The Industrial shift from Apache Hive to Apache Spark

6 min readSep 23, 2024

Why Spark became so compelling to companies

Introduction: Briefly introduce Hive and Spark, highlighting their roles in big data processing.
Five Reasons Companies Will Prefer Apache Spark to Hive
Conclusion: Summarize the reasons why companies are migrating from Hive to Spark and emphasize the benefits of this shift.
Additional Considerations

This was originally published on https://dailyepochs.substack.com

Intro

Nowadays, Data Engineers take it as the gospel truth that Apache Spark is the default processing engine for their batch and streaming workloads. While this may be true now, just a couple of years ago, Apache Hive was once the defacto tool for data engineering and analytics workflows. Now, although both tools still remain powerful for data processing workflows, each with its strengths and use cases, it is important to learn why and how Spark’s emergence significantly impacted the landscape

Better Performance for Iterative Tasks

Companies will always choose tools that can deliver the best performance; hence, the decision to adopt Spark for most workloads was a clear one. Spark’s in-memory processing(RAM) for caching data is the best choice for a variety of tasks, including iterative tasks such as real-time monitoring, logging, and incremental updates.

Spark’s versatility makes it the go-to choice for most companies, as most data tasks are iterative and repetitive.

That being said, Hive is a valuable tool in specific scenarios, particularly for tasks that involve one-time data transformations or data integrations that sometimes require data spaces too large to fit in memory.

Hive vs Spark Approach Image created by Author

There are a couple examples of companies that have marked significant improvements in performance from migrating from the Apache Hive Ecosystem to that of Spark’s like Netflix, which experienced up to 5x faster data processing speeds, allowing for more efficient real-time analytics after moving from Apache Pig and Hive infrastructure to Spark or eBay who reported up to 10x faster query performance and reduced job completion times and other companies like Yahoo and Uber who also noted improved scalability, with Spark’s in-memory processing enabling them to handle larger datasets more effectively than Hive’s disk-based storage.

Scalability

Hive translates its SQL queries into MapReduce jobs, which can lead to a large amount of data shuffling (redistribution of data across different partitions or nodes), especially in large joins or aggregation operations. As data volumes increase, so does the amount of data shuffling, causing delays and bottlenecks. In contrast, Spark’s DAG execution plans are highly efficient, minimizing data movement and optimizing shuffling. By keeping intermediate data in memory and reducing the number of stages that requires data shuffling, Spark significantly outperforms Hive in handling joins and aggregations over large datasets. This not only makes Spark a more efficient choice but also makes scalability a non-issue.

Spark, unlike Hive, is designed for scalability. It features advanced optimizations like predicate pushdown, code generation (Tungsten), and vectorized query execution, making it a powerful tool for handling complex analytical workloads. As data volumes grow, Hive’s efficiency diminishes, while Spark’s capabilities remain robust. For organizations looking to scale their data processing infrastructure, Spark clusters are the ideal solution. By adding more nodes (horizontal scaling), each with its own processing power (CPU, memory) and storage (disk), companies can seamlessly manage their increasing workloads. Spark’s scheduler further enhances performance by balancing workloads, ensuring optimal performance for concurrent queries, even in multi-user environments.

Real-time Processing

Apache Hive is limited in real-time data processing due to its batch-oriented design, high query latency, and lack of native support for real-time data ingestion and streaming. It is optimized for large-scale, high-latency batch queries and relies on resource-intensive execution engines like MapReduce or Tez, which are inefficient for low-latency, real-time workloads. Hive struggles with handling frequent updates, concurrent queries, and real-time event-driven architectures, and it lacks seamless integration with real-time dashboards.

Spark’s streaming, on the other hand, has frameworks like Spark Streaming and Structured Streaming, which enable near real-time data processing by leveraging micro-batches and continuous streaming. Spark Streaming divides live data into micro-batches for fault-tolerant, scalable processing, while Structured Streaming offers a more advanced, unified API with continuous processing, event-time handling, and exactly-once semantics.

Real-time analytics has become increasingly vital across industries as businesses seek immediate insights for faster decision-making and improved efficiency to get that competitive edge

With the growing volume of data generated by IoT devices, social media, and other sources, it has become increasingly imperative for businesses to have the access and ability to analyze and act on data instantly. This allows companies to optimize decision-making, improve customer experiences, and respond swiftly to changing market conditions. Industries such as finance, retail, and logistics now rely on real-time insights to detect fraud, manage supply chains, and personalize services.

Unified Platform

Companies have realised having a unified platform for handling both their batch and real-time data processing is essential for their operational efficiency, as managing separate infrastructures for each type can be resource-intensive and complex . This is where Spark comes in, as it acts as a versatile, unified platform for handling diverse data workloads, including batch processing, real-time streaming, and machine learning, which sets it apart from Hive. Spark’s core engine is designed to process data in memory, which makes it highly efficient for large-scale batch-processing tasks.

Integrating all these features like data preparation, feature engineering, and real-time model inference and processing, ultimately enhancing the efficiency and effectiveness of its models based on fresh data being ingested, empowers organizations to stay agile in a rapidly changing market landscape. Spark’s real-time capabilities also provide a sense of security and keeps organizations up-to-date . In contrast, Hive primarily focuses on batch processing and data warehousing, with limited real-time capabilities and no built-in machine learning tools, making Spark a more comprehensive solution for most organizations’ modern, multi-faceted data processing needs.

Integration with Machine Learning
The increasing demand for integrating data warehousing with machine learning pipelines stems from the need for organizations to leverage their data to glean and make intelligent decision-making . As more and more data accumulate both structured and unstructured , the traditional silos between data storage and analytics hinder their ability to extract actionable insights. By combining data warehousing with machine learning, companies can streamline data flow from storage to model training and deployment, ensuring that insights are derived from the most current and comprehensive datasets.

Spark’s seamless integration with popular machine learning libraries like TensorFlow and PyTorch significantly enhances its appeal for data-driven organizations, setting it apart from Apache Hive.

Spark’s architecture allows for distributed data processing, enabling efficient model training and deployment across large datasets, which is crucial for deep learning applications. In contrast, Hive is primarily designed for batch processing and lacks direct support for advanced machine learning workflows, making it less suitable for modern data science needs. Consequently, companies prefer Spark for its flexibility, scalability, and the ability to integrate seamlessly with cutting-edge machine learning frameworks, allowing them to accelerate their analytics and improve their decision-making processes.

Conclusion

In conclusion, the shift from Apache Hive to Apache Spark marks a pivotal transformation in the world of data engineering and analytics. While both tools have their strengths, Spark’s superior performance, scalability, real-time processing capabilities, and integration with machine learning pipelines make it the clear choice for most modern data workloads. Companies like Netflix, eBay, and Uber have demonstrated the significant performance gains and operational efficiencies realized through adopting Spark. As the demand for real-time analytics, scalability, and unified data processing continues to grow, Spark’s versatility has made it indispensable in helping organizations stay competitive in a data-driven landscape, positioning it as the future of big data processing.