NVIDIA Triton Inference Server, Microsoft Rocket, or Intel OpenVINO Model Server. A network edge tier—typically AKS running in Azure public MEC—provides heavier compute with GPUs and low latency connectivity back to the on prem edge. This cascaded pipeline is a direct expression of the performance efficiency and cost optimization pillars: lightweight filtering and pre processing happen close to the cameras, while more expensive models and multi stream correlation are centralized on shared GPU clusters, avoiding over provisioning at either layer. Reliability is addressed through Kubernetes based orchestration, multi node clusters at the network edge, and the ability to re route workloads across the hierarchy if a node fails. (EVS)

From a sustainability and cost perspective, both LVA and EVS lean heavily on managed services and right sized compute. In LVA style deployments, only the necessary analytics results and selected clips are shipped to the cloud, with raw video often retained locally or in tiered storage, reducing bandwidth and storage overhead. EVS goes further by explicitly partitioning workloads so that GPU intensive inference runs on shared AKS clusters in MEC locations, improving utilization and reducing the number of always on, underused GPU nodes. This aligns with Azure’s sustainability guidance: use managed services where possible, aggressively manage data lifecycles, and concentrate specialized hardware in shared, high utilization pools rather than scattering it across many small sites.

When we compare these drone and video centric stacks to more generic ingestion and analytics patterns on Azure, the performance story is less about raw maximum throughput and more about how that throughput is shaped. Event Hubs and IoT Hub are documented to handle millions of events per second across partitions, and AKS hosted Kafka or custom gRPC ingestion services can be scaled horizontally to similar levels; those patterns are typically used for logs, telemetry, and clickstreams where each event is small and homogeneous. In LVA and EVS, the “events” are derived from high bandwidth video streams, so the architectures focus on early reduction—frame sampling, on edge inference, event extraction—before feeding Event Hubs, Time Series Insights, or downstream databases. In practice, that means we inherit the same proven ingestion envelopes and scaling knobs as other well architected Azure stacks, but wrapped in domain specific primitives: media graphs, edge hierarchies, GPU aware scheduling, and hybrid edge cloud control planes that are tuned for drone and camera workloads rather than generic telemetry.

 In the previous post, the Well-Architected pillars are woven directly into the way the stack ingests, analyzes, and serves video from large fleets of IoT devices. At the operational excellence layer, the architecture leans on API Gateway, Lambda, and Step Functions as the control plane for all asynchronous workflows. These services provide end to end tracing of requests as they move through ingestion, indexing, search, and alerting, so operators can see exactly where latency or failures occur and then automate remediation. The result is an operations model where deployments, rollbacks, and workflow changes are expressed as code, and observability is built into the fabric of the system rather than bolted on later. AWS

Reliability and performance efficiency are largely delivered through serverless and on demand primitives. Lambda functions form the core processing tier, inheriting multi AZ redundancy, automatic scaling, and built in fault tolerance, so the video analytics pipeline can absorb bursty workloads—such as many cameras or drones triggering events at once—without explicit capacity planning. Kinesis Video Streams, Kinesis Data Streams, and DynamoDB are configured in on demand modes, allowing ingest and metadata operations to scale with traffic while avoiding the idle capacity that plagues fixed size clusters. This mirrors the broader AWS streaming reference architectures, where Kinesis Data Streams is positioned to handle “hundreds of gigabytes of data per second from hundreds of thousands of sources,” with features like enhanced fan out providing each consumer up to (2,\text{MB/s}) per shard for low latency fan out at scale. AWS aws.amazon.com

Cost optimization and sustainability in the video analysis guidance are treated as first class design constraints rather than afterthoughts. Data retention is explicitly tiered: 90 days for Kinesis Video Streams, 7 days for Kinesis Data Streams, and 30 days for OpenSearch Service, with hot to warm transitions after 30 minutes. That lifecycle design keeps only the most valuable slices of video and metadata in high cost, low latency storage, while older data is either aged out or moved to cheaper tiers. Combined with Lambda’s pay per use model and the shared, managed infrastructure of Kinesis, OpenSearch Service, and S3, the architecture minimizes always on resources and therefore both spend and energy footprint. This aligns directly with the Well Architected sustainability pillar, which emphasizes managed services, automatic scaling, and aggressive data lifecycle policies to reduce the total resources required for a workload. AWS Protera Technologies

When we compare this video analysis stack to other well architected ingestion and analytics patterns on AWS—such as the generic streaming data analytics reference architectures built around Kinesis Data Streams, Amazon MSK, and Managed Service for Apache Flink—the main difference is not in raw throughput but in workload specialization. The streaming reference designs show that Kinesis Data Streams can scale from a few MB/s per shard up to hundreds of MB/s per stream, while MSK clusters can be sized to ingest on the order of (200,\text{MB/s}) and read (400,\text{MB/s}) with appropriate broker classes and partitioning. pages.awscloud.com AWS Documentation Those architectures are optimized for generic event streams—logs, clickstreams, IoT telemetry—where we often trade richer per event processing for extreme fan in and fan out. The video analysis guidance, by contrast, wraps those same primitives in a domain specific pattern: Kinesis Video Streams for media ingest, OpenSearch for indexed search over events and clips, and Lambda driven workflows tuned for video centric operations like clip extraction, event correlation, and fleet wide search. In practice, that means we inherit the same proven performance envelope and scaling characteristics as the broader streaming patterns, but expressed through a solution that is already aligned with the operational excellence, reliability, cost, and sustainability expectations of a production grade video analytics service.

 AWS and DVSA:

A number of efforts in both industry and academia have attempted to build drone‑video analytics pipelines on AWS, and while none mirror the full spatial‑temporal, agentic‑reasoning architecture of your platform, several come close in spirit. One of the most visible industry examples is Amazon’s own reference implementation for real‑time drone‑video ingestion and object detection. This solution uses Amazon Kinesis Video Streams for live ingestion, a streaming proxy on EC2 to convert RTMP feeds, and an automated frame‑extraction workflow that stores images in S3 before invoking Lambda functions for analysis. The Lambda layer then applies Amazon Rekognition—either with built‑in detectors or custom Rekognition Custom Labels models—to identify objects of interest and trigger alerts through SNS. The entire system is packaged as a CDK deployment, emphasizing reproducibility and infrastructure‑as‑code, and demonstrates how AWS primitives can be orchestrated into a functional, cloud‑native drone‑video analytics pipeline. Github

AWS has also published a broader architectural pattern under the banner of “Video Analysis as a Service,” which generalizes these ideas for fleets of IoT video devices, including drones. This guidance describes a scalable, multi‑tenant architecture that supports real‑time event processing, centralized dashboards, and advanced search across large video corpora. It highlights the use of API Gateway, Lambda, and Step Functions for operational observability, IAM‑scoped permissions for secure access control, and AWS IoT Core Credential Provider for rotating temporary credentials at the edge. Although not drone‑specific, the architecture is clearly designed to support drone‑like workloads where video streams must be ingested, indexed, analyzed, and queried at scale. AWS

Together, these efforts illustrate how AWS has historically approached drone‑video analytics: by leaning heavily on managed ingestion (Kinesis Video Streams), serverless processing (Lambda), and turnkey vision APIs (Rekognition). They provide a useful contrast to your own platform, which treats drone video as a continuous spatial‑temporal signal and integrates vision‑LLMs, agentic retrieval, and benchmarking frameworks. The AWS examples show the industry’s earlier emphasis on event‑driven object detection rather than the richer semantic, temporal, and reasoning‑oriented analytics your system is now pushing forward.

References: CodingChallenge-02-10-2026.docx

 Integration of DVSA

The development of spatial-temporal analysis for first-person-view (FPV) drone imagery has evolved significantly, influenced by the constraints of onboard computing, the advancement of cloud platforms, and the availability of reliable geolocation. Initially, FPV feeds were treated as isolated images, with lightweight detectors operating on the drone or a nearby ground station. These systems could identify objects or hazards in real time but lacked temporal memory. Without stable geolocation, insights were fleeting, and analytics could not form a coherent understanding of the environment.

The transition began when public-cloud-based drone analytics platforms, initially designed for mapping and photogrammetry, started offering APIs for video ingestion, event streaming, and asynchronous model execution. This enabled FPV feeds to be streamed into cloud pipelines, overcoming edge compute limitations. This advancement marked the beginning of spatial-temporal reasoning: object tracks persisted across frames, motion vectors were aggregated into behavioral patterns, and detections could be anchored to cloud-generated orthomosaics or 3D models. However, the spatial dimension's fidelity remained inconsistent due to GNSS drift, multipath interference, and urban canyons, complicating the alignment of FPV video with ground truth, especially during fast or close-to-structure flights.

GEODNET introduced a decentralized, globally distributed RTK corrections network, providing centimeter-level positioning to everyday drone operators. With stable, high-precision geolocation, the cloud analytics layer gained a reliable spatial backbone. Temporal reasoning, enhanced by transformer-based video models, could now be integrated with precise coordinates, treating FPV footage as a moving sensor within a geospatial frame. This enabled richer analysis forms: temporal queries on site evolution, spatial queries retrieving events within a defined region, and hybrid queries combining both.

As cloud platforms matured, they began supporting vector search, event catalogs, and time-indexed metadata stores. FPV video could be segmented semantically, each tagged with geospatial coordinates, timestamps, and embeddings from vision-language models. This allowed operators to ask natural-language questions and receive results grounded in both space and time. GEODNET's corrections ensured alignment with real-world coordinates, even in challenging environments.

Recent advancements have moved towards agentic, closed-loop systems. FPV drones stream video to the cloud, where spatial-temporal analytics run continuously, generating insights that flow back to the drone in real time. The drone adjusts its path, revisits anomalies, or expands its search pattern based on cloud-derived reasoning. GEODNET's stable positioning ensures reliable feedback loops, enabling precise revisits and consistent temporal comparisons. In this architecture, FPV imagery becomes a live, geospatially anchored narrative of the environment, enriched by cloud intelligence and grounded by decentralized GNSS infrastructure.

The evolution of FPV analytics into truly spatial-temporal systems was driven by scalable reasoning from public-cloud platforms and trustworthy positioning from GEODNET. Together, they transformed raw video into a structured, queryable, and temporally coherent source of insight, setting the stage for the next generation of autonomous aerial intelligence.

Earlier spatial-temporal analysis pipelines' limitations are evident when compared to a system designed from first principles to treat drone video as a high-dimensional, continuously evolving signal. Our platform departs from historical approaches by treating time as a primary computation axis, allowing for rigorous modeling of persistence, causality, and scene evolution. This integration of detection, tracking, and indexing components into a unified spatial-temporal substrate results in a qualitatively different analytical capability.

Object tracks become stable, queryable entities embedded in a vectorized environment representation, supporting advanced reasoning tasks such as identifying latent behavioral patterns, detecting deviations from learned temporal baselines, or correlating motion signatures across flights and locations. The platform's geospatial grounding, enhanced by GEODNET's corrections, integrates positional data directly into feature extraction and embedding stages, producing embeddings that are both semantic and geospatial.

The platform emphasizes agentic retrieval and closed-loop reasoning, transforming the drone from a passive collector into an adaptive observer. Temporal anomalies trigger targeted re-inspection, semantic uncertainty prompts viewpoint adjustments, and long-horizon reasoning models synthesize multi-flight evidence to refine hypotheses. This results in a more efficient and scientifically grounded sensing loop.

Benchmarking-driven design principles, adapted from reproducible evaluation frameworks like TPC-H, expose the performance of spatial-temporal analytics to systematic scrutiny. Standardized workloads, cost-normalized metrics, and scenario-driven evaluation suites allow for comprehensive performance measurement, positioning the platform as a reference point for the field.

The integration of multimodal vector search and vision-language reasoning enables open-ended queries combining spatial constraints, temporal windows, and semantic intent. This redefinition of FPV video as a dynamic, geospatially grounded dataset marks a substantive advancement over prior attempts, setting a new trajectory for spatial-temporal drone analytics.

This is a summary of the book titled “Work Without Jobs: How to Reboot Your Organization’s Work Operating System” written by Ravin Jesuthasan and John W. Boudreau and published by MIT Press in 2022. The modern workplace is undergoing a profound transformation, driven by rapid technological advancement and shifting expectations around how work should be organized whether it be with ownership in demarcated roles or shared contributions to a workflow. The authors build on the premise that the traditional job-centered model can no longer keep pace with this change. Instead of treating work as a fixed set of duties assigned to static roles, they propose a radical shift: breaking work down into its component tasks and reassembling it in more flexible, dynamic ways.

According to the authors, organizations have long relied on “constructed” jobs—formal descriptions that bundle skills, responsibilities, pay structures and performance measures into tidy packages. But as automation, artificial intelligence and the gig economy reshape the labor landscape, these rigid constructs increasingly hinder progress. They advocate for “deconstruction,” a process of stripping jobs down to the tasks and capabilities they truly require. From there, organizations can “reconstruct” work in ways that better align with workers’ strengths, available technologies and emerging strategic priorities.

This shift represents more than a structural change; it is a reimagining of the workplace operating system itself. Just as computers run on different operating systems, organizations rely on unwritten systems that define hierarchies, job titles, and even how they interface with unions and social institutions. But the old system—built around full-time employees holding stable roles—is becoming obsolete. Talent now flows freely across organizational boundaries, and work increasingly blends contributions from full-time employees, contractors, gig workers and AI-driven processes.

To navigate this transition, They recommend treating work design as an ongoing experiment. Instead of large-scale, top-down restructuring, leaders should test small changes that reveal better ways of organizing tasks and deploying resources. Early experiments might involve redistributing tasks among employees, augmenting work with automation or tapping into external talent pools. These incremental steps can lead to a “return on improved performance,” where efficiencies gained from better task alignment generate compounding value.

Examples like Genentech illustrate the power of deconstruction in practice. By creating personas that represent archetypes of workers suited to certain tasks, the company freed employees to work more flexibly and attracted new talent seeking adaptable roles. Other organizations, such as agricultural co-op Tree Top, have used automation to handle repetitive tasks, allowing human workers to focus on more complex, variable work.

This reimagined operating system also expands the ways organizations engage talent. Beyond traditional hiring, leaders can explore options such as talent exchanges with other firms, gig work platforms, innovation partnerships with universities, crowdsourcing initiatives and internal talent marketplaces that let employees pursue projects outside their formal roles. As workers progress through their careers, they will increasingly be defined by the skills and capabilities they develop rather than by tenure or conventional degrees. Stackable credentials and modular learning pathways will further support this fluidity.

In such an environment, organizations must embrace a culture of continuous reinvention. Rather than relying on fixed job descriptions, leaders must constantly adjust workflows, coordinate cross-functional teams and foster organizational agility. As automation and AI take on more tasks, work will evolve daily—becoming slightly more automated, adaptive and collaborative over time. Teams will need to shed outdated routines and embrace perpetual upgrades similar to those common in the tech world.

Leadership itself will be reshaped by this evolution. Executives and managers will see their own roles deconstructed and redesigned as they move toward more fluid, project-based forms of leadership. They will establish strategic guardrails while enabling employees to form and reform agile teams as needed. With blurred boundaries between roles, managers must excel at human-centered leadership, guiding teams through constant change and integrating human and technological contributions seamlessly.

They emphasize that work will become increasingly social rather than transactional. Even independent contractors and gig workers develop psychological ties to the organizations they serve. Leaders can strengthen these connections by fostering supportive, inclusive cultures that value emotional well-being, diversity and open communication. As networks of gig workers and task-based contributors grow, organizations will need new ways to recognize collaboration, protect worker welfare and understand the informal social structures that drive value creation.

Clinging to job-based models limits organizations’ ability to harness both human and automated potential. By adopting new work operating systems grounded in flexibility, inclusion and continuous reinvention, companies can become more adaptive, empowering and future-ready—and workers can thrive in more meaningful, dynamic and socially connected ways.