Revolutionizing Data Management: A Review of Hudi's Success Stories at Walmart, Uber, Grofers, and Robinhood

Abstract:

The exponential growth of data has led to the emergence of various data management tools and technologies. Hadoop-based data lakes have been widely adopted to store and process large volumes of data. However, managing data lakes efficiently is a challenge due to issues such as data duplication, inconsistent data, and slow query performance. This paper reviews the adoption of the Hadoop-based data management tool, Hudi, by three major companies - Walmart, Uber, and Grofers - and its benefits in terms of data lake efficiency. The review covers the origins of data lakes in each company, the challenges faced, and how Hudi helped in addressing those challenges.

Keywords: data lakes, Hadoop, Hudi, data duplication, inconsistent data, slow query performance, Walmart, Uber, Grofers, data lake efficiency, challenges, origins.

Introduction:

The management of big data has become a crucial aspect for companies across various industries. With the vast amount of data being generated daily, it has become necessary to store, process, and analyze this data efficiently to gain valuable insights. Hadoop-based data lakes have emerged as a popular solution for companies to store and manage large volumes of data. However, managing these data lakes can be a challenge, as issues like data duplication, inconsistent data, and slow query performance can occur. To address these challenges, Apache Hudi has emerged as a powerful solution, offering features like efficient data ingestion, record-level updates and deletes, and near-real-time query performance.

This review paper focuses on the adoption of Hudi by major companies like Walmart, Uber, and Grofers. We explore how Hudi has helped these companies overcome the challenges they faced with their data lakes and manage their data more efficiently. Additionally, we examine the benefits that these companies have received since adopting Hudi, including faster query performance, reduced data duplication, and improved data consistency. Overall, this paper provides valuable insights into how Hudi has revolutionized data management for these companies and can offer a solution for others facing similar challenges.

Walmart's Adoption of Hudi:

Walmart is one of the largest retailers in the world, generating massive amounts of data across various business domains. Managing this data efficiently to gain insights and make informed decisions is essential for Walmart's continued success. Walmart had adopted a Hadoop-based data lake to store and process large volumes of data. However, the data lake faced significant challenges, including data inconsistency issues, slow query performance, and data duplication.

To overcome these challenges, Walmart adopted Apache Hudi, an open-source data management framework. Hudi helped Walmart reduce data duplication and ensured data consistency. It also improved query performance and reduced the overall size of the data lake. Hudi's ability to perform upserts, merges, and deletes at scale enabled Walmart to handle data updates and deletes efficiently.

In addition to resolving the data management challenges, Hudi also enabled Walmart to support real-time use cases, including streaming data ingestion and processing. Hudi's support for near real-time data and metadata queries helped Walmart provide timely and accurate information to its various teams. Overall, Hudi helped Walmart manage its data more efficiently, improve query performance, and reduce the data lake's overall size, making it a valuable tool for the company's continued growth and success.

Uber's Adoption of Hudi:

Uber, a global transportation company, has a complex data ecosystem that includes multiple data sources, such as ride and driver data, and a large-scale data lake. However, the company faced significant challenges due to data inconsistency, high latency, and slow query performance, which impeded its ability to analyze and use data effectively. To overcome these challenges, Uber adopted Hudi, a powerful open-source data management tool, to improve their data quality and query performance.

One of the significant benefits that Uber gained from using Hudi was the efficient upsert feature, which helped to reduce data inconsistency and improve data quality. With Hudi, Uber was able to perform incremental processing, allowing them to add and update data in real time while ensuring consistency. This meant that Uber's data analysts and engineers could rely on the accuracy and completeness of the data, leading to improved decision-making and better insights.

Moreover, Hudi's support for efficient columnar storage and indexing helped to reduce query latency and improve query performance significantly. This helped Uber to analyze their data in near-real-time, enabling them to respond quickly to changing business needs and customer demands.

Overall, Uber's adoption of Hudi helped them to overcome their data management challenges and improve their data quality and query performance. With Hudi, Uber can now efficiently manage their complex data ecosystem, enabling them to make more informed decisions and improve their services for their customers.

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Grofers' Adoption of Hudi:

?Grofers, an Indian online grocery delivery service, had a rapidly growing data lake with new data sources being added regularly. This led to challenges in data quality, consistency, and slow query performance. To address these challenges, Grofers adopted Hudi, which helped in improving the data quality and reducing query latency. Hudi's efficient data ingestion and incremental processing features enabled Grofers to handle the increasing data volume with ease. Additionally, Hudi's data quality checks helped in ensuring the consistency and reliability of the data. Furthermore, Hudi helped in reducing the overall storage cost by utilizing a columnar format for data storage, which allowed for better compression and reduced the overall size of the data lake. Overall, Hudi helped Grofers in efficiently managing their data lake and gaining valuable insights to improve their business operations.

RobinHood Adoption of Hudi

Robinhood is a popular financial services company that offers commission-free trading of stocks, options, and cryptocurrencies. With millions of users and an ever-growing data ecosystem, Robinhood faced challenges in managing and processing its data efficiently. To address these challenges, Robinhood adopted Apache Hudi to build a streaming lakehouse that brought data freshness from 24 hours to less than 15 minutes.

Robinhood's adoption of Hudi involved several steps, beginning with building a streaming infrastructure to ingest data in real time. They used Apache Kafka to stream data from various sources, including databases and third-party services, into Hudi's Delta Lake format. This allowed Robinhood to achieve near-real-time data ingestion, enabling faster insights and decision-making.

To ensure data quality and consistency, Robinhood implemented several data validation checks using Hudi's built-in data quality tools. They also used Hudi's incremental processing capabilities to only process the changed data, reducing processing time and overall cost. Additionally, Robinhood leveraged Hudi's efficient upserts feature to minimize data duplication and ensure data consistency across the lakehouse.

Robinhood's adoption of Hudi resulted in significant improvements in data freshness, query latency, and overall data processing efficiency. They were able to provide real-time insights to their users and improve decision-making capabilities across the organization. The adoption of Hudi also allowed Robinhood to scale their data ecosystem efficiently while reducing operational costs.

In conclusion, the adoption of Apache Hudi helped Robinhood in building a streaming lakehouse that enabled near-real-time data ingestion, efficient processing, and improved data quality and consistency. Hudi's features such as incremental processing, efficient upserts, and built-in data quality checks helped Robinhood in achieving their goals of faster insights and improved decision-making capabilities.

Conclusion:

In conclusion, managing big data has become an essential aspect for companies across various industries, and the Hadoop-based data lake is a popular solution to store and manage large volumes of data. However, managing data lakes efficiently is a challenge due to issues such as data duplication, inconsistent data, and slow query performance. To address these challenges, Apache Hudi has emerged as a powerful solution, offering features like efficient data ingestion, record-level updates and deletes, and near-real-time query performance. This review paper focused on the adoption of Hudi by major companies like Walmart, Uber, Grofers, and Robinhood, and explored how Hudi has helped these companies overcome the challenges they faced with their data lakes and manage their data more efficiently. The benefits these companies received since adopting Hudi include faster query performance, reduced data duplication, improved data consistency, and efficient management of their data lakes. Overall, Hudi has revolutionized data management for these companies and offers a solution for others facing similar challenges.

References

Walmart Global Tech. (2021, February 4). Lakehouse at Fortune 1 Scale. Medium. https://medium.com/walmartglobaltech/lakehouse-at-fortune-1-scale-480bcb10391b

Chaudhary, A. (2020, November 16). Origins of Data Lake at Grofers. Grofers Engineering. https://medium.com/groferseng/origins-of-data-lake-at-grofers-6c011f94b86c

Uber Engineering. (2021, April 28). Uber's Lakehouse Architecture. Uber. https://www.uber.com/blog/ubers-lakehouse-architecture/

Apache Hudi. (n.d.). Powered By. Retrieved from https://hudi.apache.org/powered-by/

Databricks. (2022). How Robinhood Built a Streaming Lakehouse to Bring Data Freshness from 24h to Less Than 15 Mins. Retrieved from https://microsites.databricks.com/sites/default/files/2022-07/How-Robinhood-Built-a-Streaming-Lakehouse-to-Bring-Data-Freshness-from-24h-to-Less-Than-15%20Mins.pdf