The Basic Tenets of Delta Lake: Enabling Reliable AI & ML on Data Lakes

Introduction

The rapid growth of data and the increasing adoption of AI and machine learning are driving a new focus on modernizing data architectures. Data lakes have emerged as the platform of choice for storing the massive volumes of data needed to train machine learning models and derive AI-driven insights. According to Mordor Intelligence, the data lake market is expected to reach $17.6 billion by 2026, representing a CAGR of 20.6%.

However, while data lakes offer unmatched storage scalability and flexibility for AI/ML workflows, they also come with significant challenges. Data reliability issues, inconsistent schemas, and the lack of transactional guarantees make it difficult to build and operate production machine learning pipelines. A study by NewVantage Partners found that 77% of businesses report adoption challenges with big data and AI initiatives, with data integrity being a top concern.

This is where Delta Lake shines. Developed at Databricks, Delta Lake is an open-source storage layer that brings data reliability and performance to data lakes. By leveraging ACID transactions, schema enforcement, and time travel capabilities, Delta Lake makes data lakes suitable for hosting the reliable, high-quality data needed for mission-critical AI and ML use cases.

In this post, we‘ll explore the basic tenets of Delta Lake through the lens of enabling AI and ML workloads. We‘ll dive into its key features, performance optimizations, and integration with popular AI/ML tools. Whether you‘re a data engineer building feature pipelines or a data scientist operationalizing models, understanding Delta Lake will be key to unlocking the full potential of your data lake for AI and ML.

Why Data Lakes Need Delta Lake for AI/ML

To understand the importance of Delta Lake for AI/ML workflows, it‘s helpful to look at the core limitations of data lakes that it addresses:

1. Lack of ACID Transactions – In a typical data lake, multiple producers and consumers access data without any transactional guarantees. This can lead to inconsistent and unreliable data, which is problematic for machine learning models that assume data validity. By implementing ACID transactions, Delta Lake ensures data consistency and enables concurrent reads and writes.

2. No Schema Enforcement – Data lakes allow storing unstructured and semi-structured data, but lack of schema enforcement can lead to data quality issues over time as pipelines evolve. Delta Lake provides schema validation on write and schema evolution, preventing bad data from being written and enabling changes to table schema.

3. Difficulty Reproducing Models – Data scientists need the ability to reproduce prior data versions to debug and audit models. Delta Lake‘s time travel capabilities and data versioning make it simple to query data as it existed at a specific point in time or revert tables to a previous state. This is essential for enabling experiment reproducibility.

4. Lack of Support for Streaming – Streaming data is increasingly important for real-time AI/ML applications, but data lakes typically can‘t handle streaming and batch data in the same table. Delta Lake supports both streaming writes and reads, enabling unified batch and streaming pipelines.

5. Poor Query Performance – While data lakes are great for storing large volumes of data, query performance can suffer due to the lack of indexing and partitioning. Delta Lake provides several performance optimization features such as data compaction, Z-Ordering, and data skipping that can dramatically accelerate reads.

By addressing these core challenges, Delta Lake is becoming a foundational component of the modern AI/ML platform. It enables organizations to build reliable and performant pipelines that make full use of their data lake, with capabilities that were previously only possible in proprietary data warehouses.

Delta Lake Architecture for AI/ML

The key to Delta Lake‘s capabilities lies in its unique architecture. Let‘s examine how the different components of Delta Lake work together to enable reliable AI/ML pipelines.

At its core, Delta Lake consists of:

1. Delta Tables – Data is stored in Apache Parquet format and organized into Delta tables. Tables are defined by a transaction log that tracks all changes and a set of data files that contain the actual data.

2. Delta Transaction Log – An ordered record of all the commits made to a table. The transaction log is stored as JSON files in the _delta_log subdirectory and used to construct a snapshot of the table at any point in time.

3. Delta Snapshots – A snapshot is a versioned view of a Delta table at a specific point in time. It‘s constructed by applying a set of commits from the transaction log to the base data files. Readers query tables using the latest snapshot by default but can also time travel to query historical versions.

This architecture offers several key benefits for AI/ML workloads:

• Versioned datasets for experiment tracking and reproducibility. Each write to a Delta table creates a new version that can be referenced and reproduced later, making it simple to track data lineage and debug models.

• Integration with feature stores for real-time serving. Feature data can be stored in Delta tables and accessed with low latency for real-time inferencing using Streaming queries. Delta Lake provides intra-row and inter-row statistics that can be used by feature stores to accelerate online feature serving.

• Support for large-scale batch scoring and inference. Delta Lake‘s ability to handle petabyte-scale tables with billions of partitions and files makes it well-suited for distributed batch scoring use cases. Tables can be efficiently read by Spark jobs to generate insights and predictions.

• Unified storage layer for feature engineering. Both streaming and batch data can be landed in Delta tables, enabling feature engineering pipelines that combine real-time and historical data. Delta Lake can capture changes to data over time (e.g. SCD Type-2 changes) which are important for many feature engineering use cases.

By leveraging this architecture, data teams can build end-to-end AI/ML pipelines with data reliability and performance guarantees that were previously not possible with data lakes. This reduces time spent on data wrangling and debugging and enables data scientists to focus on building high quality models.

Delta Lake Performance Optimizations for AI/ML

In addition to data reliability, performance is critical for AI/ML use cases. Training machine learning models and generating real-time predictions often requires querying large volumes of data with low latency. Poor query performance can lead to longer training times and missed SLAs.

Delta Lake implements several key optimizations to accelerate query performance on data lakes:

• Data Compaction – Small files are automatically compacted into larger ones, which reduces the overhead of opening and closing files. Databricks has observed data compaction accelerating queries by up to 3x.

• Z-Ordering – Z-ordering is a technique for co-locating related data in the same set of files. By clustering data by particular columns, Delta Lake can dramatically reduce the amount of data that needs to be read.

• Data Skipping – Delta Lake‘s data skipping feature uses file statistics to avoid reading irrelevant data files, thereby accelerating queries. Databricks has seen data skipping improve query performance by up to 30x.

• Caching – Delta Lake supports caching of tables to accelerate reads. Frequently accessed tables can be cached in memory or on SSDs, reducing read latencies and improving overall cluster throughput.

These optimizations are especially impactful for AI/ML use cases:

• Faster feature engineering – With these optimizations, data scientists can more quickly transform and aggregate large feature sets to train models. Queries that used to take hours or days can be completed in minutes.

• Real-time feature serving – By co-locating feature data and caching frequently accessed features, Delta Lake can support extremely low latency access patterns needed for real-time inferencing and feature serving.

• Improved model training throughput – Faster data access allows for more efficient utilization of GPU clusters, leading to improved model training throughput. Data scientists can train more models in less time.

• Interactive data exploration – Data skipping and caching make it possible to interactively query large datasets in tools like Jupyter notebooks. This accelerates the process of prototyping models and evaluating results.

Delta Lake Integration with AI/ML Tools

To realize the full value of Delta Lake for AI/ML workflows, it‘s critical to integrate with popular tools and frameworks in the ecosystem. Thankfully, Delta Lake has a vibrant community and strong support from vendors like Databricks. Let‘s look at some of the key integrations.

• Apache Spark – Delta Lake is deeply integrated with Apache Spark and leverages Spark SQL for executing queries on Delta tables. This allows data scientists to interact with data using familiar DataFrame/Dataset APIs and SQL.

• MLflow – MLflow is an open-source platform for managing the ML lifecycle, including tracking experiments, packaging code into reproducible runs, and deploying models. Delta Lake integrates closely with MLflow to track experiment results and data versions used for training.

• Feature Stores – Several open-source and commercial feature store technologies support Delta Lake as an offline and online storage backend. For example, Feast can source feature data from Delta tables and materialize features into an online store for low-latency serving. Hopsworks also integrates with Delta Lake to version and store training datasets.

• SageMaker – Amazon SageMaker, a fully-managed ML platform, recently added support for Delta Lake. Users can now connect SageMaker Studio to Delta Lake tables in S3 for building and deploying models.

• Databricks – Databricks provides a comprehensive platform for data engineering and data science workloads and was the original creator of Delta Lake. Their offering includes managed MLflow, collaborative notebooks, and distributed model training and inference on Spark clusters.

Getting Started with Delta Lake

If you‘re excited to start using Delta Lake in your own AI/ML projects, there are several ways to get started:

• Delta Lake Open Source Project – Delta Lake is an open-source project and the core functionality can be leveraged for free. To use Delta Lake with an existing Spark cluster, check out the quick start guide.

• Databricks Community Edition – The easiest way to learn Delta Lake is using the free Databricks Community Edition. Community Edition provides free access to a mini-cluster along with notebooks and tutorials. Sign up here.

• Managed Platforms – If you prefer to use a fully-managed platform, Databricks offers pay-as-you-go pricing in AWS and Azure. For on-premises deployments, Cloudera Machine Learning supports Delta Lake as well.

Regardless of how you use it, Delta Lake is quickly becoming a key technology for organizations looking to scale AI and ML initiatives on their data lakes. By starting small and establishing patterns and practices around Delta Lake, data teams can set the stage for future ML projects to be successful.

Conclusion

As data lakes become the default repository for ML initiatives, Delta Lake is playing an increasingly pivotal role in enabling organizations to derive value from their data. By addressing many of the core data reliability and performance challenges associated with data lakes, Delta Lake is helping data scientists and ML engineers be more productive and deliver more accurate models.

Some of the key advantages of Delta Lake for AI/ML workloads include:

• Enabling reproducible models through data versioning and time travel
• Providing a reliable data layer for training models and serving features
• Accelerating queries and data processing through performance optimizations
• Integrating with popular AI/ML tools and frameworks

With an active open-source community and strong vendor support, Delta Lake is poised to become a foundational component of the modern data stack. As AI/ML initiatives become increasingly important, Delta Lake will be key to ensuring these projects can scale reliably.

To learn more about Delta Lake and how it‘s being used for AI and ML, check out the following resources:

• Delta Lake: The Definitive Guide (new in 2023)
• Delta Lake: Improving the Quality of Machine Learning Pipeline Data (Kevin Mennel – Stanford MLSys Seminar)
• Delta Lake: The Foundation for Modern ML Platforms (Reynold Xin & Josh Smith – Data+AI Summit)

With this knowledge, I hope you feel empowered to start leveraging Delta Lake in your own AI/ML projects. The combination of data reliability and performance that Delta Lake enables will be essential for scaling ML initiatives and driving ROI with your data lake investments.