Organizing Data Types for Efficient ML Pipelines

Selecting and implementing storage and serialization formats means placing each data type where distributed trainers and serving systems can load it fastest. BigQuery (opens in a new tab) is the store for structured and semi-structured tabular data, while Cloud Storage (opens in a new tab) is the store for image, video, audio, and other unstructured data in large sharded container files. Serialization follows the training framework, with sharded TFRecord files for TensorFlow and Avro files for other frameworks.

1.1Separate Training Copies from Operational Sources

Separate training copies are ML-optimized extracts of operational data stored apart from production systems. Source data flows from online service logs, on-device images, or web-scraped documents into a dedicated training store with a different schema, file layout, and access pattern. The training store uses columnar tables for tabular data and large binary containers for unstructured data. This separation prevents experimentation and repeated training reads from contending with production writes and latency requirements.

1.2Store Tabular Data in BigQuery with Materialized Tables

BigQuery is the recommended store for all unprocessed structured and semi-structured data, and in most cases for intermediate processed data as well. BigQuery training data is stored as materialized tables instead of views or subqueries, because materialization avoids recomputing logic on every read. Large tables use partitioning to improve query performance and cost efficiency by scanning only relevant partitions. Data flows from BigQuery tables to training workers through the BigQuery Storage API, which provides high-throughput parallel reads.

The read path from BigQuery Storage API to training depends on the processing framework:

For artifact tracking and lineage, training pipelines can reference the data as a managed tabular dataset in Vertex AI.

1.3Store Unstructured Data in Cloud Storage with Sharded Containers

Cloud Storage is the store for image, video, audio, and unstructured data packaged for high sequential throughput. Dataflow can convert raw unstructured objects into binary container formats such as TFRecord, which improves ingestion performance because trainers read fewer, larger sequential files. Throughput improves when many small images, videos, or audio clips are combined into large files, with buckets and directories grouping shards for management and parallel discovery. Cloud Storage with BigLake can present lake data for governed analytics, while BigQuery object tables can reference Cloud Storage objects without copying bytes and signed URLs can grant time-limited access.

1.4Serve Reusable Features from Vertex AI Feature Store

Vertex AI Feature Store is the central location to create, maintain, share, and serve machine learning features for structured data. Feature data itself lives in a BigQuery table or view, which lets BigQuery store all feature data including historical values for offline training and point-in-time correctness. To use Vertex AI Feature Store, first create an online store instance and then define feature views. The service then depends on BigQuery for offline history and on the online store for low-latency serving. Because features are defined once and reused across projects, teams avoid redundant data engineering and reduce infrastructure cost.

Optimizing data access patterns for training and serving means matching each read pattern to the storage, format, and API that removes its I/O bottleneck. BigQuery (opens in a new tab) serves structured and semi-structured training reads, Cloud Storage serves image, video, audio, and other unstructured training reads, and Agent Platform Feature Store (opens in a new tab) serves shared features for low-latency online prediction. Throughput and latency depend on materialized tables, partitioned queries, sharded container files, high-throughput read APIs, and a separate online feature layer.

2.1Isolate Training Storage to Avoid Production Contention

Training storage isolation is the practice of extracting data from operational systems into separate ML-optimized storage before experimentation or training. Source data flows from service logs, device images, or scraped documents into a dedicated training store with bulk-scan layouts. The training store does not share access patterns with the operational source, so training jobs do not contend with live traffic. This separation lets each downstream reader use the file layout and API that minimizes its own bottleneck.

2.2Accelerate Tabular Reads Through the BigQuery Storage API

BigQuery is the recommended store for structured and semi-structured data when the goal is fast training access for distributed workers. Data flows from BigQuery tables through the BigQuery Storage API to Vertex AI Training workers, and lineage flows through a managed tabular dataset when tracking matters. For maximum speed, training data must be stored as materialized tables instead of views or subqueries, and large datasets should use partitioning to reduce scanned bytes and cost. Materialized and partitioned tables let many parallel workers pull splits without waiting on query re-execution.

Choose the connector that matches the training framework so data flows directly from BigQuery storage into the input pipeline:

2.3Scale Unstructured Reads with Large Balanced Shards

Cloud Storage is the store for unstructured training data when many workers need high parallel read and write throughput. Data flows from many small media objects into large container files in Cloud Storage, then from those containers to distributed trainers. TensorFlow training uses sharded TFRecord files, while all other frameworks use Avro files, so the container matches the framework decoder. Because each storage request carries overhead, fewer large objects sustain sequential throughput while many small objects stall on per-object latency.

To keep distributed reads balanced, apply three sharding rules:

• Store unstructured data in large container files using sharded TFRecord for TensorFlow and Avro for other frameworks.
• Size each shard to at least 100 MB and keep total shard count between 100 and 10,000.
• Group shards with Cloud Storage buckets and directories for data management and parallel discovery.

When shards drop below 100 MB or exceed 10,000 shards, workers spend more time opening objects than reading bytes and throughput falls.

2.4Serve Low-Latency Predictions from the Online Feature Layer

Agent Platform Feature Store is the central layer for creating, maintaining, sharing, and serving features for offline experimentation and online prediction. Feature data lives in a BigQuery table or view that retains historical values for training and analysis. Serving depends on an online store instance plus defined feature views, where each feature view defines which features flow from BigQuery to the low-latency online copy. A request for online prediction therefore flows from the endpoint to the online store instance instead of scanning BigQuery. Choose Agent Platform Feature Store when tabular features must be reused across teams and served consistently in training and production.

2.5Align Preprocessing Scale with the Training Read Path

Preprocessing alignment means choosing the transformation engine whose output preserves the optimized access pattern for training. The options differ by data type, volume, and existing code:

• BigQuery transformations handle structured and semi-structured preprocessing, including built-in preprocessing for BigQuery ML and AutoML, and are often the most cost-effective path for custom models.
• Dataflow, which uses the Apache Beam programming model, handles large-volume batch and streaming conversion of unstructured data into binary formats such as TFRecord.
• Managed Service for Apache Spark handles serverless Spark processing when an organization already has Spark code and skills.
• One-off Python scripts fit smaller datasets that fit into memory.

After preprocessing, a managed Vertex AI dataset links data to custom-trained models and provides descriptive statistics with automatic or manual splitting into train, test, and validation sets. Training scale follows data size and schedule, where a Vertex AI Workbench instance fits small datasets or subsets and the managed training service fits larger datasets, distributed training, and production retraining on arrival of new data.

Designing scalable data exploration and feature engineering pipelines means automating profiling, transformation, labeling, and versioning for tabular, text, and image data. Raw data flows from logs, devices, or web scrapes into BigQuery (opens in a new tab) for structured data and into Cloud Storage for unstructured data, then through Dataflow, Dataproc, or BigQuery transforms into training-ready datasets and features. Managed datasets, feature views, and lineage tracking connect those outputs across experimentation, training, and serving.

3.1Organize Structured and Unstructured Data for Efficient Access

Organized storage separates data by type so exploration and training read at high throughput without touching operational systems. BigQuery holds structured and semi-structured data, including intermediate processed data, following BigQuery project-structure recommendations. Cloud Storage holds image, video, audio, and other unstructured data in large container formats grouped by buckets and directories. The ML pipeline depends on that separation for reproducibility and to avoid production contention.

For maximum training speed, BigQuery pipelines store materialized tables instead of serving training from views or subqueries, and large tables use partitioning to improve performance and cost. Reads use the BigQuery Storage API, with artifact tracking when data is registered as a managed tabular dataset. Unstructured data uses sharded TFRecord files for TensorFlow or Avro files for other frameworks, combining many small objects into large files to improve Cloud Storage throughput.

3.2Profile and Transform Data with BigQuery, Dataflow, and Dataproc

Large-scale profiling and feature extraction use different engines depending on data type, volume, and existing code, with outputs feeding managed datasets and feature stores. BigQuery processes structured tables with built-in transformations, Dataflow processes batch and streaming data with Apache Beam, and Managed Service for Apache Spark on Dataproc processes data with serverless Spark. Dataflow converts unstructured data into binary formats such as TFRecord to improve ingestion, and Dataflow combined with Eventarc supports event-driven ingestion. Knowledge Catalog and BigQuery data profiles, quality checks, and anomaly detection then validate formats, ranges, and distributions before training.

The three engines compose in one lifecycle where BigQuery stores and profiles tabular data, Dataflow ingests and converts streaming and unstructured inputs, and Dataproc runs Spark feature jobs:

BigQuery transformations are often the most cost-effective path when data is already in BigQuery, while Dataflow plus pandas handles transformations not expressible in SQL. One-off Python scripts fit smaller datasets that fit into memory.

3.3Automate Labeling for Text and Images and Centralize Features

Automated labeling turns raw unstructured data into structured training data, while feature engineering centralizes reusable structured features for serving. Vertex AI data labeling jobs run from the Agent Platform page in the Google Cloud console to create and import training data, adding informative tags that reduce ambiguity. For text, Gemini prompt and tuning capabilities manage classification, entity extraction, and sentiment analysis, while marketplace solutions such as Labelbox (opens in a new tab) and Snorkel Flow provide assisted labeling, review, and workforce workflows. Vertex AI Feature Store creates, maintains, shares, and serves features from a central location, where feature data lives in a BigQuery table or view and use requires first creating an online store instance and then defining feature views.

Label-driven iteration for vision, NLP, and generative AI follows an ordered active-learning loop across Cloud Storage, BigQuery, Labelbox, and Agent Platform:

1. Store: Unstructured objects land in Cloud Storage buckets and directories, with associated metadata stored in a BigQuery table.
2. Connect and explore: Labelbox connects via Identity and Access Management (IAM) delegated access for Cloud Storage and the BigQuery Connector, then Catalog provides visual search and organization.
3. Annotate: Annotate creates ground truth with model-assisted labeling, enterprise review, and workforce management, optionally using Agent Platform models for pre-labels.
4. Train and diagnose: Agent Platform trains models on the prepared data, and Labelbox Model filters each dataset version by metrics or metadata to identify errors.
5. Target next data: Catalog finds similar unlabeled or mislabeled data to build the refined next iteration of the training dataset.

3.4Preserve Reproducibility with Datasets, Lineage, and Versioning

Reproducibility links every model to the exact data, features, code, and parameters that produced it. Managed Vertex AI Datasets create a clear link between pre-processed data and custom-trained models, provide descriptive statistics, and support automatic or manual splitting into train, test, and validation sets. Managed datasets are optional when custom splitting logic in training code is preferred or when lineage is not critical. Vertex ML Metadata logs data sources, transformations, parameters, metrics, and dataset and model artifacts, while Vertex AI Pipelines lineage tracks each pipeline artifact for debugging. Model Registry applies and manages versions for each stored model, and Vertex AI TensorBoard and Experiments visualize runs by hyperparameters, loss, accuracy, model graph, and embeddings. For reuse, Feature Store promotes tabular features through BigQuery, unstructured data is governed with object tables and signed URLs, vector embeddings carry metadata in Vector Search indexes, and Knowledge Catalog plus BigQuery sharing publish discoverable data products with lineage and quality checks.

Key Facts

• BigQuery is the store for structured and semi-structured training data as materialized and partitioned tables, while Cloud Storage is the store for image, video, audio, and unstructured data in large sharded containers.
• TensorFlow training uses sharded TFRecord files and all other frameworks use Avro files, with unstructured shards sized to at least 100 MB and 100 to 10,000 shards per dataset.
• Data flows from BigQuery tables to trainers through the BigQuery Storage API using a framework-specific connector such as the tf.data.dataset reader for BigQuery, BigQuery client for TFX, or BigQuery I/O Connector for Dataflow.
• Vertex AI Feature Store and Agent Platform Feature Store keep feature history in BigQuery and serve current features from an online store instance defined by feature views for low-latency online prediction.
• Reproducibility depends on managed Vertex AI Datasets, Vertex ML Metadata, Vertex AI Pipelines lineage, Model Registry versions, and TensorBoard and Experiments comparisons.