Evolving Data Lake Architectures: A Comprehensive Analysis of Apache Hudi and Beyond

Abstract

Data lakes have become a cornerstone of modern data architectures, providing a centralized repository for diverse data types. However, the initial promise of cost-effective storage and schema-on-read capabilities often clashes with the demands of real-time analytics and compliance requirements necessitating frequent data updates and deletions. This report presents a comprehensive analysis of Apache Hudi as a prominent solution for addressing these challenges, delving into its core functionalities, advanced features, performance characteristics, and comparative advantages. Beyond a Hudi-centric view, the report broadens the scope to examine the broader landscape of data lake update frameworks and emerging trends shaping the future of data lake architectures. We explore topics such as multi-modal data lakes, lakehouse architectures, and the role of AI/ML in data lake management, offering insights into evolving best practices and future research directions.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

1. Introduction

The proliferation of data has led to the widespread adoption of data lakes as a cost-effective means of storing and processing vast amounts of information from diverse sources. Data lakes typically leverage object storage systems like Amazon S3 or Azure Blob Storage, allowing organizations to ingest data in its raw format without the need for upfront schema definition. This flexibility, however, comes at a cost. Traditional data lake architectures often struggle with several key challenges:

• Data Mutation: Updating or deleting specific records within a data lake can be computationally expensive and inefficient, often requiring rewriting entire partitions. This limitation hinders the ability to comply with data privacy regulations such as GDPR and CCPA, which mandate the right to be forgotten.
• Data Quality: Without proper data governance mechanisms, data lakes can quickly become “data swamps” filled with stale, inaccurate, or inconsistent information. This degrades the value of the data and undermines the effectiveness of analytical applications.
• Real-Time Analytics: Traditional batch-oriented processing methods are often insufficient for applications that require low-latency access to up-to-date data. The lack of transactional guarantees further complicates real-time data processing.

To address these challenges, a new generation of data lake technologies has emerged, offering features such as ACID transactions, incremental processing, and data versioning. Apache Hudi is a prominent example of such a technology, enabling efficient data updates and deletes within data lakes. This report provides a detailed examination of Hudi, its capabilities, and its role in evolving data lake architectures. However, it goes beyond a simple overview of Hudi to explore the broader context of modern data lake technologies and emerging trends. We delve into concepts such as data lakehouses, multi-modal data lakes, and the application of AI/ML to data lake management, aiming to provide a comprehensive understanding of the current state and future direction of data lake technologies.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

2. Apache Hudi: A Deep Dive

Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source data lake platform that brings database and data warehouse capabilities to data lakes. It achieves this by introducing a data layout and metadata management layer on top of existing data storage systems. Hudi enables efficient upserts, deletes, and incremental data ingestion, addressing the core limitations of traditional data lake architectures.

2.1 Key Concepts and Architecture

Hudi introduces several key concepts that are fundamental to its operation:

• Table Types: Hudi supports two main table types:
  • Copy on Write (CoW): When a record is updated, the entire partition containing the record is rewritten. This is a simpler approach but can be less efficient for frequent updates, particularly for large partitions.
  • Merge on Read (MoR): Updates are written to delta logs (also known as log files), and the base files are only updated during compaction. This approach offers faster write performance but requires merging base files and delta logs at read time, potentially increasing read latency.
• Index: Hudi uses an index to map incoming records to their corresponding locations within the data lake. This allows for efficient identification of records to be updated or deleted. Supported index types include:
  • Bloom Filter Index: A probabilistic data structure that provides a fast way to determine if a record is present in a file. This index is suitable for read-heavy workloads.
  • Simple Index: A simple key-value index that maps record keys to file paths and record locations.
  • HBase Index: Leverages HBase as an external index store.
  • Bucket Index: Divides the table into buckets based on a bucket key, improving performance for equality lookups.
• Compaction: MoR tables rely on compaction to merge delta logs into base files. This process is essential for maintaining read performance and reducing storage costs.
• Cleaning: Hudi automatically cleans up older versions of data files, ensuring that the data lake does not accumulate excessive historical data.
• Timeline: Hudi maintains a timeline of all actions performed on the table, such as commits, compactions, and cleans. This timeline provides a complete audit trail of data changes.

The Hudi architecture consists of the following main components:

• Hudi Clients: Applications that interact with Hudi tables, such as Spark, Flink, and Presto.
• Hudi Writers: Components that write data to Hudi tables. These writers are responsible for managing the index, writing data to base files or delta logs, and updating the timeline.
• Hudi Readers: Components that read data from Hudi tables. These readers are responsible for merging base files and delta logs (in the case of MoR tables) and filtering data based on the Hudi timeline.
• Metadata Table: Since v0.14.0, Hudi provides a Metadata Table (MT) to accelerate queries by managing table metadata. It replaces filesystem-based operations with more efficient key-value lookups. It maintains file listings, schema, locations and statistics.

2.2 Advanced Features and Configuration

Hudi offers a range of advanced features and configuration options that allow users to optimize performance and tailor the platform to specific use cases:

• Clustering: Hudi supports clustering, which reorganizes data within a table to improve query performance. Clustering can be based on various criteria, such as partition keys, z-order curves, or Hilbert curves.
• Data Skipping: Hudi supports data skipping techniques such as bloom filters and min-max indexes to reduce the amount of data that needs to be scanned during query execution.
• Record Level Indexing In addition to file level bloom filters, Hudi supports record level indexing within a parquet file, which has several advantages, particularly in smaller files.
• Schema Evolution: Hudi supports schema evolution, allowing users to add, remove, or modify columns in a table over time. Schema evolution can be performed using a variety of strategies, such as adding new columns with default values or renaming existing columns.
• Partitioning: Hudi supports partitioning, which divides a table into smaller partitions based on the values of one or more partition keys. Partitioning can significantly improve query performance by allowing users to filter data based on partition keys.
• Custom Compaction Strategies: While Hudi provides default compaction strategies, users can implement custom strategies to optimize compaction performance for specific workloads. For example, a custom strategy could prioritize compaction of partitions that are frequently queried or that contain a high volume of delta logs.
• Pre-Combining: Hudi enables pre-combining records before writing to storage. This involves selecting the most recent or relevant record among duplicates based on a defined strategy (e.g., timestamp). This is particularly useful for change data capture (CDC) scenarios.

2.3 Performance Benchmarks and Optimization

The performance of Hudi depends on several factors, including the table type (CoW or MoR), the index type, the compaction strategy, and the query engine. Several research papers and blog posts have presented performance benchmarks for Hudi under various workloads. Generally, MoR tables offer better write performance than CoW tables, while CoW tables offer better read performance. However, the optimal choice depends on the specific application requirements.

Optimizing Hudi performance requires careful tuning of several parameters. Some key optimization techniques include:

• Right Sizing Partitions: Choosing appropriate partition sizes can significantly impact performance. Too many small partitions can lead to excessive metadata overhead, while too few large partitions can result in inefficient data scanning.
• Configuring Compaction: Tuning the compaction frequency, the number of delta logs to merge, and the resources allocated to compaction can significantly impact the performance of MoR tables. Choosing the correct compaction strategy (e.g., small file handling) can also be beneficial.
• Optimizing Indexing: Selecting the appropriate index type and configuring the index parameters (e.g., bloom filter size) can improve query performance.
• Leveraging Query Engine Optimizations: Many query engines, such as Spark and Presto, offer optimizations specifically for Hudi tables. These optimizations can include predicate pushdown, data skipping, and vectorized processing.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

3. Comparative Analysis: Hudi vs. Delta Lake vs. Iceberg

Apache Hudi, Delta Lake, and Apache Iceberg are the three leading open-source data lake update frameworks. Each framework offers similar capabilities, such as ACID transactions, incremental processing, and data versioning, but they differ in their architecture, features, and performance characteristics.

| Feature | Apache Hudi | Delta Lake | Apache Iceberg |
| —————— | ———————————– | ——————————— | ———————————– |
| Table Format | Hudi format | Delta Lake format | Iceberg format |
| Update/Delete | Yes | Yes | Yes |
| ACID Transactions | Yes | Yes | Yes |
| Time Travel | Yes | Yes | Yes |
| Data Skipping | Bloom filters, custom indexes | Min/Max indexes, Z-Order indexing | Bloom filters, partition pruning |
| Schema Evolution | Yes | Yes | Yes |
| Streaming Ingestion| Yes | Yes | Yes |
| Incremental Reads | Yes | Yes | Yes |
| Metadata Management| Internal timeline, metadata table | Delta Log | Catalog integration (e.g., Hive) |
| Ecosystem | Hadoop, Spark, Flink, Presto, Trino | Spark, Databricks | Spark, Flink, Presto, Trino, Hive |
| Clustering | Yes | Yes | Yes |

Key Differences:

• Table Format: Each framework uses its own proprietary table format to store metadata and data files. This means that tables created in one format cannot be directly read by other frameworks without conversion.
• Metadata Management: Hudi uses a timeline-based approach to manage metadata, while Delta Lake relies on the Delta Log, and Iceberg integrates with a catalog service such as Hive Metastore or AWS Glue Data Catalog.
• Ecosystem: Delta Lake is tightly integrated with Databricks and Spark, while Hudi and Iceberg have broader support for other query engines such as Presto, Trino, and Flink.
• Data Skipping: While all frameworks support data skipping, the mechanisms and configuration vary. Hudi’s custom index support offers flexibility, while Delta Lake’s Z-Order indexing is particularly useful for multi-dimensional data. Iceberg relies heavily on partition pruning which depends on effective partitioning schemes.

When to Choose Which Framework:

• Apache Hudi: Hudi is a good choice for organizations that need to perform frequent updates and deletes on large datasets and that require a high degree of flexibility in terms of query engines and processing frameworks. Hudi’s support for custom indexing and compaction strategies makes it well-suited for complex workloads. Its maturity and wide adoption in the Hadoop ecosystem are also advantages.
• Delta Lake: Delta Lake is a strong option for organizations that primarily use Databricks and Spark and that require a fully managed data lake solution. Databricks provides tight integration with Delta Lake and offers a range of features for data governance, security, and performance optimization. The simplicity and ease of use of Delta Lake are appealing for many users.
• Apache Iceberg: Iceberg is a good choice for organizations that require a highly scalable and reliable data lake solution that is well-integrated with a variety of query engines and catalog services. Iceberg’s focus on metadata management and its support for schema evolution make it well-suited for evolving data lake architectures. Its flexibility and open standards approach are attractive to organizations seeking vendor independence.

Choosing the right framework depends on the specific requirements of the organization, including the data volume, update frequency, query patterns, and the existing technology stack. A thorough evaluation and benchmarking are essential before making a final decision.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

4. Emerging Trends in Data Lake Architectures

While Apache Hudi, Delta Lake, and Apache Iceberg have significantly improved the capabilities of data lakes, several emerging trends are further shaping the future of data lake architectures:

4.1 Data Lakehouses

The data lakehouse concept aims to combine the best features of data lakes and data warehouses, offering a unified platform for both analytical and transactional workloads. A data lakehouse builds on the foundation of a data lake, adding features such as ACID transactions, data governance, and optimized query performance to support a wider range of use cases. Frameworks like Hudi, Delta Lake, and Iceberg play a crucial role in enabling data lakehouse architectures by providing the necessary transactional guarantees and data management capabilities. The ability to directly query data in the data lake without moving it to a separate data warehouse significantly reduces data latency and simplifies data pipelines.

4.2 Multi-Modal Data Lakes

Modern data lakes are increasingly required to support a variety of data types, including structured, semi-structured, and unstructured data. Multi-modal data lakes provide a unified platform for storing and processing these diverse data types, enabling organizations to gain a holistic view of their data. This requires integrating different storage formats, processing engines, and data governance tools. For example, a multi-modal data lake might store structured data in Parquet format, semi-structured data in JSON format, and unstructured data in object storage. It might then use Spark for batch processing, Flink for stream processing, and specialized engines for processing images, videos, or other types of unstructured data.

4.3 AI/ML-Powered Data Lake Management

Artificial intelligence and machine learning are playing an increasingly important role in data lake management. AI/ML can be used to automate tasks such as data quality monitoring, anomaly detection, and data optimization. For example, AI/ML models can be trained to identify and correct data errors, to detect unusual patterns in data access, or to optimize data partitioning and compaction strategies. AI/ML can also be used to improve data discovery and data governance by automatically tagging and classifying data assets. Furthermore, AI-powered query optimization can dynamically adjust query plans based on data characteristics and query patterns, leading to significant performance improvements.

4.4 Serverless Data Lakes

Serverless computing is gaining traction in data lake architectures, allowing organizations to build and deploy data pipelines without managing underlying infrastructure. Serverless data lakes leverage cloud-native services such as AWS Lambda, Azure Functions, and Google Cloud Functions to execute data processing tasks on demand. This approach offers several benefits, including reduced operational overhead, improved scalability, and pay-per-use pricing. Serverless data lakes can be used for a variety of use cases, such as data ingestion, data transformation, and data analysis. They are particularly well-suited for event-driven architectures and real-time data processing.

4.5 Data Mesh

The Data Mesh is a decentralized architectural paradigm that treats data as a product and empowers domain teams to own and manage their data pipelines. In a Data Mesh architecture, each domain team is responsible for creating, maintaining, and exposing its data as a product to other teams. This requires establishing clear data ownership, data governance policies, and data discoverability mechanisms. Data Mesh promotes agility and innovation by enabling domain teams to independently develop and deploy data products that meet their specific needs. While not directly related to Hudi, Delta Lake, or Iceberg, Data Mesh impacts how data lake technologies are used within an organization, promoting a more distributed and decentralized approach to data management.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

5. Challenges and Future Directions

Despite the significant advancements in data lake technologies, several challenges remain. These challenges present opportunities for future research and development:

• Metadata Management Complexity: Managing metadata in large-scale data lakes can be complex and challenging. Current metadata management solutions often struggle to keep pace with the rapid growth of data and the evolving requirements of data consumers. Future research should focus on developing more scalable, efficient, and automated metadata management solutions.
• Data Governance and Security: Ensuring data governance and security in data lakes is critical, particularly in regulated industries. Current data governance solutions often lack the granularity and flexibility required to enforce complex data policies. Future research should focus on developing more sophisticated data governance tools that can automatically discover, classify, and protect sensitive data.
• Interoperability and Standardization: The lack of interoperability between different data lake formats and processing engines remains a significant challenge. Standardization efforts are needed to promote interoperability and to reduce the complexity of building data pipelines. Future research should focus on developing open standards for data lake metadata and data formats.
• Real-Time Data Lake Performance: Achieving low-latency access to data in data lakes remains a challenge, particularly for complex queries and large datasets. Future research should focus on developing more efficient query optimization techniques, data skipping strategies, and indexing methods.
• Integration with Emerging Technologies: Integrating data lakes with emerging technologies such as AI/ML, serverless computing, and blockchain presents both opportunities and challenges. Future research should focus on developing new techniques for leveraging these technologies to improve data lake performance, scalability, and security.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

6. Conclusion

Apache Hudi, Delta Lake, and Apache Iceberg have revolutionized data lake architectures by providing transactional guarantees, incremental processing capabilities, and data governance features. These frameworks enable organizations to build more robust and reliable data lakes that can support a wider range of analytical and operational workloads. The emergence of data lakehouses, multi-modal data lakes, and AI/ML-powered data lake management are further shaping the future of data lake technologies. While challenges remain, the ongoing innovation in this field promises to unlock even greater value from data lakes in the years to come. Organizations should carefully evaluate their specific requirements and technology stack to choose the right framework and architecture for their needs. A key consideration should always be the future proofing aspects to ensure that the chosen technology can evolve along with the requirements of the business.

Many thanks to our sponsor Esdebe who helped us prepare this research report.

References

• Apache Hudi Documentation: https://hudi.apache.org/
• Delta Lake Documentation: https://delta.io/
• Apache Iceberg Documentation: https://iceberg.apache.org/
• Armbrust, M., Das, T., Ghodsi, A., Xin, R., & Zaharia, M. (2020). Delta lake: High-performance acid transactions for data lakes. Proceedings of the VLDB Endowment, 13(12), 2419-2432.
• Wamsley, B., Radke, D., Jain, D., Phillips, T., & Venner, J. (2021). Beyond the Data Lake: A Comparison of Lakehouse Technologies. Datanami. https://www.datanami.com/2021/07/29/beyond-the-data-lake-a-comparison-of-lakehouse-technologies/
• O’Reilly. (2020). What is a data lakehouse?. https://www.oreilly.com/radar/what-is-a-data-lakehouse/
• Dehghani, Z. (2019). Data mesh: A distributed architecture for enterprise-scale analytical data management. https://martinfowler.com/articles/data-mesh-principles.html
• Hudilite project: https://github.com/hudi-lite/hudi-lite
• Hudi Metadata Table: https://hudi.apache.org/docs/next/table_management/metadata_table/
• Record Level Indexing: https://hudi.apache.org/blog/2023/09/11/record-level-indexing/