Feeding the Beast: Building Data Pipelines for an 8B Parameter Model

The Tiger Team

2024. The company decides to build its first large-scale internal language model. Not a toy. Not a proof of concept. An 8 billion parameter model that would run in production.

They assembled a tiger team: the best ML scientists, systems engineers, and infrastructure specialists they could find. My role? Build the data pipelines that would feed this beast.

Turns out, when you're training foundation models, data engineering is 80% of the problem.

The Data Challenge

Volume: It's Not Big Data, It's Enormous Data

Training an 8B parameter model requires... a lot of data.

The Math:

Training dataset: ~XXX billion tokens
Average token size: ~4 bytes
Raw text: ~XXX TB

But that's just the text. With metadata, lineage, quality scores, deduplication indices...
Actual storage: XXX TB+

Velocity: The Data Never Stops

This wasn't a one-time ingestion. The model would be continuously trained as new data became available.

Variety: Not All Data Is Created Equal

• Internal documentation systems
• Code repositories
• Customer interaction logs (properly anonymized)
• Technical specifications

Each source had its own:

• Format (JSON, XML, plaintext, structured logs)
• Access patterns (batch APIs, streaming, database exports)
• Quality characteristics
• Privacy requirements

Veracity: Garbage In, Garbage Model

The ML scientists kept saying: "The model is only as good as the data."

The data, unfortunately, was... messy.

Pipeline Architecture

The Three-Stage Pipeline

Stage 1: Ingestion & Standardization

Stage 2: Quality Filtering & Enrichment

Stage 3: Tokenization & Packaging

Data Sources → Ingestion → Quality Filter → Enrichment → Tokenization → Training Data Store
                   ↓            ↓             ↓              ↓
               Monitoring   Sampling      Validation    Verification

The Critical Resources Pipeline

My specific responsibility: build the pipeline for some critical internal resources.

The Challenges

1. Access Control and Privacy

These resources contained sensitive information. Not all of it could go into the training data.

2. Data Freshness vs Consistency

The resources were constantly updated. How do you maintain a consistent training corpus when the source data is always changing?

3. Scale and Throughput

4. Quality and Signal Density

Not all records were equally valuable for training. Some were gold. Some were noise.

Building the Pipeline

Ingestion Layer

# Simplified conceptual example
# TODO: Add architectural details

class ResourceIngestionPipeline:
    def __init__(self, source_config):
        # TODO: Describe initialization and config
        self.source = source_config
        self.rate_limiter = None  # Respect source system limits
        self.checkpoint_manager = None  # Resume capability

    async def ingest(self):
        """
        Stream data from source system
        """
        # TODO: Add implementation overview
        # - Pagination strategy
        # - Error handling and retries
        # - Backpressure management
        pass

    def transform(self, raw_data):
        """
        Normalize into standard format
        """
        # TODO: Describe transformation logic
        # - Schema mapping
        # - Type coercion
        # - Metadata extraction
        pass

Quality Filtering

The ML scientists provided heuristics, but we needed to apply them at scale.

Quality Dimensions:

• Length (too short? too long?)
• Language detection (English only? multilingual?)
• Toxicity (content filtering)
• Duplication (exact and near-duplicate detection)
• Signal density (information content vs boilerplate)

Deduplication at Scale

Training on duplicate data is wasteful and can bias the model.

Exact Deduplication:

• Hash-based approach

Near-Deduplication:

• MinHash / LSH approach

Tokenization Pipeline

Converting text into tokens the model can consume.

# Conceptual tokenization pipeline
# TODO: Add architectural details

class TokenizationPipeline:
    def __init__(self, tokenizer_model):
        self.tokenizer = tokenizer_model
        self.batch_size = 1000  # TODO: Discuss batch sizing

    def tokenize_batch(self, documents):
        """
        Convert text to token IDs
        """
        # TODO: Add implementation details
        # - Padding strategy
        # - Truncation handling
        # - Special token insertion
        pass

    def pack_sequences(self, tokenized_docs):
        """
        Pack multiple documents into fixed-length sequences
        """
        # TODO: Describe sequence packing for efficiency
        # - Minimizing padding
        # - Document boundary markers
        pass

Performance Optimization

Throughput Bottlenecks

Initial pipeline throughput: Target throughput:

Bottlenecks Identified:

Parallelization Strategy

Caching and Incremental Processing

Monitoring and Observability

Key Metrics

Pipeline Health:

• Ingestion rate (records/sec)
• Processing latency (end-to-end)
• Error rate by stage
• Backlog size

Data Quality:

• Records filtered per quality dimension
• Deduplication rate
• Token distribution statistics
• Source coverage

Alerting Strategy

The Integration Dance

The Feedback Loop

ML scientists would train on a data snapshot, evaluate model performance, and provide feedback:

"Can we get more data from source X?" "The quality filter is too aggressive on category Y." "We're seeing too many duplicates in domain Z."

Experiment Tracking

Challenges and Gotchas

1. The Moving Target

Requirements evolved as the team learned more about model behavior.

2. Privacy and Compliance

3. Data Lineage

When a model produces unexpected output, you need to trace it back to the training data.

4. Scale Testing

5. Cost Management

Processing terabytes of data is expensive.

What Worked

1. Modular Architecture

Each stage could be developed, tested, and optimized independently.

2. Incremental Processing

Don't reprocess everything on every run—track what's changed.

3. Quality Over Quantity

Better to have less high-quality data than more noisy data.

4. Strong Monitoring

You can't improve what you can't measure.

What Didn't Work

1. Over-Engineering Early

2. Underestimating Coordination Overhead

Working with a large team means lots of dependencies and communication.

3. Assuming Data Quality

The Results

• Pipeline throughput:
• Data processed:
• Pipeline uptime:
• Model training: Successfully fed training for

Lessons Learned

1. Data Engineering Is the Bottleneck

The model architecture gets all the attention, but data pipelines determine success.

2. Privacy Can't Be Bolted On

Design for privacy from day one. Retrofitting is painful.

3. Invest in Observability

You'll spend more time debugging pipelines than building them.

4. Test at Scale

Problems that don't appear with 1GB of data will definitely appear with 1TB.

5. Collaboration Is Key

The best technical solution doesn't matter if it doesn't meet the ML team's needs.

The Broader Context

Key Takeaways

• Data pipelines for foundation models are a unique challenge: scale, quality, and velocity all matter
• Privacy and compliance requirements must be designed in from the start
• Quality filtering is as important as volume—garbage in, garbage model
• Deduplication at scale requires specialized algorithms and infrastructure
• Monitoring and observability are critical for maintaining pipeline health
• Close collaboration between data engineers and ML scientists is essential
• The pipeline is never "done"—continuous improvement based on model performance feedback

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Details have been abstracted to protect proprietary information. All examples are simplified for illustration.