For over a decade, Hadoop* served as the cornerstone of big data infrastructure. However enterprises seek greater speed, flexibility, and scalability, Apache Spark™ has emerged as the preferred alternative. Industry leaders such as Databricks*, Ezmeral (HPE)*, AWS*, Microsoft Fabric*, and Blendata, a Thai Big Data & AI platform, have integrated Apache Spark™ into their core architectures to drive next-generation data processing solutions.
Why Are Product Vendors Transitioning from Hadoop to Apache Spark™?
1. Unified Processing Engine: Simplified Architecture and Enhanced Usability
One of Hadoop’s biggest limitations is its complex architecture, which requires multiple tools — such as MapReduce, Hive, Pig, and Impala — to work together. Managing and integrating these tools demands specialized expertise and increases system complexity.
Apache Spark™ addresses this challenge by offering a unified engine capable of handling multiple workloads on a single platform:
- Batch Processing – Scheduled processing for large-scale datasets.
- Real-Time Streaming – Instant analytics for continuous data flows.
- SQL Queries – Supports structured data analysis through Spark SQL.
- Machine Learning (MLlib) – Scalable AI model training and deployment.
- Graph Processing – Efficient analysis of graph-based data structures.
By consolidating these functionalities, it simplifies infrastructure management, making it easier to develop flexible and scalable solutions.
Real-World Use Case: Blendata utilizes Apache Spark™ to power Blendata Enterprise, its Data Lakehouse solution, enabling businesses to manage both batch and real-time data processing efficiently. (Blendata Enterprise)
2. High-Performance Computing with In-Memory Processing
Apache Spark™ outperforms Hadoop by leveraging In-Memory Processing, significantly reducing latency and enhancing computation speed.
The Key Difference Between Disk-Based Processing and In-Memory Processing:
- Hadoop (Disk-Based Processing):
Hadoop’s MapReduce framework relies on reading and writing data to disk at every stage of processing. This disk-intensive approach significantly increases latency, particularly when handling large datasets or performing complex computations.
- Apache Spark™ (In-Memory Processing):
Apache Spark™ leverages In-Memory Computing, storing data in RAM instead of repeatedly accessing disk storage. This approach dramatically reduces disk I/O operations, making data processing up to 100x faster in certain scenarios. Spark is particularly efficient for iterative analytics, such as training machine learning models or executing multiple computations on the same dataset.
Real-World Use Case: Databricks utilizes Apache Spark™ to power its advanced big data platform, leveraging in-memory processing for high-performance analytics. It enables businesses to develop solutions for Machine Learning, Real-Time Data Analytics, and SQL-based analytics, allowing them to gain insights faster and more efficiently. (Databricks)
3. Built-In AI and Machine Learning Capabilities
Another key important feature of Apache Spark™ is providing support of native AI and Machine Learning through MLlib (Machine Learning Library) that facilitates seamless model training and deployment without requiring multiple tools like Hadoop.
Real-World Use Case:
- HPE (Ezmeral): Utilize Apache Spark™ in HPE Ezmeral platform to support AI-driven solutions. (HPE)
- AWS: With Amazon EMR that supports Apache Spark™ for analyzing large-scale data and developing Machine Learning models by leveraging in-memory processing to accelerate training times. (AWS)
4. Real-Time Data Streaming, Unlike Hadoop
Another Hadoop key limitation is that its architecture is designed primarily for batch processing, making real-time data analytics challenging . In contrast, Apache Spark™ supports real-time streaming through Spark Structured Streaming, able to integrate with any data storage, such as file store and Kafka, enabling enterprises to process and analyze data as it arrives.
Real-World Use Case: Databricks harnesses the power of Apache Spark™ in its Databricks Data Intelligence Platform and integrates Spark Structured Streaming to enhance real-time analytics and enable faster decision-making. (Databricks)
5. Cost Efficiency and Scalability For Future Data Growth
Apache Spark™ is optimized for cloud-native environments, seamlessly integrating with AWS S3, Google Cloud Storage, and other modern cloud solutions. This reduces infrastructure costs and enhances scalability, allowing businesses to scale resources as needed without the need for costly infrastructure reinvestments.
In contrast, Hadoop relies on HDFS (Hadoop Distributed File System), which requires additional tools and configurations to integrate with cloud services or Data Lakes. This complexity makes Hadoop less flexible, slower to scale, and more costly compared to Apache Spark™.
Real-World Use Case:
- Blendata: Leveraging Apache Spark™ as the main engine to enhance big data processing efficiently, reducing complexity, minimizing operational costs, while seamlessly supporting future data scalability. (Blendata Enterprise)
- Microsoft Fabric: Harnessing the power of Apache Spark™ to enhance big data processing and supporting scalability. (Microsoft Fabric)
The Key Differences Between Hadoop vs. Apache Spark™
| Features | Hadoop | Apache Spark™ |
| System Architecture | Requires Multiple Tools (MapReduce, Hive, Pig) | Unified Engine with All-in-one Capabilities |
| Processing Speed | Disk-Based Processing (Slower) | In-Memory Processing (Up to 100x Faster) |
| Workload Support | Primary Focus on Batch Processing | Supports Batch, Streaming, SQL, ML, and Graph Processing |
| Streaming Capabilities | Not Supported | Fully Supports Real-Time Streaming |
| AI/ML Capabilities | Requires Additional Tools Like Mahout | Built-in MLlib for Seamless AI Model Training |
Leading technology providers are choosing Apache Spark™ as the core engine behind their innovative and next-generation software solutions. With superior performance over Hadoop, Apache Spark™ offers faster data processing, built-in AI/ML capabilities, real-time analytics, and cost efficiency—making it the preferred choice for modern big data platforms.
For businesses seeking a cutting-edge Big Data & AI solution that enhances data management, maximizes cost efficiency, and unlocks valuable insights, Apache Spark™ is the answer.
Explore how Blendata can transform your data strategy. Contact Blendata’s experts at [email protected] or visit https://www.blendata.co/ to learn more.
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