Spark vs. Hadoop in data engineering
Spark vs. Hadoop in data engineering
Spark has recently been gaining popularity over Hadoop for ML/AI use cases. Is Hadoop becoming obsolete? Do these technologies compete or complement each other? This blog post answers all your questions about Spark and Hadoop.
Hadoop and Spark are two open-source data processing technologies frequently found in data pipelines. Both are used to transform massive data sets for analytics. Spark is an advancement to Hadoop’s processing layer but still uses Hadoop for data storage. It is a leading technology in big data ML use cases and works alongside Hadoop in massive ML/AI training pipelines.
Spark and Hadoop allow ML engineers to process data faster at scale. You can do model training and SQL transformations within a single ecosystem. This article explores the difference between Spark and Hadoop and how they work together.
What is Spark?
Apache Spark™ is a multi-language data processing engine that can run data transformations at scale. Every Spark application has a driver program that runs various parallel operations on a cluster. Resilient Distributed Dataset (RDD) is the fundamental abstraction in Spark. You can think of an RDD as an immutable, distributed object collection that Spark processes in parallel.
You can perform two main kinds of operations on RDDs:
-
Transformations that generate a new dataset from an existing one.
-
Computations that return a result after data processing.
Once Spark processes the data, it needs to store it somewhere. Spark requires a distributed file system so it can store a large data file across multiple storage nodes. The keyword here is “requires”. Spark does not implement its own file system. Instead, it can work with several popular technologies, including Amazon S3, Apache Cassandra, MongoDB, etc. But the default file system that Spark ships with is Hadoop Distributed File System (HDFS). Yes — Spark downloads are pre-packaged for popular Hadoop client libraries.
What is Hadoop?
Apache Hadoop is a framework for large scale distributed data processing across computing clusters. As one of the original “big data” technologies, it provides several building blocks that other technologies utilized and advanced over time.
The four core components of Hadoop are:
-
HDFS: The storage layer that divides data files and stores them across a node cluster. HDFS can scale to thousands of nodes and automatically replicate data for enhanced fault tolerance.
-
MapReduce: The processing layer that divides tasks into smaller units for parallel processing. The Map function processes input data and produces a set of intermediate key/value pairs. The Reduce function takes these intermediate results, processes them, and produces the final output.
-
YARN: Hadoop’s cluster management technology. It allocates system resources, schedules tasks, and ensures that the cluster’s resources are utilized efficiently.
-
Hadoop Common: Includes essential Java files, scripts, and documentation required to operate Hadoop.
Being open source, a whole ecosystem emerged around Hadoop. For example, the data warehousing solution, Apache Hive, and the noSQL database, Apache HBase®, both run on top of HDFS.
Similarly, the initial motivation behind Spark was to address the limitations of Hadoop MapReduce. Spark was designed to provide faster data processing and in-memory computing capabilities. It continued to evolve to meet modern data processing requirements. It supports batch and stream data processing and has expanded storage options beyond HDFS.
Spark vs. Hadoop — Spark’s strengths
Firstly, it’s important to point out that you cannot compare Spark and Hadoop directly as they are two different technologies. Spark is a processing engine that leaves storage and cluster management to other systems. Hadoop is a complete solution that includes processing, storage, and management — but its processing engine is not the most efficient. Technically, we can compare Spark vs. Hadoop MapReduce, which is the processing layer within Hadoop. Here’s how Spark improves Hadoop MapReduce.
Processing speed
Some tests have shown Spark to process data 100X times faster than Hadoop MapReduce.
Hadoop MapReduce processes data in batches. Each MapReduce job reads and writes data to disk, leading to significant I/O overhead. In comparison, Spark processes data in memory, reducing the need for disk I/O and faster performance
Another factor impacting processing speed is Spark’s DAG (Direct Acyclic Graph) implementation. When you define operations on RDDs, Spark does not run them immediately. Instead, it performs lazy evaluation by first building a DAG that represents the global sequence of operations. The Spark job scheduler uses DAG to optimize the execution plan to minimize data shuffling and maximize data locality.
Spark runs your data transformation operations in the most efficient way possible. In comparison, Hadoop follows a two-stage (Map and Reduce) execution and does not perform global optimization across jobs.
Real-time processing
Hadoop MapReduce is inherently a batch processing system, as streaming data was less important when Hadoop was built. Real-time processing in Hadoop can be achieved through complementary projects like Apache Storm or Apache Flink, but they are separate from the core Hadoop ecosystem.
In contrast, Spark ships with Spark Streaming — a built-in library that allows for real-time data processing. It automatically processes data streams in small batches (micro-batching), enabling near real-time analytics.
Ease of use
Writing MapReduce code is complex and involves writing excessive boilerplate code in Java. Higher-level tools like Pig and Hive simplify this., but Hadoop has a steeper learning curve.
In contrast, Spark is much easier to learn and use. It offers a unified API for batch processing, streaming, SQL, machine learning, and graph processing. It supports more languages beyond Java and provides interactive shells (e.g., PySpark for Python) so developers can query and process data interactively.
Spark vs. Hadoop — Hadoop’s strengths
Spark is a processing powerhouse, but Hadoop has its advantages.
Cost
Those in-memory computations in Spark don’t come cheap! You have to pay for more RAM and compute compared to Hadoop. Even in the cloud, disk-based processing is cheaper than RAM-based. The cost of Hadoop per instance hour can be as much as 3x times less than Spark.
Scalability
Spark’s scalability depends on the underlying storage layer you use. Hadoop scales efficiently with HDFS. If you use Spark with HDFS, you get the same benefit — but if you choose another technology, that may not be the case.
Security
Hadoop has more built-in security features and out-of-the-box security options—especially for enterprise use cases. In contrast, Spark has limited built-in security features. You can use it over HDFS for enhanced security or implement workarounds like Apache Ranger.
Flexibility
You get more options with Hadoop for niche use cases. You can pick and choose different components for different data requirements and get a similar performance to Spark. Of course, that increases project complexity, but it may be worth the effort if you have to work with different legacy and modern technologies.
| Spark | Hadoop | |
|---|---|---|
| What is it? | Data processing engine | Data processing framework |
| Architecture | Processing — Spark Engine. Storage — HDFS (Hadoop Distributed File System) or other distributed database | Processing — Hadoop MapReduce. Storage — HDFS |
| Processing speed | Much faster because of in-memory computing and DAG implementation | Slower because of external I/O read/writes for every operation and no global DAG optimization |
| Real-time processing | Yes, with Spark Streaming | Requires external tooling with Apache Storm or Flink |
| Ease of use | Wider range of languages, libraries, and tools | Limited range, works only with Java |
| Cost | Higher due to higher RAM requirements | Lower |
| Scalability | Depends on the storage layer | High because of HDFS |
| Security | Less built-in functionality. Requires additional support from Apache Ranger of HDFS | Extensive built-in security features |
| Flexibility | Must use Spark libraries for optimum outcomes | You can combine with a range of open source solutions to meet specific requirements |
Summary of differences — Spark vs. Hadoop
Spark vs. Hadoop — use cases
In practice, Hadoop is rarely used by itself. You will often find it implemented in conjunction with Hive for data warehousing/ETL, Lucene for Indexing, HBase for NoSQL, etc.
Hadoop with Hive is preferred over Spark for ETL use cases that involve reading from various sources, processing the data, and storing it in a structured format. Hive allows users to run SQL queries on large datasets stored in HDFS, but the performance is generally slower. Organizations also use Hadoop in data archiving pipelines to store massive datasets for long-term retention, compliance, or future analysis.
Hadoop with Spark, or Spark by itself, is preferred for scenarios where users need to interactively explore large datasets, run queries, and get results quickly — for example, processing sensor data from IoT devices or updating dashboards.
Theoretically, you can implement every use case in either Spark or Hadoop. It’s just that in Hadoop, you have to implement other open-source components for ease of use and performance. This introduces complexity to the project. In contrast, if you start with Spark with default HDFS, you generally need far fewer workarounds. You also get more coding flexibility — you don’t have to stick to Java like with Hadoop.
Spark advantages in machine learning
Spark is hands down the more popular technology for machine learning use cases. It integrates better with ML environments and offers more features and abstractions for ML.
Hadoop does not offer any native ML libraries. Traditionally, Hadoop users turned to Apache Mahout for machine learning. Mahout was initially built on Hadoop’s MapReduce, but due to the limitations of MapReduce for iterative algorithms, Mahout has since moved away from Hadoop to Spark. When people talk about using Hadoop for ML, they are mostly referring to Spark running over HDFS!
MLlib is Spark’s scalable machine learning library. It includes a wide range of algorithms and tools for common machine-learning tasks. MLlib provides a high-level DataFrame-based API that facilitates the construction of machine learning workflows, including feature extraction, model training, and evaluation. MLlib can automate and manage complex machine learning workflows.
Comprehensive ML functionality
MLlib is a very comprehensive solution for ML developers. For example, it includes:
- Algorithms like logistic regression, decision trees, random forests, gradient-boosted trees, and support vector machines (SVMs).
- Advanced clustering algorithms like Gaussian mixture models (GMM) and Latent Dirichlet Allocation (LDA).
- Feature Transformation tools for scaling, normalization, tokenization, encoding, etc.
- Dimensionality reduction techniques like Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).
- Collaborative filtering like Alternating Least Squares (ALS) for building recommendation systems.
- Metrics and utilities for evaluating model performance like AUC and others.
Advanced integrations
You can use MLlib and other Spark tools to perform data processing and model development in the same environment.
MLlib integrates with Spark SQL for easy querying and transformation, especially if you require ETL to integrate data from various sources for model training.
MLlib can be used with Spark Streaming to build real-time machine learning models. This is useful when your model must continuously adapt to new data, such as in fraud detection or online recommendations.
You can integrate MLlib with Spark’s GraphX for graph-based machine learning, such as social network analysis and recommendation systems.
Performance
Spark’s in-memory capabilities are well-suited for iterative algorithms like logistic regression, k-means clustering, and gradient boosting, which are common in machine learning. You can train models with massive datasets distributed across hundreds of clusters. Scaling your ML tasks will not impact ML pipeline performance.
Spark — challenges and solutions
Even though Spark is the preferred data engine for ML, it comes with its complexities. Determining the right configuration for a Spark cluster is challenging. Getting the promised performance without the right node setup for your data is hard. Continuous cluster monitoring is necessary to deal with performance bottlenecks.
Given Spark’s costs, scaling clusters with workloads is more efficient than allocating excess unused resources. In some scenarios, cluster provisioning and de-provisioning can become a full-time job. Even with predictable workloads, Spark’s security management and regular maintenance tasks like software updates and patching take away time from ML work.
ML engineers want to focus on data transformations and avoid dealing with Spark operational complexities. That’s where managed Spark can help.
Managed Spark
Managed Spark abstracts Spark’s operational complexity so ML engineers focus on data processing tasks over infrastructure. You get:
- Single click cluster provisioning — use GUI, CLI, or familiar IDE and Notebooks to access your Spark environment.
- Auto scaling — use Spark with consistent performance without worrying about resource allocation.
- Monitoring and logging — ongoing cluster health and performance fully managed for you.
- Security and compliance — pre-configured security settings, including encryption and access control, for compliance with industry standards.
Managed Service for Spark, a part of Nebius platform, is currently in preview mode. Request access and try Spark for free to see how it can accelerate your ML application development.
Conclusion
Hadoop is the original technology that laid the foundation for big data processing. You can still use it with other technologies for modern data engineering use cases. Its storage layer, HDFS, allows you to read and store massive data files in a distributed environment.
Spark is a processing engine that has significantly improved Hadoop’s processing capabilities thanks to in-memory computation and DAG implementation. Spark can run on HDFS or on another compatible storage layer to give 100X times faster processing. It also offers specialized libraries like SparkSQL, SparkMLlib, Spark Streaming, and GraphX for modern data use cases.
Thanks to advanced capabilities, Spark is the preferred choice for ML training at scale. Managed Spark is the solution to avoid Spark’s operational complexities and enjoy all its benefits in ML.
Explore Nebius AI
