Engineering data pipelines for AI performance

In 2025, the performance of AI systems is only as good as the data they’re trained and operated on. As models become more precise, complex, and context-aware, the bar for clean, timely, and scalable data rises. 

This article breaks down the architectural and engineering principles behind high-performance data pipelines for AI, with a focus on web data ingestion. 

We’ll explore how tools like Airflow, dbt, and real-time processing frameworks fit into this puzzle, and how proxy and unblocking platforms like SOAX solve the foundational problem of reliable web data access.

Whether you're training LLMs, running sentiment models, or building real-time recommender systems, this post will help you understand what it takes to build a pipeline that can keep up with your AI stack.

Why data pipelines are essential for AI success

Modern AI systems don’t just consume data — they depend on it, continuously. Whether training foundation models or powering real-time inference systems, AI teams need pipelines that can automate, scale, and adapt across changing data conditions.

A robust AI data pipeline handles the full journey: ingesting raw inputs from sources like the web, transforming that data into structured formats, validating it for accuracy and consistency, and then delivering it to storage layers, feature stores, or ML frameworks.

But building this kind of pipeline — especially for web data — needs more than just scraping. It demands:

• Performance: Low-latency processing to support real-time and batch workflows

• Modularity: Plug-and-play architecture with tools like Airflow and dbt

• Observability: Monitoring and logging to debug data issues before they affect models

• Reliability: Resilience to site structure changes, anti-bot blocks, and schema drift

Without this foundation, model performance degrades, edge cases slip through, and retraining cycles get longer and more expensive. This is why web-scale AI isn’t just about better models — it’s about better pipelines.

Top components of an AI-ready data pipeline

Designing pipelines for AI isn’t just about moving data — it’s about shaping it for learning. An AI-ready pipeline must handle unpredictable input, prioritize quality, and support real-time responsiveness. Below are the key components that make up a modern, production-grade data pipeline for AI use cases.

1. Data ingestion layer

Every AI pipeline begins with ingestion — the process of capturing raw data from various sources and feeding it into the system. For AI use cases, especially those relying on external, real-time, or web-based inputs, this layer must be robust, flexible, and able to operate at scale.

Alongside web scraping, teams often ingest data from public APIs such as Reddit or news aggregators, or from internal systems like product telemetry, user interactions, or CRM exports. 

While structured APIs are generally easier to work with, they rarely provide the full picture AI models need — especially for nuanced tasks like sentiment analysis, trend detection, or training large language models.

On the tooling side, some teams use frameworks like Airbyte, which offers modular, open-source connectors that plug into both APIs and databases. But when web data is the core, many teams rely on custom-built Python pipelines, often combining requests and BeautifulSoup, to gain full control over how and when data is collected.

An effective ingestion layer doesn’t just pull data — it anticipates failure points, retries intelligently, and maintains consistency over time. In AI, where stale or incomplete data can degrade model accuracy, getting ingestion right is foundational.

2. Orchestration and scheduling

Once your ingestion logic is in place, the next challenge is keeping it running reliably — across dependencies, failures, and schedules. Orchestration tools bring discipline to your pipeline by defining when tasks run, in what order, and how failures are handled. For AI teams working with complex, multi-stage workflows, orchestration is non-negotiable.

Apache Airflow is the most widely adopted orchestrator in data engineering. It allows teams to define workflows as code (DAGs — Directed Acyclic Graphs), making it easy to schedule web scraping jobs, run data transformations, validate results, and pass outputs to storage or model training pipelines. Alternatives like Prefect and Dagster offer more modern developer experiences and richer observability, but Airflow remains the gold standard for production environments.

Here’s a simplified Airflow DAG that scrapes data from the web using SOAX, transforms it into a structured format, and stores it in a database:

from airflow import DAG

from airflow.operators.python import PythonOperator

from datetime import datetime

import requests

def scrape_data():

    proxies = { "http": "http://SOAX_PROXY", "https": "http://SOAX_PROXY" }

    response = requests.get("https://example.com/data", proxies=proxies)

    with open('/tmp/raw_data.json', 'w') as f:

        f.write(response.text)

def transform_data():

    import json

    with open('/tmp/raw_data.json') as f:

        raw = json.load(f)

    transformed = [{"title": r["title"].strip(), "timestamp": r["created_at"]} for r in raw]

    with open('/tmp/clean_data.json', 'w') as f:

        json.dump(transformed, f)

default_args = { "retries": 3, "retry_delay": timedelta(minutes=5) }

with DAG("web_scraping_pipeline", start_date=datetime(2025, 1, 1), schedule_interval="@hourly", default_args=default_args, catchup=False) as dag:

    scrape = PythonOperator(task_id="scrape_data", python_callable=scrape_data)

    transform = PythonOperator(task_id="transform_data", python_callable=transform_data)

    scrape >> transform

This DAG handles:

• Task scheduling (@hourly runs)

• Retry logic (3 retries with 5-minute intervals)

• Clear dependencies (transformation waits for ingestion)

To operate at scale, AI teams monitor task duration, success rates, and failure patterns using tools like Airflow’s web UI, Prometheus + Grafana dashboards, or native logs. Alerting on unusually long task durations or repeated retries helps identify issues before they impact downstream model performance.

In fast-moving AI workflows — where pipelines may retrain models daily or stream features into real-time systems — a well-orchestrated schedule is just as important as the code that runs inside it.

3. Transformation and cleaning

Once raw data is ingested, it must be structured, normalized, and cleaned — otherwise, it’s just noise. For AI systems, this stage is critical: messy inputs lead to poor predictions, costly retraining cycles, and even model drift. Whether you're building LLM training datasets or streaming features into real-time ranking models, transforming your data into a consistent and usable format is foundational.

Web data often arrives as HTML, deeply nested JSON, or semi-structured tables. The first step is parsing and extracting the relevant fields. Here’s an example using BeautifulSoup to extract product titles and prices from raw HTML:

from bs4 import BeautifulSoup

html = """<div class="product"><h2>AirPods Pro</h2><span class="price">$249</span></div>"""

soup = BeautifulSoup(html, "html.parser")

title = soup.select_one("h2").text.strip()

price = soup.select_one(".price").text.replace("$", "")

print({"title": title, "price": float(price)})

# Output: {'title': 'AirPods Pro', 'price': 249.0}

After extraction, you often need to clean, deduplicate, and validate the data — especially when combining sources or tracking data over time. Pandas is widely used here:

import pandas as pd

df = pd.DataFrame([

    {"title": "AirPods Pro", "price": 249},

    {"title": "AirPods Pro", "price": 249},

    {"title": "Galaxy Buds", "price": 129}

])

df_clean = df.drop_duplicates().reset_index(drop=True)

print(df_clean)

In production pipelines, these transformations often move into dbt (Data Build Tool) — a framework for defining SQL-based models with versioning, schema checks, and freshness alerts. Here’s an example of a products.sql model in dbt that makes sure prices are numeric and only includes fresh data:

-- models/products.sql

SELECT

  title,

  CAST(price AS FLOAT) AS price,

  updated_at

FROM {{ ref('raw_products') }}

WHERE updated_at > CURRENT_DATE - INTERVAL '1 day'

You can then enforce schema constraints using dbt's schema.yml:

version: 2

models:

  - name: products

    columns:

      - name: title

        tests:

          - not_null

      - name: price

        tests:

          - not_null

          - numeric

      - name: updated_at

        tests:

          - freshness:

              warn_after: { count: 24, period: hour }

Together, tools like BeautifulSoup, Pandas, and dbt allow AI teams to take chaotic, low-signal web data and turn it into structured, validated, and ML-ready features. This not only boosts model accuracy but also keeps your pipelines compliant and interpretable — a must for regulated or production environments.

4. Storage and versioning

At the foundational level, most teams use cloud object storage like Amazon S3 or Google Cloud Storage (GCS). These services are inexpensive and highly durable. They are ideal for storing raw HTML, scraped JSON, or intermediate files from batch pipelines. They integrate well with Spark, dbt, and other transformation tools, especially when paired with metadata tracking.

For structured and analytics-ready data, data warehouses like BigQuery and Snowflake offer fast querying, support for large datasets, and easy integration with downstream ML tools like Vertex AI or SageMaker. Their ability to run SQL over terabytes of structured product data, news content, or log events makes them ideal for batch training jobs and exploratory analysis.

Some teams opt for Delta Lake, an open-source storage layer that brings ACID transactions, schema enforcement, and time travel to cloud object storage. This is especially valuable for AI workflows where training reproducibility is critical. It allows teams to recreate exactly the same dataset used for a past model checkpoint.

Beyond storage, data lineage and versioning are essential for maintaining trust in AI systems. Knowing where your data came from, when it was collected, and how it was transformed allows for transparency and compliance. It also allows teams to roll back models if corrupted data sneaks into a pipeline, or to debug accuracy issues tied to data drift.

5. Model interface and delivery layer

The final stage of the pipeline is where all the engineering effort pays off: getting the right data to the model at the right time, whether for training, fine-tuning, or real-time inference.

This delivery layer must be tightly coupled with your ML stack while remaining flexible enough to support different model types and deployment patterns. Some use cases, like real-time recommendation engines, demand high-frequency, low-latency data streams. Others rely on large, static batches for training foundation models or running A/B tests.

For batch workflows, data is typically exported in columnar formats like Parquet or Feather — both of which are optimized for speed, compression, and compatibility with ML libraries like PyTorch, TensorFlow, and XGBoost. These formats are especially useful when working with massive feature sets or time-series records across distributed compute environments.

In real-time systems, data is often served via low-latency APIs or streamed into a feature store like Feast or Tecton, which manage online/offline consistency. Feature stores act as the operational bridge between data engineering and ML, ensuring that the same features used for training are available instantly at inference time. This reduces skew and maintains model integrity.

Choosing the right interface depends on your use case:

• Batch training pipelines often rely on structured exports to object storage or warehouses

• Inference pipelines benefit from feature stores, Redis caches, or fast API lookups

• Online learning or reinforcement models may need direct data streaming via Kafka or Flink

Regardless of format, one principle holds: your data delivery must be modular, observable, and decoupled from the model logic. That way you can retrain faster, iterate safer, and scale without reinventing infrastructure every time a model changes.

Common challenges in pipeline engineering for AI (and how to solve them)

Even with the best tools and architecture, data pipelines for AI are rarely set-and-forget. The web is unpredictable, systems evolve, and model expectations shift over time. Below are some of the most common pain points AI teams face — and proven strategies to handle them.

Schema drift in web data

Web data is fragile. One day your scraper works; the next, a small HTML tweak or missing field causes silent failures. This is schema drift — when the structure of incoming data changes unexpectedly. For AI pipelines, it's especially risky. Models might train on corrupted data, or features might go missing entirely, with no clear trace until performance drops.

Use dynamic parsers and validation layers

Instead of assuming the data will stay the same, smart pipelines validate structure as they go. Tools like Pydantic let you define expected fields in code and catch errors as soon as they appear. If a product listing suddenly lacks a price, the pipeline knows — and logs it, skips it, or alerts you.

On the transformation side, dbt tests play a similar role. They enforce schema rules — like requiring all price fields to be numeric — before data reaches your models. It’s a layered defense: validate early with Pydantic, and catch downstream issues with dbt.

from pydantic import BaseModel

class Product(BaseModel):

    title: str

    price: float

This model makes sure your input has the fields your model expects. Anything missing or malformed is flagged immediately — no guesswork.

columns:

  - name: price

    tests:

      - not_null

      - numeric

Together, these tools keep your pipeline honest, even when the web isn't.

Slow ingestion due to anti-bot defenses

Modern websites are designed to keep scrapers out. From rate limiting and IP blocks to JavaScript rendering and CAPTCHA walls, anti-bot defenses can slow or completely halt data ingestion. This is especially true if you’re relying on basic scraping techniques. For AI pipelines that depend on fresh, high-volume data, these delays create bottlenecks that ripple across the entire system.

Integrate robust proxy solutions and headless browsers

To get around these barriers, AI teams are combining rotating proxies with headless browsers to mimic real users and access dynamic content reliably. 

For even greater efficiency, SOAX’s Web Data API automates much of this, handling CAPTCHAs, JavaScript execution, and retries under the hood. 

Our Web Data API is especially useful for high-volume ingestion pipelines where speed and reliability matter more than writing custom logic for every site.

With the right setup, your ingestion layer becomes as agile as the web is defensive — and your AI models get the timely data they need.

Maintaining data freshness and minimizing latency

In fast-moving AI applications — like real-time recommendations or market predictions — stale data can kill performance. If your model is making decisions based on hours-old inputs, you're not just lagging — you're losing. The challenge is syncing data frequently enough without overwhelming your systems or triggering cascading failures.

Schedule smart syncs with Airflow sensors or streaming frameworks

To stay fresh without going overboard, teams often rely on Airflow sensors to orchestrate smarter, dependency-aware syncing. TimeSensor makes sure a task runs after a specific time window, while ExternalTaskSensor waits for upstream processes (like ingestion or transformation) to finish before kicking off training or scoring.

Here’s a simple example:

from airflow.sensors.time_sensor import TimeSensor

from airflow.sensors.external_task_sensor import ExternalTaskSensor

wait_until_morning = TimeSensor(

    task_id='wait_for_5am',

    target_time=datetime.time(5, 0),

    dag=dag

)

wait_for_ingestion = ExternalTaskSensor(

    task_id='wait_for_data_ready',

    external_dag_id='web_scraper_dag',

    external_task_id='transform_data',

    timeout=600,

    dag=dag

)

This makes sure that downstream AI tasks only run once fresh data is guaranteed — without race conditions or guesswork. For ultra-low-latency use cases, some teams go further and adopt streaming architectures (Kafka, Flink) to update features in near real time.

Either way, freshness isn't a luxury — it's a baseline requirement for modern AI systems.

Debugging pipeline failures at scale

As pipelines grow more complex, pinpointing where something broke becomes a real challenge. 

A scraper times out. A schema test fails. A model starts underperforming. Without observability, these issues take hours (or days) to trace, and in production AI systems, that’s time you can’t afford.

Build observability into every layer of the pipeline

Reliable AI pipelines are observable pipelines. That means integrating tools that expose what’s happening, when, and why — from metadata lineage to data quality to system metrics.

Frameworks like OpenLineage help track dependencies and data flow across DAGs. This makes it easier to see how upstream changes affect downstream models.

More importantly, treat data workflows like code. Build them with tests, logging, and fail-fast mechanisms. If a transformation fails or a key column is missing, your system should tell you, not your model performance metrics two days later.

Observability turns reactive firefighting into proactive monitoring — a must-have when you’re scaling AI infrastructure across teams and workflows.

Resource scaling and cost

As AI data pipelines grow, so do compute costs. Ingesting terabytes of web data, running transformations on schedule, and storing large volumes of training-ready outputs can quickly become a financial burden. This is especially true if workloads aren't optimized or scale inefficiently with traffic spikes.

Use serverless compute and containerized jobs to scale efficiently

To keep costs predictable and performance scalable, leading AI teams are shifting to serverless and containerized infrastructure. With Google Cloud Functions or AWS Lambda, you can run lightweight scraping or validation tasks only when triggered. That way you only pay for the compute you use.

For heavier workloads like batch transformations or model-ready exports, Kubernetes offers flexible autoscaling of containerized jobs. Pipelines running in K8s can spin up multiple pods for concurrent scraping, then scale down to zero when idle — a powerful cost-saving pattern for intermittent data tasks.

Finally, using cloud-native storage formats like Parquet, ORC, or Delta Lake helps reduce I/O, compress storage costs, and accelerate downstream ML workflows, especially when paired with data warehouses or lakehouses.

Scaling AI pipelines isn’t just about speed. It’s about creating a cost-aware design that grows with your needs without ballooning your budget.

Design best practices for scalable AI data pipelines

Building a pipeline that works once is easy. Building one that scales, recovers gracefully, and stays maintainable over time — that’s where engineering discipline matters. As your AI infrastructure matures, these best practices help maintain reliability, traceability, and long-term velocity.

Separate ingestion, transformation, and delivery concerns

Tightly coupled pipelines are fragile and nearly impossible to debug at scale. That’s why you need to structure your pipeline into clear stages: ingestion (data acquisition), transformation (cleaning and normalization), and delivery (storage or model input). This creates modularity. 

Each stage should be a standalone service or task, capable of operating, retrying, and logging independently.

This separation is especially critical when working with high-cardinality web data and volatile schemas. 

For example, a Playwright-based SOAX scraper should output raw JSON to a cloud bucket, while a downstream dbt model or Pandas job processes and standardizes the structure. 

That way, if the website structure changes, you don’t have to rewrite transformation logic or break model input contracts.

Treat pipelines as code

AI pipelines should live in version-controlled repositories, be peer-reviewed, and deployed via CI/CD pipelines. Whether you’re orchestrating with Airflow or managing Spark jobs, define configurations and DAGs as declarative code, not GUI toggles or shell scripts.

If you’re running model training jobs downstream, keepe scraping scripts, transformation SQL, and even feature logic in Git. That way you have traceability between data state and model versioning. 

GitHub Actions or GitLab CI can automate tests and deployment. Reusable modules (e.g., a SOAX ingestion module or a dbt macro for deduplication) help with consistency across projects and teams.

Document lineage and data contracts

Without lineage, diagnosing model issues becomes a guessing game. Tools like dbt docs and OpenLineage offer visibility into the flow of data. They show exactly where a model’s training data came from, what transformations it passed through, and how it connects to other tables or models.

For AI workflows, lineage also supports reproducibility and compliance. When retraining a model six months later, you should be able to pinpoint the exact version of the ingestion script, schema, and transformation logic used. 

Implementing contract checks — such as schema definitions enforced via Pydantic or dbt tests — helps catch upstream changes that might otherwise cascade into invalid training data.

Design with testability from day one

Testable pipelines are resilient pipelines. Unit tests can cover individual transformation steps (e.g., parsing product titles, currency normalization), while integration tests validate full end-to-end flows with mock or staging data. Every data pipeline should have a local or sandbox environment for safe experimentation.

For AI-specific workflows, test inputs should mirror real data diversity — especially when preparing training data. 

This means testing your pipeline not just against happy paths, but edge cases like missing fields, invalid encodings, or outlier data ranges. Add assertions for row counts, null checks, and freshness windows so you can trust what flows into your models.

How SOAX fits into your AI data pipeline stack

SOAX isn’t a pipeline framework — and that’s by design. Instead, we solve the first, most critical part of every AI data pipeline: reliable access to high-quality, real-time, geo-targeted web data. Without clean input, even the best-engineered pipeline fails. SOAX makes sure your ingestion layer is resilient, scalable, and compliant — so the rest of your AI system can operate with confidence.

Our flexible proxy infrastructure supports residential, mobile, and ISP IPs — making it ideal for bypassing anti-bot protections without triggering rate limits. With >99% uptime and intelligent rotation strategies, it powers  consistent, large-scale scraping at speed.

For engineering teams using Airflow, SOAX plugs directly into Python ingestion tasks via proxy configs. In addition, we support Playwright, Scrapy, Selenium, and other scraping tools — making it easy to inject into headless browser workflows or CLI-based crawlers. 

For modern, JavaScript-heavy websites, the SOAX Web Data API handles dynamic rendering, CAPTCHA solving, and session management automatically. This removes the need for complex headless browser setups in many cases.

Get Better AI Today

From web scraping to transformation to delivery, every layer of the stack plays a role in ensuring that models learn from the right data, and continue performing as expected in production.

But scalability doesn't mean you have to reinvent the wheel. With the right combination of tools — Airflow for orchestration, dbt for transformation, and SOAX for web data access — AI teams can build pipelines that are both powerful and maintainable. 

If you're building AI systems that rely on external data, consider SOAX as your go-to source for real-time, geo-specific, and JavaScript-ready web data.