Apache Hudi (pronounced "hoodie")

An alternative to Apache Iceberg and Delta Lake

Author

Gladstone Benjamin
December 23, 2024

What’s in today’s newsletter

  • Milvus 2.5 enhances searches with hybrid capabilities 🔍

  • Snowflake leads cloud data but faces growth challenges. 📊

  • Frank Slootman sells $115 million in Snowflake stock 💸

  • Snowflake EVP sells $517,959 in stock shares 💼

  • Big data errors impact decisions; validation is crucial. 📊

  • Automated lineage analysis improves database migration integrity 🔄

  • Databricks and Azure partnership enhances data workflows efficiency 📊

  • Varonis enhances data security with Databricks integration 🔐

Check out the new “What’s Hot” and up to the minute “New Database Job Opportunities” sections, as well as the weekly Deep Dive - This week we take a look at Apache Hudi

VECTOR DATABASES

TL;DR: Milvus 2.5 enhances vector databases with hybrid search, combining vector and keyword capabilities for better accuracy, efficiency, and real-time performance, significantly impacting data-driven industries like e-commerce and healthcare.

  • Milvus 2.5 introduces hybrid search capabilities that combine vector and keyword searches for improved accuracy.

  • Users can navigate extensive datasets more efficiently, benefiting applications like recommendation systems and natural language processing.

  • Enhanced indexing and real-time data query performance improve the overall user experience and data management.

  • The dual search functionality is transformative for industries such as e-commerce, finance, and healthcare, supporting informed decision-making.

Why this matters: With Milvus 2.5's hybrid search, organizations can process large data more accurately, boosting decision-making in sectors reliant on precise data retrieval, such as e-commerce and healthcare. This development signifies a leap in search technology by combining traditional and modern approaches, enhancing competitive advantage in a data-driven world.

SNOWFLAKE

TL;DR: Snowflake Inc. excels in cloud data warehousing but confronts growth challenges from competition, market saturation, and weaknesses. Strategic adaptation in data analytics and AI is vital for future success.

  • Snowflake Inc. leads the cloud data warehousing market but faces challenges from competition and market saturation.

  • The company's strengths include robust technology and brand recognition, contributing to impressive growth rates.

  • Weaknesses like dependency on infrastructure providers and rising costs indicate vulnerabilities that may hinder progress.

  • Addressing threats and leveraging opportunities in data analytics and AI are essential for sustained growth.

Why this matters: Snowflake's SWOT analysis highlights the critical need for strategic planning to maintain its leadership amid fierce competition and economic shifts. By addressing vulnerabilities and tapping into data analytics and AI innovations, Snowflake can better position itself for sustainable growth in a rapidly evolving market. 


TL;DR: Snowflake's Chairman Frank Slootman sold $115 million in stock, prompting discussions about insider trading, investor perception, and personal financial strategies, amidst a volatile market environment.

  • Frank Slootman, Snowflake's Executive Chairman, sold $115 million of stock amidst a volatile market environment.

  • The stock sale included over 500,000 shares sold at an average price of approximately $230 each.

  • Slootman's sales may reflect personal financial strategies rather than a lack of confidence in Snowflake's performance.

  • The transaction may influence investor perception and raise regulatory scrutiny regarding insider trading practices.

Why this matters: Slootman's substantial stock sale at Snowflake highlights a delicate tension between personal finance decisions and the impact on market perceptions. This could lead to heightened investor skepticism and regulatory scrutiny, which might not only affect Snowflake's stock price but also set precedents in the tech sector's approach to insider trading.

TL;DR: Snowflake EVP Christian Kleinerman's $517,959 stock sale of 2,060 shares raises investor speculation regarding the company's future, highlighting the complexities of insider trading and market perceptions.

  • Christian Kleinerman, Snowflake's EVP, sold stock worth approximately $517,959, raising attention from investors.

  • He sold 2,060 shares at an average price of $251.81 each, indicating a significant financial move.

  • Kleinerman's stock sale may lead to speculation about his outlook on Snowflake's future performance.

  • Insider trading raises questions about company health, necessitating broader consideration of market conditions by stakeholders.

Why this matters: Kleinerman's stock sale amid Snowflake's executive ranks may raise speculative concerns about company performance, yet it could reflect a personal financial strategy. Understanding such insider transactions is crucial for investors, as they navigate market uncertainties and seek signals about a company's future trajectory amidst broader economic contexts. 

DATA LINEAGE

TL;DR: Abhijeet Bajaj's automated lineage analysis revolutionizes database migration, enhancing data integrity and governance while reducing risks of data loss, thereby improving overall data management efficiency in organizations.

  • Databricks and Microsoft Azure's partnership aims to simplify data workflows, enhancing operational efficiency for organizations.

  • The collaboration allows users to access Databricks’ analytics capabilities directly on Azure, benefiting from improved security features.

  • This integration supports diverse data workloads, enabling businesses to derive actionable insights and accelerate innovation.

  • The partnership represents a growing trend in tech, emphasizing collaboration between cloud services and data analytics platforms.

Why this matters: Why this matters: As data volumes surge and compliance demands intensify, Bajaj’s automated lineage analysis offers companies a vital tool to secure data integrity during migrations. This innovation not only minimizes risks of data loss but also fortifies overall data governance, positioning organizations to leverage data as a strategic resource effectively. 

TL;DR: The article highlights the importance of detecting and correcting errors in big data, emphasizing effective validation techniques to ensure data accuracy for informed decision-making and operational efficiency.

  • Researchers stress that errors in big data can stem from collection methods, human mistakes, and technical issues.

  • Data validation techniques, including statistical analysis and auditing, are essential to detect inconsistencies in datasets.

  • Accurate big data analysis leads to informed decision-making and improved operational efficiency for organizations.

  • Enhancing error detection fosters stakeholder trust in data-driven processes and maximizes insights from big data.

Why this matters: As organizations increasingly rely on big data for decision-making, identifying and correcting data errors is crucial to avoid costly missteps. Effective error detection not only enhances analytical reliability but also builds stakeholder trust, ultimately ensuring organizations can realize the full potential of their data investments. 

DATABRICKS

TL;DR: Databricks and Microsoft Azure's partnership simplifies data workflows, enhancing operational efficiency and supporting diverse data workloads, empowering organizations to innovate and make data-driven decisions effectively.

  • Databricks and Microsoft Azure's partnership aims to simplify data workflows, enhancing operational efficiency for organizations.

  • The collaboration allows users to access Databricks’ analytics capabilities directly on Azure, benefiting from improved security features.

  • This integration supports diverse data workloads, enabling businesses to derive actionable insights and accelerate innovation.

  • The partnership represents a growing trend in tech, emphasizing collaboration between cloud services and data analytics platforms.

Why this matters: Simplifying data workflows through collaboration like that of Databricks and Azure can give organizations a crucial competitive edge. It enables rapid innovation and effective decision-making, which are key in today's data-driven market environment. This reflects an industry trend towards integrating cloud services with powerful data analytics, crucial for managing complex data environments. 

TL;DR: Varonis expands its data security solutions to Databricks, enhancing data visibility, compliance, and protection against breaches, potentially influencing other firms to strengthen security measures in cloud environments.

  • Varonis has expanded its data security solutions to include the Databricks cloud data platform for analytics.

  • The integration improves data visibility, anomaly detection, and user compliance within cloud environments for organizations.

  • This initiative helps enterprises safeguard sensitive data and comply with data protection regulations to avoid penalties.

  • Varonis' move may influence other data security firms to enhance offerings, fostering a more secure data ecosystem.

Why this matters: As businesses increasingly rely on cloud-based data analytics, the need for robust security solutions is vital to protect sensitive information. Varonis’ expansion to Databricks enhances data protection, supports compliance, and may drive industry-wide innovation, creating a more resilient defense against data breaches in cloud environments. 

NEW DATABASE JOB OPPORTUNITIES

DEEP DIVE

Let’s Look at Apache Hudi - An Open Source Data Management Framework

Apache Hudi (pronounced "hoodie") is a pioneering open-source data lakehouse platform that brings core database-like functionality—such as ACID transactions, record-level updates, and incremental data processing—directly to data lakes. Originally developed by Uber in 2016 to efficiently handle massive real-time data volumes, Hudi has evolved into a robust framework that unifies batch and streaming workloads, supports schema evolution, and integrates seamlessly with the modern big data ecosystem. It enables organizations to maintain high-quality, current datasets in their data lakes without sacrificing performance or consistency.

Key Features

Transactional Data Management (ACID Properties)

Hudi enables ACID (Atomicity, Consistency, Isolation, Durability) compliance on data lakes, ensuring transactional integrity even over large and frequently changing datasets. By maintaining a timeline of all actions performed on a table, Hudi allows atomic writes and consistent views of the data at any point in time. This transactional layer (often not natively supported in traditional data lakes) ensures that readers see a stable, coherent snapshot of the data, even as new writes occur [8][9].

Efficient Upserts and Deletes

Traditionally, data lakes were designed primarily for append-only workloads, making updates and deletes cumbersome or costly. Hudi solves this challenge by providing efficient, record-level upserts and deletes. It achieves this through indexing, which maps records to file groups, allowing Hudi to quickly locate and modify only the affected records rather than rewriting entire partitions or tables [8][9]. This capability is particularly beneficial for use cases like Change Data Capture (CDC) and compliance-driven data corrections.

Incremental Data Processing

Rather than reprocessing entire datasets each time new data arrives, Hudi supports incremental processing, focusing only on rows that have changed since the last commit. This greatly reduces computational overhead and speeds up ingestion cycles, enabling near real-time analytics and more responsive data pipelines [9].

Table Types: Copy-on-Write (CoW) and Merge-on-Read (MoR)

Hudi offers two main storage strategies, each optimized for different workloads and performance characteristics [4]:

  1. Copy-on-Write (CoW):
    CoW tables store all data in columnar Parquet files. Each write creates a new version of these files with updated records already merged. This approach is simpler for readers—queries see a fully compacted view of the data at all times, offering excellent read performance but potentially slower writes.

  2. Merge-on-Read (MoR):
    MoR tables store a combination of columnar Parquet base files and row-based Avro log files. Updates are written to these log files incrementally. Reads can combine base and delta files on-the-fly (lazy merging) or through scheduled compactions. This approach delivers a good balance between write speed and query latency, allowing faster ingestion at the cost of more complex reads or periodic background compactions.

Query Types: Snapshot, Incremental, and Read-Optimized

Hudi supports three main query modes [9]:

  1. Snapshot Queries:
    Return the latest, consistent snapshot of the table as of a given commit, reflecting all upserts and deletes.

  2. Incremental Queries:
    Provide only the data that has changed since a specified commit, enabling downstream systems to process data continuously without full rescans.

  3. Read-Optimized Queries:
    Access only columnar base files (in CoW or after compactions in MoR), offering the fastest query performance and a simpler read path.

Data Consistency, Integrity, and Schema Evolution

Hudi ensures that data in the lakehouse is accurate, complete, and current. It guarantees conflict resolution and consistent snapshots through concurrency control and atomic commits [9]. Additionally, Hudi supports schema evolution, allowing you to alter schemas over time without breaking existing pipelines. This flexibility is critical as business requirements and source data formats evolve.

Integration with the Big Data Ecosystem

Hudi integrates tightly with popular data processing engines and query layers [9]. It works with Apache Spark, Apache Hive, Trino (Presto), and Apache Flink, and can operate on various storage layers including HDFS, Amazon S3, Azure Data Lake Storage (ADLS), and Google Cloud Storage (GCS). This interoperability lets organizations easily incorporate Hudi into their existing architectures and leverage familiar tools for data ingestion, transformation, and analysis [1][3][5].

Benefits

  • Data Consistency and Integrity:
    By bringing ACID transactions and concurrent write/read capabilities to data lakes, Hudi ensures up-to-date, reliable, and correct datasets [9].

  • Performance Improvements:
    Indexing, incremental processing, and choice of table formats allow for faster queries and more efficient ingestion, reducing both computational and storage overhead [9].

  • Schema Flexibility:
    Hudi’s support for schema evolution lets teams adapt to changing data structures without costly migrations or downtime [9].

  • Compliance and Governance:
    Hudi’s ability to apply deletes and updates at a record level helps organizations comply with privacy regulations (like GDPR) and manage data governance tasks more effectively [7].

Use Cases

  1. Real-time Data Lakes:
    Incorporating new data as it arrives while ensuring the data is immediately queryable is a key scenario Hudi excels at [9].

  2. Change Data Capture (CDC):
    Hudi seamlessly integrates with CDC pipelines to continuously ingest updates and deletes from transactional source systems, maintaining a current and historized view of the data lake.

  3. Near Real-Time Analytics:
    E-commerce, financial services, and ad-tech companies use Hudi to enable near real-time analytics, personalizing customer experiences and informing time-sensitive decision-making [7].

  4. Healthcare and 360-Degree Views:
    Healthcare use cases benefit from Hudi’s ability to maintain a complete and current view of patient records, enabling better treatment decisions and patient care [7].

  5. Governance and Compliance Automation:
    Applying record-level updates and deletes to comply with privacy laws and regulatory requirements is made simpler by Hudi’s ACID capabilities and incremental file management [7].

Conclusion

Apache Hudi revolutionizes how data lakes are managed by bringing transactional database features, efficient incremental processing, and flexible schema evolution directly to large-scale storage systems. Originally developed to handle Uber’s real-time data challenges, it now enables organizations across industries to build robust, low-latency, and consistent data lakehouses. By offering a unified framework for upserts, deletes, and incremental queries—along with seamless integration into the modern big data stack—Hudi ensures that data remains fresh, queryable, and trustworthy at all times [10].

Gladstone