2025-12-22 18:16:01
How fluent answers quietly bypass logic, metrics and governance with AI in BI.
We didn’t test AI assistants to see which one sounded smarter. We tested them to see which one followed the rules.
Same data. \n Same semantic model. \n Same fiscal calendar. \n Same enterprise BI environment.
And yet, the answers were different.
Not because the data changed but because the AI’s relationship to governance did.
That difference is subtle. \n It’s quiet. \n And in enterprise analytics, it’s dangerous.
Enterprise Business Intelligence doesn’t usually fail because dashboards are wrong.
It fails because definitions drift.
Fiscal weeks quietly become calendar weeks. \n Ratios get calculated at the wrong grain. \n Executive summaries sound confident while skipping required comparisons.
Before AI, this drift happened slowly through bad reports, shadow spreadsheets and one-off analyses.
AI changed that.
Now drift happens instantly, conversationally and with confidence.
As Gartner has repeatedly warned in its analytics research:
The most common cause of analytics failure is not poor data quality, but the lack of governance over how insights are generated and interpreted.
An AI assistant can give you an answer that sounds right, looks polished and feels authoritative while violating the very rules your organization depends on to make decisions.
Fluency is not correctness. Confidence is not governance.
And AI is exceptionally good at hiding the difference.
We didn’t ask AI assistants trivia questions.
We asked them enterprise questions - the kind executives ask without warning.
Payroll-to-sales ratios. \n Fiscal-week comparisons. \n Year-over-year performance. \n Executive summaries based on governed metrics.
We built a 50-question test harness designed to punish shortcuts.
If an AI:
Same prompts. \n Same governed semantic model. \n No excuses.
We weren’t measuring how clever the AI sounded. We were measuring how it behaved when the rules mattered.
Microsoft’s own Power BI and Fabric documentation explicitly acknowledges this risk:
AI-generated insights must respect the semantic model and business definitions to ensure consistent and trusted decision-making.
That shift is exactly where things start to break.
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What emerged wasn’t a product comparison.
It was something more fundamental.
| The Conversational AI Assistant | The Semantically Anchored AI Assistant | |----|----| | This assistant was fast. \n Helpful. \n Confident.It wanted to keep the conversation moving.When a question was ambiguous, it filled in the gaps. \n When a rule was inconvenient, it improvised. \n When governance slowed things down, it optimized for flow. \n It tried to help. \n That fluency feels empowering until the rules matter. | This assistant behaved differently.It was stricter. \n Sometimes slower. \n Less willing to “just answer.”It refused to reinterpret fiscal logic. \n It respected aggregation constraints. \n It stayed bound to governed definitions even when that made the response less fluent. \n It didn’t try to help. \n It tried to be correct. | | As one popular analytics platform blog puts it: \n \n “Modern AI assistants prioritize conversational fluency to reduce friction between users and data.” | Microsoft describes this design philosophy clearly: \n \n “Semantic models define a single version of the truth, ensuring all analytics and AI experiences are grounded in governed definitions.” |
The conversational assistant didn’t usually fail spectacularly.
That would have been obvious.
Instead, it failed quietly.
The answers weren’t absurd.
They were almost right. And that’s the problem.
Gartner has described this exact failure mode:
Analytics errors that appear reasonable are more likely to be trusted and acted upon than obviously incorrect results.
In enterprise BI, “almost right” is worse than wrong because it gets trusted.
No one double-checks a confident answer delivered in natural language.
Executives don’t ask whether a metric was calculated at an approved aggregation level.
They assume it was.
Semantic models already define what metrics mean:
But AI introduced a new risk.
There’s now an interpreter between the question and the model.
Semantic anchoring is what constrains that interpreter.
It doesn’t add new rules. It doesn’t change your semantic model.
It limits how much freedom the AI has when translating natural language into analytical logic.
As Microsoft’s AI governance guidance states:
AI systems must operate within defined business constraints to avoid generating misleading but plausible outputs.
| When semantic anchoring is strong. | When semantic anchoring is weak. | |----|----| | AI cannot bypass fiscal logic. \n AI cannot invent aggregation levels. \n AI cannot smooth over missing comparisons. | AI fills gaps creatively. \n AI optimizes for fluency. \n AI drifts even when the data is perfect. |
The data didn’t change. The interpretation did.
Most discussions about AI in BI focus on accuracy.
That’s the wrong lens.
The real risk is interpretive variance.
Two people ask the same question. \n The AI answers differently not because the data changed, but because the rules weren’t enforced consistently.
Gartner calls this out directly:
Consistency of interpretation is a prerequisite for trusted analytics, especially in executive and financial decision-making.
That’s not an AI failure. That’s a governance failure at the AI interaction layer.
And it’s exactly where most enterprise BI teams aren’t looking.
This isn’t an argument for or against any specific product.
It’s about design philosophy.
You can build AI assistants that: Optimize for conversation Or optimize for constraint.
Both have a place. But not in the same context.
Exploratory analytics? Ad-hoc questions? Early hypothesis generation?
Let AI be flexible.
Executive reporting? Financial performance? Governance-intensive metrics?
AI must be anchored. Because in those contexts, correctness beats fluency every time.
AI didn’t break Business Intelligence. Ungoverned AI did.
Vendor blogs often promise “faster insights” and “natural conversations with data.”
The future of AI in analytics isn’t about better prompts.
It’s about better constraints.
The most valuable AI assistant in your organization won’t be the one that talks the best.
It will be the one that refuses to break the rules even when no one is watching.
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2025-12-22 18:15:28
In our multitasking culture, the specific moments of cognitive overload are rarely accurately described. It's not dramatic. It's not a feeling of panic. It's more like being in an infinitely vast library, but the cataloging system has broken down, and your mind quietly loses the ability to properly "categorize" things.
You can walk down the aisles.
You can read the book titles.
But you can't put labels on them.
The organizational function that usually keeps your work in order simply stops working effectively.
I know this feeling well because I've experienced it myself.
While building a journaling system, I hit severe neural fatigue. I started missing small, almost embarrassing errors—basic implementation mistakes I would normally catch instantly. Because something upstream had degraded: attention, sequencing, and the ability to hold context long enough to verify reality. That "upstream degradation" is what we need to name. Not to moralize it. Not to pathologize it. But to recognize what you're actually experiencing. This is what cognitive overload looks like from the inside—and what we know about what's happening underneath it.
Even after sleeping, the feeling of exhaustion didn't go away.
I couldn't sleep because my work wasn't finished. And because my work wasn't finished, I couldn't shake off the fatigue.
Even in bed, I was mentally tracing the steps towards completing my tasks, one by one.
Studies on interrupted work are consistent: interruptions raise stress and push people to work faster, often at the expense of accuracy and quality. Over time, many people notice the same shift—attention feels shorter and easier to break. That isn’t a personal shortcoming. It’s what happens when you spend your days in a fragmented environment. Gloria Mark’s research on attention and digital behavior has tracked this pattern for years. The core issue is structural. When a day is built around constant task switching, focus becomes intermittent by default. And intermittent focus has a price.
Our brains can't completely switch between tasks. Sophie Leroy's research on attention residue clearly demonstrates what many people experience: when moving from one task to another, a portion of our attention remains focused on the previous task. Only the remaining attention can be directed to the next task.
This is why information overload leads to confusion. You might open a document, but you're still mulling over the contents of the meeting. You try to write an email, but you can't stop thinking about the emails you haven't replied to. You start debugging, but other thoughts continue running in the background. From the outside, it might look like procrastination. But internally, you're experiencing a feeling of being unable to fully concentrate because you haven't completely detached yourself from the previous task.
Before blaming yourself, understanding this transition period to the next task can be helpful for your mind and brain.
In this state, task switching produces measurable behavioral costs, such as decreased reaction time and increased errors. In fact, this state is quite common these days. But let's delve into what's happening in the brain and mind.
You don't need to memorize brain regions to understand what this means: Switching forces the brain to reconfigure what matters. What is the goal? What is relevant? What is noise? What is the next step?
When overload is chronic, your day becomes a repeated demand to reconfigure goals—without adequate recovery. Eventually, the system starts failing in predictable ways.
The shallow version: "We made too many decisions, so we're depleted." The accurate version is more sequential: Attention fragments Context becomes unstable Error-checking degrades More micro-decisions are required just to reorient Cognitive control becomes expensive You begin defaulting to whatever requires the least effort What this feels like internally is not simply "tired." It feels like our mind starts choosing whatever requires the least cognitive negotiation. Not because we became careless. Because the cost of caring became too high.
When the brain overloads, does something like spiritual exhaustion occur? If we define "spiritual" as the felt capacity for clarity, inner continuity, ethical orientation, and meaning—then yes. Overload often produces what people describe as spiritual depletion. But it's not mystical. It's mechanistic: When attention fragments, metacognition weakens. We lose the ability to notice what our mind is doing. When fatigue rises, self-regulation becomes costly, and reactivity becomes more likely. When context is unstable, meaning collapses into urgency. Everything becomes "next." Nothing becomes "important." Contemporary neuroscience increasingly frames fatigue not only as a "low battery," but as a state where the perceived cost of cognitive control rises, shaping what we are willing—or able—to do.
So the "spiritual" symptom is often this: we stop feeling internally governed. We start feeling externally pushed. We begin to lose sight of our purpose in life. That is a loss of inner agency. Not moral.
Buddhist epistemology helps because it doesn’t treat perception as a neutral recording. It treats knowing as conditional—shaped by causes, context, and internal state.
When those conditions shift—sleep debt, sensory overload, emotional strain, constant urgency—the mind doesn’t simply get “tired.” It starts seeing less cleanly. The same situation can look harsher, narrower, and more urgent than it actually is.
In Buddhist terms, the first consequence of overload isn’t that you make slightly worse choices. It’s that the world you’re choosing from changes.
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\ Threats and demands take up more space. The future compresses. Immediate relief starts to look like the only reasonable move. And the thought-stream becomes more adhesive: this stress is me.
That’s why overload can feel existential. It’s not only “too much work.” It’s a distortion of perception—and once you’re inside it, it’s easy to mistake that distortion for reality.
We all know the basics: getting enough sleep, exercising regularly, and eating a healthy diet. And most knowledge workers, whether they like it or not, find themselves in situations where they have to multitask. While we may not be able to change larger societal systems overnight, we can change how we interact with those systems. We can change the workplace of the future. We should start now, before it's too late.
Vipassana is often misunderstood as "calming down." That's not the core value. In overload, the first thing that goes isn’t intelligence. It’s perceptual resolution. Attention stops landing. our skim. our take in words without taking in meaning.
Then a familiar pattern kicks in: the urge to “fix it” shows up before we can even name what’s wrong. Self-critical voices become louder and more persuasive, creating interpretations that seem like facts. Thoughts like, "I'm falling behind," "This is pointless," or "I should switch to another task" run through the mind.
So we reach for substitute actions—checking messages, switching tabs, reorganizing, making lists—because our mind can’t hold the situation steady long enough to choose precisely.
At the same time, the inner commentary gets louder. It starts speaking in conclusions, not observations: I’m behind. This is pointless. I should switch tasks.
Vipassana practitioners learn to catch the turning point: I’m seeing my reaction to the task. It’s a small recognition, but it matters. It keeps you from treating a strained mental state as reality—and it reframes overload as a conditional process, not a verdict on your ability.
There is a form of intelligence that not only solves problems. It understands the mind that is solving them. You can call it metacognition. We can call it contemplative literacy. In Buddhist terms, it's prajna—direct insight into the nature of mind and experience. Spiritual intelligence, grounded in observation, is the capacity to perceive internal experience clearly enough to avoid being unconsciously driven by it. Under overload, that capacity degrades first. Which is exactly why naming the mechanism—attention fragmentation, residue, fatigue cascades, perceptual distortion—is not a self-help tactic. It's epistemic hygiene. Because once we can accurately recognize what you're in, we stop making a secondary mistake: We stop treating a degraded cognitive state as a verdict on who we are.
Many people come to monasteries because they want their mind to rest. But by the time they arrive, they’ve already burned out—and they’ve often had to quit their job to make it happen. There’s something we can do before it gets that far.
2025-12-22 18:14:08
Brazil's largest private bank, Itaú Unibanco, has advised its clients to consider allocating up to 3% of their portfolios to Bitcoin. The recommendation was made by Itau Asset Management, the bank's investment division.
In a recent report outlining investment strategies for the coming year, the bank states that holding a small portion of Bitcoin can further help investors diversify their portfolios and reduce risk.
Itau explained that Bitcoin behaves differently from traditional assets like stocks and bonds. BTC can help balance a portfolio during periods of market uncertainty. The bank suggested allocating between 1% and 3% to Bitcoin and advised investors to treat it as a long-term investment rather than a short-term trade.
Itau also stated that Bitcoin's global nature could help protect investors from local currency fluctuations, especially in an emerging market like Brazil. Renato Eid, Itau Asset Management's head of beta strategies and responsible investment, in a recent research note, concluded:
"The idea is not to make investors use bitcoin as the core of their total portfolio, but rather to integrate it as a complementary component." In his words, "the goal is to capture returns uncorrelated with domestic cycles, partially protect against currency devaluation, and add potential for long-term appreciation."
This move highlights the growing acceptance of cryptocurrencies by traditional financial institutions. In recent years, several global banks and asset managers have begun to recognize Bitcoin as a legitimate investment asset.
\n Itau itself has been expanding its crypto services. The bank already allows clients to trade cryptocurrencies through its digital investment platforms and has supported crypto-related investment products.
\n It will be recalled that the bank launched Bitcoin and Ethereum trading in December 2023. Also, the central bank in Brazil recently released new rules for domestic digital asset firms, requiring them to register with the central bank to operate legally in the country.
Itau's recommendation does not mean investors should go "all in" on Bitcoin. Instead, the bank advises a careful, limited exposure to improve long-term portfolio performance.
The advice signals a shift in how major banks in Latin America view digital assets — from risky speculation to a possible part of a balanced investment strategy.
2025-12-22 18:06:25
\ Modern backend systems are increasingly asked to do more than serve requests. They ingest continuous streams of events, process historical data at scale, recompute results when business logic changes, and remain correct in the presence of partial failures. For engineers coming from a REST API background, this shift often feels unintuitive: the tools, abstractions, and even the definition of “done” are fundamentally different.
This article is a practical introduction to Apache Beam from the perspective of a conventional backend engineer. Rather than focusing on APIs or syntax, it explains why Beam exists, how its execution model works under the hood, and what enables it to process enormous volumes of data reliably.
The discussion is intentionally GCP-focused, covering how Beam pipelines are executed on Dataflow, orchestrated with Cloud Composer, and provisioned using infrastructure-as-code. The goal is not to replace documentation, but to provide a conceptual bridge into the dataflow ecosystem—connecting familiar service-oriented thinking with the realities of large-scale, distributed data processing.
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Most backend engineers start with a request/response mental model. A client sends a request, the server processes it synchronously or asynchronously, and a response is returned. Even when systems become distributed—behind load balancers, message queues, or service meshes—the core abstraction remains the same: a discrete interaction with a defined start and end.
Dataflow systems operate under a fundamentally different model. Instead of individual requests, they deal with continuous or bulk flows of data. There is often no single “caller” waiting for a response, and in many cases no clear notion of when processing is “finished.” Data arrives over time, possibly out of order, and computation progresses as data becomes available.
This difference is more conceptual than architectural:
Trying to reason about large-scale data processing using request/response abstractions quickly becomes limiting.
Apache Beam was created to address these limitations by introducing a unified programming model for data processing, independent of execution environment.
\ Instead of writing code that describes how data should be processed step by step, Beam encourages developers to focus on business domain and primarily describe:
Key problems Beam explicitly targets:
Crucially, Beam assumes that distribution, parallelism, retries, and failures are normal. Its abstractions are designed so that a pipeline can be executed across thousands of machines, restarted partially or fully, and still produce correct results.
For REST API engineers, this requires a shift in mindset: away from services responding to requests, and toward pipelines continuously transforming data. Beam exists to make that shift possible without forcing developers to manually manage the complexities of distributed execution.
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In Apache Beam, a Pipeline is not a program in the traditional sense. It is a logical description of a data processing graph. When you define a pipeline, you are not executing code; you are declaring how data should conceptually flow through a series of transformations.
This graph is directed and acyclic, commonly refered as DAG. Each node represents a transformation, and each edge represents a collection of data flowing between transformations. The pipeline definition is declarative: it specifies relationships and dependencies, not execution order or resource allocation.
A typical pipeline should look readable in code, and be domain centric:
Pipeline pipeline = Pipeline.create(options);
pipeline
.apply("ReadEvents",
PubsubIO.readStrings()
.fromSubscription("projects/my-project/subscriptions/iot-events"))
.apply("ParseJson",
MapElements.into(TypeDescriptors.kvs(TypeDescriptors.strings(), TypeDescriptors.strings()))
.via(EventParser::parse))
.apply("WindowByMinute",
Window.into(FixedWindows.of(Duration.standardMinutes(1))))
.apply("CountPerKey",
Count.perKey())
.apply("WriteToBigQuery",
BigQueryIO.<KV<String, Long>>write()
.to("my-project:analytics.iot_events")
.withFormatFunction(RowMapper::toTableRow)
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND));
pipeline.run();
\ For engineers used to imperative control flow—loops, conditionals, sequential method calls—this can feel unusual. In Beam, there is no guarantee that transformations will execute in the order they appear in code. The runner is free to reorder, fuse, parallelize, or split stages as long as the logical graph semantics are preserved.
This separation between definition and execution is what allows a single pipeline to run unchanged on different execution engines, from local runners to large-scale distributed systems.
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A PCollection represents a dataset in a pipeline, but it is not equivalent to a list, stream, or table. It is a logical abstraction of data, not a concrete in-memory structure.
PCollections come in two forms:
This distinction is critical. Many operations that are trivial on bounded data—such as global aggregation—are undefined or require additional constraints on unbounded data. Beam addresses this through concepts like windowing and triggers, but at the core level, the key idea is that data may never be “complete.”
Importantly, a PCollection does not imply where data is stored, how it is partitioned, or whether it is currently materialized. These details are intentionally hidden, allowing the runner to optimize data movement and storage transparently.
A PTransform defines how input PCollections are converted into output PCollections. It describes what computation should be applied, not how it should be performed.
This distinction is subtle but fundamental. A PTransform:
Instead, it declares a logical operation—such as mapping elements, grouping by key, or combining values—that the runner later translates into executable steps.
Because PTransforms are declarative, they can be analyzed, optimized, and rearranged by the runner. Multiple transforms may be fused together, executed remotely, or retried independently. From the developer’s perspective, this means writing code that is side-effect free and repeatable, even though it may execute many times across different machines.
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Apache Beam relies heavily on immutability and functional-style processing. Once a PCollection is created, it is never modified. Each transform produces a new PCollection rather than altering existing data.
For this model to work, transforms must also be pure and deterministic. Given the same input, a transform should always produce the same output, without relying on external mutable state or time-dependent behavior.
In distributed systems, failures are expected. A transform may be executed multiple times, partially or fully, across different workers. Beam’s model assumes this and designs for it explicitly. Deterministic, side-effect-free computation is what allows pipelines to scale horizontally while remaining correct—even when processing massive datasets over long periods of time.
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In Apache Beam, a runner is not a library that “calls your code.” It is an execution engine that interprets a pipeline definition and turns it into a distributed computation. When you write a Beam pipeline, you are producing a portable, runner-agnostic description of a dataflow graph. The runner’s responsibility is to take that description and map it onto real infrastructure.
This distinction is critical. Beam itself does not decide:
All of that is delegated to the runner. Beam defines semantics; the runner defines mechanics. This is why the same pipeline can execute locally, on a managed cloud service, or on a custom distributed system without changing application logic.
When a pipeline is submitted for execution, the runner first translates the logical pipeline graph into an execution graph. This graph is no longer a high-level description of transforms and collections, but a concrete plan composed of executable stages.
During this translation phase, the runner may break transforms into smaller executable units, reorder compatible operations, combine adjacent transforms or insert-shuffle the persistence boundaries.
The resulting execution graph often looks very different from the pipeline as written in code. ==This is intentional==. The logical graph expresses the human readable domain definitionas and the execution graph expresses efficient computation instructions.
This translation step is what enables Beam’s portability model. The same logical pipeline can result in different execution graphs depending on the runner, available resources, and configuration.
Once a pipeline is translated, the runner must distribute work across multiple machines. This immediately introduces a constraint that is easy to overlook: all user-defined logic and data must be serializable.
Beam does not execute your code in the same process where it is defined. Instead, it serializes user-defined functions, ships them to worker processes and finally deserializes and executes them remotely.
This means that serialization is not an implementation detail—it is a core part of the programming model. In fact having any of the objects not serializable will lead to runtime exeptions. Any object captured by a transform must be safely serializable and deterministic. Hidden dependencies on local state, thread-local variables, or non-serializable resources will fail at runtime or, worse, produce inconsistent results.
To move data between stages in the execution graph, Beam relies on Coders. A coder defines how elements in a PCollection are encoded into bytes and decoded back into objects.
Every boundary in the execution graph—especially shuffles and repartitioning steps—requires data to be serialized. The choice of coder directly affects performance.
For simple types like String or Integer, Beam can infer coders automatically. For complex domain objects, custom coders are often required to ensure efficiency and correctness. Poorly defined coders can easily become a bottleneck, even if the transform logic itself is trivial.
Coders are one of the reasons Beam can process extremely large datasets: they provide a controlled, explicit contract for how data moves across machines and execution stages.
A common confusion point is the first glance similarities between these two.
Both Coders and Java’s Serializable exist because Apache Beam is distributed, but they solve different problems at different layers.
Serializable is about shipping code. Beam uses it to move user-defined logic—such as DoFns and their captured configuration—to remote worker JVMs. It answers the question: can this code and its state be sent to another process?
Coders are about shipping data. They define how elements in a PCollection are encoded to bytes and decoded again as data moves between pipeline stages, workers, and machines. Coders directly affect performance, memory usage, and shuffle cost
After translation, the runner applies optimizations to maximize throughput. One of the most important is fusion: combining multiple transforms into a single execution stage when possible. Fusion reduces unnecessary data materialization and network transfer, allowing elements to flow directly through several transforms in memory.
At the same time, the runner introduces sharding to parallelize work. Input data is split into independent shards, processed concurrently by multiple workers. As data flows through the pipeline, it may be repartitioned multiple times to balance load or satisfy grouping requirements.
These mechanisms work together:
Fusion minimizes overhead
Sharding maximizes parallelism
Retry and recomputation handle failures transparently
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The result is a system where massive volumes of data can be processed efficiently without explicit threading, batching, or partitioning logic in user code. The developer describes what should happen; the runner continuously adapts how it happens at scale.
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On Google Cloud, Apache Beam pipelines are most commonly executed using Google Cloud Dataflow. Dataflow is not a separate programming model; it is a fully managed runner that understands Beam’s execution semantics and provides the infrastructure required to run pipelines at scale. \n
When a pipeline is submitted to Dataflow, the following happens at a high level:
From the developer’s perspective, the process is largely opaque by design. There is no need to manage clusters, provision machines, or handle worker placement. Dataflow abstracts these concerns away so that the pipeline definition remains focused on data transformation rather than infrastructure management.
This model aligns closely with Beam’s philosophy: the pipeline describes what should happen, while the runner decides how it happens in a concrete environment.
One of the key advantages of running Beam pipelines on Dataflow is automatic resource management. Worker pools are not fixed in size. Instead, Dataflow continuously adjusts the number of workers.
Workers are treated as ephemeral execution units. They may be created, reused, or terminated at any point during pipeline execution. Pipelines are therefore expected to tolerate worker churn without relying on local state or long-lived processes.
Monitoring is an integral part of this lifecycle. Dataflow exposes detailed metrics, including:
These metrics are not merely informational; they directly influence autoscaling decisions. Observability and execution are tightly coupled, allowing the system to adapt dynamically as workload characteristics change. For engineers used to manually scaling services, this represents a significant shift. Capacity planning becomes an ongoing, automated process rather than a static configuration.
Because Dataflow is fully managed, cost is primarily driven by resource usage over time rather than fixed infrastructure. Factors that influence cost include:
Pipeline design has a direct impact on both performance and cost. Inefficient coders, excessive shuffles, or poorly chosen windowing strategies can dramatically increase resource consumption. Conversely, well-fused pipelines with efficient serialization can process large volumes of data with minimal overhead.
This reinforces an important principle: while Dataflow removes much of the operational burden, it does not eliminate the need for thoughtful pipeline design.
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A common point of confusion when first working with Apache Beam is assuming that pipelines are responsible for when they run. In practice, Beam deliberately avoids this concern. A Beam pipeline defines how data is processed, not when or why execution starts.
Orchestration exists at a different layer.
On Google Cloud, this role is typically filled by Cloud Composer, which is built on Apache Airflow. Composer does not execute Beam pipelines itself. Instead, it coordinates their execution by triggering Dataflow jobs and reacting to their outcomes.
This separation of concerns is intentional. Beam focuses on data correctness and scalability, while Airflow focuses on workflow control. Conflating the two leads to pipelines that are harder to reason about, test, and operate.
In a typical GCP setup, Beam pipelines are executed on Dataflow and triggered by Airflow tasks. These tasks may:
From Airflow’s perspective, a Beam pipeline is an external job with a well-defined lifecycle: submitted, running, succeeded, or failed. Airflow does not inspect pipeline internals; it treats Dataflow as an execution service.
For engineers familiar with cron jobs or ad-hoc scripts, this structured orchestration layer provides a more reliable and observable way to manage large-scale data workflows.
One of Airflow’s primary strengths is explicit dependency management. Complex data systems often involve multiple pipelines with ordering constraints, shared inputs, or downstream consumers. Airflow encodes these relationships directly in workflow definitions, making dependencies visible and auditable.
Retries are handled at the orchestration level rather than inside the pipeline. If a Dataflow job fails due to transient infrastructure issues, Airflow can retry the job without modifying pipeline code. This keeps error-handling logic centralized and consistent.
Backfills are another critical capability. When historical data needs to be reprocessed—due to schema changes, logic updates, or late-arriving data—Airflow can systematically re-run pipelines over past time ranges. Because Beam pipelines are deterministic and replayable by design, this approach scales far better than ad-hoc reprocessing scripts.
Together, these features allow Beam pipelines to operate as reliable, long-lived components in a larger data platform, rather than isolated batch jobs triggered manually.
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At scale, data pipelines stop being “jobs” and start behaving like long-lived infrastructure. They have environments, dependencies, permissions, and operational requirements that evolve over time. Managing these concerns manually does not scale.
Infrastructure as Code (IaC) treats pipelines and their supporting services as versioned, reproducible artifacts. Instead of configuring environments through consoles or ad-hoc scripts, all resources are declared explicitly and stored alongside application code.
On Google Cloud, Dataflow and Cloud Composer are typically provisioned using Terraform. Terraform allows teams to define dataflow job configurations and templates, define Composer environments, create service accounts and permissions and configuring networking and storage dependencies.
By encoding these resources in Terraform, infrastructure becomes reviewable and testable. Changes to pipeline environments can be peer-reviewed in the same way as application code, reducing the risk of configuration drift or undocumented dependencies.
This approach also enables repeatable multi-environment setups. Development, staging, and production pipelines can share the same structure while differing only in controlled parameters, making promotion between environments predictable and low-risk.
A typical terraform script for defining a data pipeline in Terraform may looks something like:
resource "google_dataflow_job" "beam_pipeline" {
name = "example-beam-pipeline"
region = "europe-west1"
template_gcs_path = "gs://my-dataflow-bucket/templates/iot_pipeline"
temp_gcs_location = "gs://my-dataflow-bucket/temp"
parameters = {
inputSubscription = "projects/my-project/subscriptions/iot-events"
outputTable = "my-project:analytics.iot_events"
}
on_delete = "cancel"
}
Security and isolation are fundamental concerns in distributed data systems. Pipelines often process sensitive data and interact with multiple services, making fine-grained access control essential.
Using IaC, IAM roles and service accounts can be defined explicitly
Networking is treated similarly. Private service access, controlled egress, and environment-specific networks can be declared and enforced consistently. This prevents accidental cross-environment data access and simplifies compliance requirements.
Environment isolation completes the picture. Separate projects or environments for development, staging, and production ensure that experimentation and backfills do not interfere with critical workloads. When combined with Beam’s deterministic pipelines and Airflow’s orchestration, this results in data platforms that are not only scalable, but also auditable and operationally robust.
\
Apache Beam is well suited for problems where data volume, continuity, and correctness matter more than request latency. Typical examples include:
\ Beam excels when computation must scale horizontally and remain deterministic under partial failures. Its model is particularly effective when the same logic needs to be applied consistently to both historical and real-time data, without duplicating implementation across separate systems.
In these cases, Beam’s declarative pipeline model provides clarity, portability, and operational robustness that are difficult to achieve with ad-hoc services or scripts.
\
\ Despite its power, Apache Beam is not a general-purpose replacement for backend services. It is a poor fit for:
\ Common misuse patterns include:
\ These approaches conflict with Beam’s execution model and often lead to fragile, expensive, or hard-to-debug systems.
Apache Beam requires a shift in how engineers think about computation. Instead of services reacting to individual requests, Beam encourages designing systems that continuously transform data flows under explicit correctness guarantees.
For engineers coming from a REST API background, this shift can be nontrivial. However, once the execution model is understood—particularly how runners, serialization, and orchestration work together—Beam becomes a powerful tool for building scalable and reliable data platforms.
In cloud environments such as Google Cloud, this approach integrates naturally with managed execution, orchestration, and infrastructure tooling. The result is not just better performance, but a more disciplined way of reasoning about large-scale data processing systems.
2025-12-22 18:05:47
Are you planning, developing, or already deploying an AI system to make your processes more efficient? At whatever stage of the AI lifecycle your company is, aligning with the right ISO standards can significantly improve its responsibility, compliance, and robustness. In this article, I’ll suggest four key ISO standards for AI developers and deployers.
We quite often see confusion over basic AI terminology. Even high-level industry publications may use expressions such as “AI”, “AI System” or “Artificial General Intelligence” interchangeably. Not to mention the confusion over the difference between “AI Impact Assessment” (external effects on individuals and society) and “AI Risk Assessment” (internal organisational and operational risks) – two concepts clearly related, yet separate practices. Like a lighthouse in a mare magnum of misunderstood concepts and definitions, ISO 22989 sheds light on definitions and provides a shared vocabulary for AI concepts.
It is true that many principles charters, regulations or recommendations use their own definitions for the same concepts, which obviously does not help AI practitioners. ISO 22989 is a great reference point to clear doubts and incorporate into your AI policy. It can guide you in the drafting of your organisation’s policy by laying the terminological bedrock of your AI-related practices.
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Once we’re clear on what AI is, it is time to build your AI system in a way that allows you to develop and deploy it safely and in a controlled way. ISO 42001 gives all the necessary tools to build an effective AI management system.
Key elements of this best-practice standard comprise:
At the planning stage, you may want to conduct an AI Impact Assessment that scans all the potential, foreseeable negative impacts your AI system may cause before it is deployed. These comprise impacts on individuals (or groups of individuals) and society.
Periodically throughout the AI lifecycle, there is a strong expectation for organisations to conduct AI Risk Assessments, to make sure your AI system is
The AI Risk Assessment is contingent upon thorough Risk Assessment methodologies, a solid AI Risk Management program, and most importantly, continuous Risk Monitoring (inclusive of risk and performance metrics). This is why risk assessment should ideally be carried out on a regular basis (e.g., quarterly), to identify risks as soon as they arise, treat them, and limit any negative impact on individuals.
Additional areas of focus to make an AI management system truly effective are:
On the latter point, note that if your company demonstrates precise record-keeping, this will not only aid internal audits and regulatory inquiries, but also signal your commitment to play by the rules and, ultimately, to corporate and social responsibility. Many see this as a nice-to-have today, but it is already a strong differentiator across industries.
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More likely than not, at some point in your career you will have heard of the ISO 31000, regarded by many as the bible for risk management professionals. ISO 23894 adapts the principles and concepts of ISO 31000 to the AI environment.
Similarly to ISO 42001, this guidance emphasises the need for stakeholders’ expectations, achieving leadership’s buy-in, thorough resource planning, and setting clear roles and responsibilities. In addition, this standard proposes the famous ISO 31000’s risk management process, comprising:
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ISO 24027 can be considered an accessory to ISO 23894. This document provides practical guidance on how to assess the performance of an AI system, both in terms of robustness (does it match stakeholders’ expectations?) and fairness (does it unintentionally discriminate any demographic group?). I’ve previously discussed this standard on Hackernoon, describing the key takeaways as well as the strategies recommended by the ISO to evaluate AI systems’ performance.
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You can read more in this article.
\ Because they are globally recognised, ISO standards are a great tool and a safe first step when devising corporate processes. With appropriate tailoring to your organisation’s structure and needs, they will most likely lead you to implement best practices in your business area. ISOs focusing on AI are no exceptions, and there are plenty of them to help guide you and avoid the most negative societal impacts.
In a rapidly evolving regulatory environment, organisations that align early with recognised ISO standards will not only reduce legal and ethical risks but also build trust with customers, regulators, and society at large. Together, these standards form a practical and internationally recognised foundation for governing AI systems responsibly.
2025-12-22 18:02:40
\ The interface of the future isn't a keyboard. It isn't even a touchscreen. It’s a conversation.
For the last year, OpenAI’s Realtime API has been the king of the hill for developers building voice agents. It was magic, but it was expensive magic. Latency was "okay," pricing was "premium," and the ecosystem was a walled garden.
But yesterday, xAI (Elon Musk’s AI company) kicked down the door with the release of the Grok Voice Agent API.
They didn’t just release a "me-too" product. They released a direct challenge to the status quo: Lower latency, half the price, and native integration with the X ecosystem.
If you are building voice agents, customer support bots, or just hacking on weekend projects, here is why you need to pay attention.
In the world of Voice AI, latency is the difference between a conversation and a phone tree. If the user has to wait 2 seconds for a reply, the illusion breaks. You start talking over the bot. It gets awkward.
Grok Voice claims an average Time-to-First-Audio of roughly 0.78 seconds.
According to xAI’s internal benchmarks on "Big Bench Audio," this makes it roughly 5x faster than its closest competitors in specific reasoning tasks. They achieved this by building the entire stack in-house—training their own Voice Activity Detection (VAD), tokenizer, and acoustic models rather than stitching together third-party APIs.
Why this matters: For developers, this means you can finally build "interruptible" agents that feel like talking to a human, not a walkie-talkie.
This is the headline that will make CFOs happy.
xAI has undercut the market with a simple flat rate. If you are running a high-volume call center agent or a 24/7 companion app, that difference isn't just savings—it's margin.
Here is the smartest move xAI made: Compatibility.
The Grok Voice Agent API is compatible with the OpenAI Realtime API specification.
If you have already built your app on OpenAI’s stack, you don't need to rewrite your entire backend to test Grok. You can theoretically swap the endpoint, change the API key, and see if your latency improves and your bill goes down.
They also launched a dedicated plugin for LiveKit, the open-source infrastructure that powers most modern voice agents, making integration nearly instant for existing LiveKit users.
Grok isn't trained on a static archive of the internet from 2023. It has real-time access to the X (Twitter) firehose.
For a voice agent, this is a superpower. Imagine asking your AI assistant:
Most voice bots would hallucinate or tell you their knowledge cutoff date. Grok can query the live web and X posts instantly.
Furthermore, this API is the same stack powering the voice assistant inside millions of Tesla vehicles. It’s battle-tested in the harshest environment possible: a moving car with road noise, wind, and impatient drivers.
One of the coolest features for developers is "Emotional Prompting."
You can instruct the model to use specific paralinguistic cues using bracketed commands like [whisper], [laugh], or [sigh].
Instead of a robotic monotone, you can script interactions that require empathy (healthcare), excitement (gaming), or secrecy. This moves us one step closer to the Her operating system experience.
The AI Voice Wars have officially begun.
OpenAI has the brand. Google has the research. But xAI has the infrastructure (Colossus cluster), the data (X/Tesla), and now, the price point.
For developers, this competition is a gift. Better tools, faster models, and cheaper bills.
Go build something loud.
[whisper] and [laugh] cues to make your agents feel less robotic.Liked this breakdown? Smash that clap button and follow me for more deep dives into the API wars.
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