Amazon S3 Data Lakes: A Complete Guide

‍Amazon Simple Storage Service (S3) was the first service to be made available when Amazon Web Services (AWS) was launched in 2006. In addition to being simple, it’s affordable, secure, and scalable. Amazon S3 has proved tremendously popular, with one recent report giving it a 94% share of the enterprise data storage market. S3 is an integral part of the entire AWS business. 

This blog post provides a comprehensive guide to S3 data lakes and recommends the use of a lakehouse architecture, provided by an open source project such as Apache Hudi, in cases where improved manageability, queryability, or other desired characteristics make it worth the modest additional effort. 

What is an S3 Data Lake?

Amazon S3 provides scalable and secure object storage for a wide range of use cases, including data lakes, mobile apps, websites, backups, archives, big data analytics, business applications, AI and machine learning, and IoT devices. Customers can customize and optimize data access to meet business and compliance requirements.

How Does it Work?

An Amazon S3-based data lake works by storing vast amounts of structured and unstructured data in scalable, secure S3 buckets. Data is ingested from various sources, such as cloud, on-premises systems, or IoT devices, and stored in S3 as objects. The data is then indexed and cataloged, allowing users to easily discover and manage it. Amazon S3 integrates with analytics and machine learning services such as Amazon Athena, Amazon Redshift, and SageMaker, enabling real-time data analysis, visualization, and insights. Data governance and security are ensured through role-based access control, encryption, and detailed logging of all actions within the data lake.

Benefits of an S3 Data Lake

The main benefits of an S3-based data lake include:

1. Scalability: S3 can store virtually unlimited amounts of structured and unstructured data, allowing organizations to scale their data storage as their needs grow without worrying about capacity limits.
2. Cost-effectiveness: S3 offers a tiered pricing model, including storage classes such as S3 Glacier, making it cost-efficient for storing large volumes of infrequently accessed data.
3. Data accessibility and integration: S3 integrates seamlessly with AWS analytics, machine learning, and big data services (e.g., Amazon Athena, Amazon Redshift, Amazon EMR), enabling easy data exploration, analysis, and insights generation.
4. Security and compliance: S3 provides robust security features such as encryption, access control, and compliance certifications (e.g. GDPR, HIPAA), ensuring that data is securely managed and meets regulatory requirements.
5. High availability and durability: Amazon S3 is designed for 11 9’s (99.999999999%) durability and 99.99% availability in a given year, ensuring that data is protected from loss and readily accessible when needed.
6. Flexible data management: With indexing, cataloging, and lifecycle management tools, S3 allows users to organize, tag, and automate data processes, optimizing data management over time.

These benefits make S3 a powerful and flexible foundation for building a data lake that can handle diverse data types and analytics workloads.

Drawbacks or Challenges

While an S3-based data lake offers many benefits, it also has some common implementation challenges. Here are a few of the key challenges and strategies to address them:

1. Data Governance and Quality Control

• Challenge: Managing data governance, ensuring data quality, and enforcing consistent policies across a large-scale, decentralized data lake can be challenging. Without proper governance, data lakes can turn into "data swamps," where data is poorly organized, undocumented, and hard to analyze.
• Solution: Implement strong data governance frameworks using tools such as AWS Lake Formation to manage permissions and enforce policies. Employ metadata management and cataloging through AWS Glue to maintain data quality and keep data discoverable and organized.
• Note: The old saying, “garbage in, garbage out” does not necessarily apply fully in this age of data science. Surprising uses can be found for even unpromising data. It’s not necessarily a bad idea to store data that is partial, not well described, etc., as long as needed descriptive information is recorded and maintained along with the data. 

2. Performance Limitations for Complex Queries

• Challenge: S3 is an object storage system optimized for storing large amounts of data, but not necessarily for high-performance querying use cases. Querying large datasets directly from S3 using services like Amazon Athena or Redshift Spectrum can lead to relatively slow response times, especially with complex queries, unoptimized data structures, and other complicating factors.
• Solution: Improve query performance by optimizing data formats. Store data in efficient, compressed formats such as Parquet or ORC and partition data based on key dimensions. Use services like Amazon Redshift or EMR for more complex workloads that need higher compute power. Use pilot projects and comparative benchmarking to expose issues and set reasonable expectations for performance. For more information, see Onehouse Analytics Engine Guide. 

3. Security and Access Control Challenges

• Challenge: Managing fine-grained access controls and ensuring security across a large number of data sources and users can be complex, especially in multi-tenant or cross-account setups.
• Solution: Use AWS Identity and Access Management (IAM) in conjunction with AWS Lake Formation to manage fine-grained access control. Enable encryption - at rest and in transit - using AWS Key Management Service (KMS), and ensure that all access is logged using AWS CloudTrail and Amazon S3 Access Logs for auditing purposes.

4. Lack of ACID Transactions

• Challenge: Amazon S3 by itself does not support ACID (Atomicity, Consistency, Isolation, Durability) transactions, making it difficult to manage updates to data, especially when multiple writers are involved.
• Solution: Use a data lakehouse framework, such as Apache Hudi, Delta Lake, or Apache Iceberg, on top of S3 to enable ACID transactions. These frameworks can handle updates and deletes, and support time travel for queries, making the data lake more robust for data operations.

5. Metadata Management

• Challenge: S3 doesn’t inherently provide advanced metadata management, making it hard to organize, track, and search large datasets based on metadata attributes.
• Solution: Leverage AWS Glue to automate metadata extraction and build a data catalog that makes it easier to discover, search, and manage datasets. Devote resources to creating and maintaining the catalog. As part of this effort, use custom tagging for organizing data within S3.

Best Practices for S3 Data Lakes 

Here are the five best practices for implementing an S3-based data lake, aligned with common dimensions such as storage, cost management, data governance, and lifecycle management:

1. Store Data in Raw, Unaltered Format:

• Best Practice: Always store original data in its raw, unaltered format in a dedicated S3 bucket or folder. This ensures that you maintain an immutable copy of the source data for future reprocessing or analysis.
• Why: Preserving raw data ensures flexibility in performing future analyses or transformations without losing the original data, especially as business needs evolve or new data processing tools and methods are introduced. It also supports auditability and governance. 

2. Manage Costs Using S3 Storage Classes:

• Best Practice: Use the appropriate S3 storage classes based on access patterns. Store frequently accessed data in S3 Standard, infrequently accessed data in S3 Standard-IA or S3 One Zone-IA, and long-term archival data in S3 Glacier or S3 Glacier Deep Archive.
• Why: This helps reduce costs by automatically storing less frequently accessed data in cheaper storage classes while still retaining accessibility when needed and allows for cost-effective backup.

3. Leverage S3 Object Tags for Organization and Access Management:

• Best Practice: Use S3 object tags to label and categorize objects with metadata, such as project names, data sensitivity, or data type. These tags can then be used for filtering, permissions management, and/or applying cost allocation policies.
• Why: Tags provide a flexible way to organize data across different departments or projects and make it easier to track, manage permissions, or generate detailed reports on S3 usage and costs.

4. Use Lake Formation for Unified Data Governance and Data Sharing:

• Best Practice: Leverage AWS Lake Formation to manage fine-grained access controls and data governance, and to enable cross-account data sharing with unified policies.
• Why: Lake Formation simplifies data governance by offering centralized control of data access policies, allowing you to securely share data across multiple AWS accounts without needing to manage complex IAM permissions and S3 bucket policies.

5. Take Advantage of S3 Lifecycle Policies:

• Best Practice: Implement S3 lifecycle policies to automatically transition data between storage classes (e.g., from S3 Standard to Glacier) or delete objects after a certain period of time based on their age or usage.
• Why: S3 lifecycle policies help manage storage costs by automating the transition of data to more cost-effective storage tiers when it’s less likely to be needed frequently, or removing data when it's no longer needed, ensuring efficient data retention without manual intervention.

Integrating Apache Hudi & S3

To implement these S3 data lake best practices, consider using an open-source storage layer such as Apache Hudi, a transactional data lakehouse platform built around a database-like kernel. Hudi integrates natively with S3, offering a cost-effective, scalable, and high-performance solution for data lakes. Unlike a basic S3 data lake with only data files, Hudi provides a metadata management layer that enables efficient data handling with transactional capabilities—such as updates, deletes, and upserts—directly within the data lake, optimizing both storage and query performance.  

By storing only incremental data changes and automating tasks such as clustering, compaction, and cleaning, Hudi reduces storage costs on S3. It also enhances query performance with advanced indexing and optimized file layouts, benefiting engines like Amazon Athena, Presto, and Trino. Seamlessly scaling with S3, Hudi supports large-scale ingestion and real-time streaming with Apache Spark and Apache Flink, while managing metadata and table organization to keep the data lake accessible for analytics. Its broad ecosystem support makes Hudi a versatile transactional data lakehouse solution for various architectures.

Instead of a Do-It-Yourself (DIY) S3 data lakehouse project with Apache Hudi, consider Onehouse - a managed solution that simplifies setup and keeps you at the cutting edge of the evolving data ecosystem. For more details, see Building a Data Lakehouse: Managed vs. Do-it-Yourself.

Summary

S3 data lakes have revolutionized the way businesses ingest, store, and analyze data, offering unmatched flexibility, scalability, and cost-effectiveness. By preserving raw data in its original form, organizations can continually innovate, allowing analysts and data scientists to explore new insights and use cases without limitations. This approach enables businesses to adapt quickly and derive value from their data in ways that traditional storage systems cannot.

Although S3 data lakes present challenges in areas such as governance, performance, and security, these issues can be effectively addressed with tools like AWS Lake Formation, AWS Glue, and Apache Hudi. Apache Hudi’s integration with S3, for instance, enhances scalability, supports real-time data operations, and reduces costs. Together, these solutions ensure that S3-based data lakes remain powerful, efficient, and secure environments for unlocking the full potential of an organization’s data and modern data analytics.

If you're seeking a managed data lakehouse solution that lets you focus on extracting insights and value from your data, contact us at gtm@onehouse.ai or sign up via our listing on the AWS Marketplace today!