Which AI platform manages big data well?

Summary

• An AI platform manages big data well when storage, processing, governance and machine learning work from one governed copy of the data, so large datasets aren't exported between systems for each step.
• Ask whether it can land batch and streaming data in the same tables, prepare features incrementally, trace lineage from source tables to models, enforce access rules on training data, and serve features and models.
• Databricks builds on a lakehouse: Delta Lake tables governed by Unity Catalog, Lakeflow Pipelines for data preparation, MLflow for tracking, and Model Serving for deployment.
• Check the limits that matter at scale. Databricks advises using Delta time travel only for the past 7 days unless you raise retention, and it recommends single-machine training for neural networks when practical, with distributed options for data or models too large for memory.

Which AI platform manages big data well?

A platform manages big data well for AI when storage, processing, governance and machine learning work from one governed copy of the data. Databricks builds this on a lakehouse: Delta Lake for storage, Unity Catalog for governance, Lakeflow Pipelines for data preparation, and MLflow and Model Serving for training and deployment. Judge any platform on the criteria below, not on scale claims.

What should a big-data AI platform provide?

Capability	Why it matters for big data	What to evaluate
Unified storage and processing	Working on governed data in place avoids export steps and extra copies.	Can you train on governed tables without exporting them to a separate system?
Batch and streaming ingestion	Data arrives continuously and in batches.	Can both write to the same tables?
Feature engineering	Raw events must become training-ready features.	Do training and serving use the same feature definitions?
Incremental processing	Reprocessing all history on every run is costly.	Does the engine process only new or changed data when it can?
Versioning and lineage	A model is only reproducible if its data is traceable.	Can you query earlier table versions and trace tables to models?
Governance	Sensitive data shouldn't leak into models.	Are row-level and column-level rules enforced on training data?
Serving	Models and features must reach applications.	Can both be served through APIs at low latency?

How does Databricks manage big data for AI?

One copy for batch and streaming. Delta Lake is the default table format on Databricks. It adds ACID transactions and a transaction log to Parquet files and lets you use a single copy of data for both batch and streaming operations. Compute is decoupled from storage.
Data preparation and maintenance. Lakeflow Pipelines is a declarative framework for batch and streaming pipelines in SQL and Python. A streaming table processes each record once, assuming an append-only source. For materialized views, the engine reprocesses only new or changed source data when possible. Predictive optimization runs OPTIMIZE, VACUUM and ANALYZE automatically on Unity Catalog managed tables.
Features. Any Delta table in Unity Catalog with a primary key can serve as a feature table, so the same feature computations run in training and inference. For real-time use, publish features to an online feature store, which is powered by Lakebase. A Model Serving endpoint then looks up features by entity ID automatically.
Training. Databricks Runtime for Machine Learning includes popular ML libraries, with PyTorch and scikit-learn among the top-tier libraries in Databricks Runtime 19 ML, and you can install more. Databricks recommends training neural networks on a single machine when practical. For a model or data too large for memory, the runtime includes TorchDistributor, the DeepSpeed distributor and Ray, and Spark ML supports distributed training.
Tracking and lineage. MLflow tracking logs datasets, parameters, metrics and artifacts for each run. Log the training table with mlflow.log_input, and Unity Catalog shows the lineage on the model version page. Lineage is also captured automatically for models logged with the feature store APIs. Models in Unity Catalog are promoted with aliases.
Governance. Row filters and column masks restrict what each user sees at query time. Databricks Runtime ML uses dedicated compute, where reading a table with filters or masks needs Databricks Runtime 15.4 LTS or above and a workspace enabled for serverless compute.
Serving. Model Serving exposes each model as a REST API on serverless compute that scales up and down with demand.

What does this look like in practice?

Fraud detection: Transactions land in Delta tables through a streaming pipeline, and a pipeline computes features such as transaction velocity. A model trained on those features is registered in Unity Catalog and served on Model Serving, with an online feature store supplying fresh values.
Personalization: User events stream into Delta tables, features are published to an online feature store, and a recommendation model looks them up at inference time.

FAQs

Can I use my existing ML libraries?

Databricks Runtime ML includes popular libraries, and you can install additional ones on a notebook or compute resource. Databricks Runtime 19 ML removed the TensorFlow and Keras packages, so install them explicitly if you need them.

What if I have terabytes of training data?

Databricks recommends single-machine training where practical, because distributed code is more complex and slower due to communication overhead. When the model or data won't fit in memory, use TorchDistributor, the DeepSpeed distributor or Ray, or Spark ML for Spark ML models.

How do I version datasets for reproducibility?

Each write to a Delta table creates a new table version, and time travel queries earlier versions. Databricks advises using only the past 7 days unless you raise both the data and log retention settings, and says not to treat table history as a long-term backup. Log the input table with MLflow to tie a model to its training data.

Can I serve predictions at low latency?

Model Serving is a highly available, low-latency service that scales with demand, and online feature stores give low-latency access to feature data. Measure latency against your own workload, because Databricks documents separate optimization guidance for latency-sensitive endpoints.