Building a modern Data Lake with Apache Hudi, Apache Iceberg, Delta Lake

In today’s digital age, the volume and diversity of data generated by individuals, organizations, and machines are growing at an unprecedented rate. As a result, traditional data management systems are struggling to keep up with the demands of handling this vast and ever-expanding ocean of information. This is where data lakes come into play – filling the gap where data warehouses have strugled in storing, managing, and analyzing massive amounts of data.

What is a Data Lake?

First introduced back in 2010, a brief history , a data lake is a centralized repository that allows organizations to store vast amounts of structured, semi-structured, and unstructured data in its raw and original format. Unlike traditional databases, which require data to be structured and categorized upfront, data lakes accommodate data in its native form. This makes data lakes highly flexible and capable of handling a wide variety of data types, including text, images, videos, sensor readings, social media posts, and more.

Key Benefits of Data Lakes

1. Scalability: Data lakes are built to scale effortlessly, accommodating petabytes or even exabytes of data.
2. Flexibility: Since data lakes store data in its raw format, you can apply various processing and analysis techniques as needed, making them ideal for exploratory data analysis and machine learning.
3. Cost-Effectiveness: Data lakes can be built using cost-effective storage solutions like cloud-based services such as Amazon S3 or Google Cloud Storage, reducing the overall infrastructure costs.
4. Data Integration: Data lakes enable seamless integration of data from diverse sources, eliminating silos and promoting data-driven decision-making.
5. Open format: Data lakes are built using open source frameworks allowing you to avoid lock-in to a proprietary data warehouse solutions.

The challenges of traditional Data Lakes

Although data lakes have been around for more than a decade, traditional data lakes have lacked features for proper oversight and cataloging of data, resulting in so-called “data swamps”. Notable challenges that data lakes faced include:

1. Lack of Data Governance: Data lakes often lack proper data governance, leading to inconsistent data quality, undefined ownership, and unclear data definitions. This can result in data being unreliable, untrustworthy, and difficult to interpret.
2. Schema-on-Read Complexity: While data lakes allow storing data without a predefined schema (schema-on-write), this can lead to complexity when querying and analyzing the data (schema-on-read). Schema evolution and inconsistency can make it difficult to interpret the data correctly.
3. Data Quality Issues: Poor data quality, including missing values, inconsistencies, and inaccuracies, can make analysis unreliable and lead to incorrect conclusions. Without data cleansing and validation processes, data quality can deteriorate.
4. Data Security and Privacy: Data stored in data lakes might be sensitive, and inadequate security measures can expose the organization to data breaches or regulatory violations. Proper access controls, encryption, and auditing are essential to mitigate these risks.
5. Lack of Metadata Management: Proper metadata management, including data lineage, data definitions, and data relationships, is crucial for understanding and using data effectively. Without metadata, data discovery becomes challenging.

Open source formats that address the challenges

Apache Hudi, Apache Iceberg, and Delta Lake address several challenges commonly encountered when dealing with data lakes, including issues related to data quality, data management, schema evolution, query performance, and more.

Apache Hudi was originally developed at Uber and later contributed to the Apache Software Foundation in 2019. It provides support for handling data updates and incremental data processing in data lakes.

Challenges Addressed by Apache Hudi:

1. Data Updates and Deletes: Data lakes traditionally struggle with efficiently handling updates and deletes. Apache Hudi addresses this challenge by providing mechanisms for efficient upserts (updates and inserts) and supporting delete operations while maintaining data integrity.
2. Incremental Data Processing: Hudi simplifies incremental data processing by enabling efficient change capture and propagation, allowing users to process only the changed data instead of reprocessing the entire dataset.
3. ACID Transactional guarantees: Hudi brings transactional guarantees with consistent, atomic writes and concurrency controls.
4. Query Performance: Apache Hudi optimizes query performance through techniques like columnar storage and predicate pushdown, enhancing the efficiency of analytical queries on large datasets.

Apache Iceberg was originally developed by Netflix and Apple, and released to open source in November 2018. It addresses challenges related to data management and organization in data lakes by providing features for schema evolution, time travel, and snapshot isolation.

Challenges Addressed by Apache Iceberg:

1. Table Evolution: Iceberg provides a mechanism for schema evolution, enabling data lakes to accommodate changes in data structures over time without disrupting existing data and workflows.
2. Consistency and Concurrency: Iceberg supports snapshot isolation, allowing multiple users or processes to read and write data concurrently without conflicting with each other.
3. Time Travel and Versioning: Apache Iceberg enables time travel and versioning, which allows users to query historical data states, perform point-in-time analysis, and revert to previous data versions.
4. Metadata Management: Iceberg’s metadata management features, including partitioning and sorting, help optimize query performance and data organization.

Delta Lake was developed by Databricks and released to open source in April 2019 as an open-source storage layer that adds ACID transactions and improved data management capabilities to Apache Spark and data lakes.

Challenges Addressed by Delta Lake:

1. ACID Transactions: Delta Lake brings ACID transactions to data lakes, ensuring data consistency and integrity during data updates, inserts, and deletes, which were traditionally challenging to achieve.
2. Schema Enforcement and Evolution: Delta Lake enforces schema on write, helping maintain data quality, and allows for schema evolution, making it easier to adapt to changing data requirements.
3. Time Travel and Versioning: Similar to Iceberg, Delta Lake supports time travel and versioning, enabling historical data analysis and debugging.
4. Optimized Performance: Delta Lake’s optimizations, such as data compaction, statistics management, and metadata caching, contribute to improved query and processing performance.

Comparison

1. Use Case and Requirements:
   • Apache Hudi: Hudi’s use case involves frequent data updates, deletes, and incremental processing. It is well-suited for scenarios where you need to efficiently capture and propagate changes to your data.
   • Apache Iceberg: Iceberg is a good choice if you need strong data management capabilities, including schema evolution, time travel, and snapshot isolation. It’s suitable for scenarios where data consistency and organization are critical.
   • Delta Lake: Delta Lake is designed for ACID transactions and improved data management in data lakes. Data Lake is well integrated with Apache Spark , making it a good choice if your workloads already use Apache Spark for data analytics.
2. Query Performance:
   • Apache Hudi: Hudi offers optimizations like columnar storage and predicate pushdown for improved query performance, but its primary focus is on data updates and incremental processing.
   • Apache Iceberg: Iceberg provides features that enhance query performance, such as metadata management and data organization techniques, making it a good choice for scenarios where query speed is important.
   • Delta Lake: Delta Lake also emphasizes query performance through optimizations like data compaction and statistics management, making it a strong contender for improving query efficiency.
3. Data Quality and Integrity:
   • Apache Hudi: Hudi ensures data integrity for updates and deletes, making it suitable for maintaining data quality during incremental processing.
   • Apache Iceberg: Iceberg’s focus on time travel, snapshot isolation, and schema evolution contributes to data quality and consistency.
   • Delta Lake: Delta Lake’s ACID transactions and schema enforcement contribute to strong data quality and integrity.

Conclusion

Use Apache Hudi if you need ability to manage large-scale, near real-time transactional data with ACID compliance and efficient data ingestion. Its incremental processing and support for various data formats make it a versatile choice for managing dynamic datasets.

Use with Apache Iceberg if you need a layer of simplicity and scalability over huge tables. It allows for schema evolution, time travel, and efficient metadata management. Its compatibility with different storage systems provides the flexibility needed to adapt to changing requirements, ensuring a future-proof solution.

Use Delta Lake if you are in Apache Spark and Databricks train. This will allow to tap into existing ecosystem with well supported integrations for transactional capabilities and unified batch and stream processing.

Ultimately, the choice between Apache Hudi, Apache Iceberg, and Delta Lake will depend on your organization’s specific needs and priorities. In the next series of posts, I will dive deeper into building data lakes with Apache Hudi, Apache Iceberg, and Delta Lake. Stay tuned! ✌️