Hadoop and Spark, both developed by the Apache Software Foundation, are widely used open-source frameworks for big data architectures.
We are right at the heart of the Big Data phenomenon, and companies can no longer ignore the impact of data on their decision-making.
As a reminder, data considered as Big Data meets three criteria: velocity, volume, and variety. However, you cannot process Big Data with traditional systems and technologies.
To address this issue, the Apache Software Foundation has offered the most widely used solutions, namely Hadoop and Spark.
However, people who are new to Big Data processing often struggle to understand these two technologies. To dispel all doubts, discover in this article the key differences between Hadoop and Spark and when you should choose one or the other or use them together.
Hadoop
Hadoop is a utility software composed of several modules forming an ecosystem for Big Data processing. The principle used by Hadoop for this processing is the distributed distribution of data for parallel processing.
The Hadoop distributed storage system consists of multiple ordinary computers, forming a cluster of multiple nodes. Adopting this system allows Hadoop to efficiently process the enormous amount of data available by performing multiple tasks simultaneously, quickly and efficiently.
Data processed with Hadoop can take various forms. They can be structured, such as Excel spreadsheets or tables in a conventional database. These data can also be presented in a semi-structured manner, such as JSON or XML files. Hadoop also supports unstructured data such as images, videos, or audio files.
Main Components
The main components of Hadoop are as follows:
- HDFS (Hadoop Distributed File System) is the system used by Hadoop for distributed data storage. It consists of a master node containing cluster metadata and several slave nodes where the data itself is stored.
- MapReduce is the algorithmic model used to process this distributed data. This design model can be implemented using various programming languages, such as Java, R, Scala, Go, JavaScript, or Python. It runs in parallel on each node.
- Hadoop Commonin which several utilities and libraries support other Hadoop components.
- YARNis an orchestration tool that manages the resources of the Hadoop cluster and the workload executed by each node. It also supports the implementation of MapReduce since version 2.0 of this framework.
Apache Spark
Apache Spark is an open-source framework originally created by computer scientist Matei Zaharia as part of his doctoral work in 2009. It later joined the Apache Software Foundation in 2010.
Spark is a distributed computing and data processing engine spread across multiple nodes. The main feature of Spark is that it performs in-memory processing, meaning it uses RAM to cache and process large distributed data in the cluster. This gives it better performance and much higher processing speed.
Spark supports various tasks, including batch processing, real-time stream processing, machine learning, and graph computation. You can also process data from various systems such as HDFS, RDBMS, or even NoSQL databases. Spark can be implemented using several languages like Scala or Python.
Main Components
The main components of Apache Spark are as follows:
- Spark Core is the general engine of the entire platform. It is responsible for task scheduling and distribution, coordination of input/output operations, or recovery in case of failure.
- Spark SQL is the component providing the RDD (Resilient Distributed Dataset) schema that supports structured and semi-structured data. It optimizes the collection and processing of structured data by running SQL or providing SQL engine access.
- Spark Streaming allows continuous data analysis. Spark Streaming supports data from various sources such as Flume, Kinesis, or Kafka.
- MLib, Apache Spark’s built-in library for machine learning. It provides multiple machine learning algorithms and tools for creating machine learning pipelines.
- GraphX combines a set of APIs for graph modeling, computation, and analysis within a distributed architecture.
Hadoop vs. Spark: Differences
Spark is a Big Data computing and data processing engine. In theory, it is somewhat similar to Hadoop MapReduce but much faster since it operates in-memory. So, what sets Hadoop and Spark apart?
Certainly, here’s a table comparing Hadoop and Spark based on their key characteristics:
| Feature | Hadoop | Spark |
|---|---|---|
| Processing | Batch processing | In-memory processing |
| Storage | HDFS (Hadoop Distributed File System) | Utilizes HDFS from Hadoop |
| Speed | Slower | 10 to 1000 times faster |
| Supported Languages | Java, Python, Scala, R, Go, JavaScript | Java, Python, Scala, R |
| Fault Tolerance | More tolerant due to continuous data replication | Less tolerant, relies on Resilient Distributed Datasets (RDDs) |
| Cost | Less expensive | More expensive in terms of RAM |
| Use Cases | Better for batch processing | Better for real-time and unstructured data processing |
| Scalability | Highly scalable, can add machines as needed | Less scalable, relies on other frameworks like HDFS |
| Suitable for | Nightly batch processing, handling large datasets | Real-time interactive data analysis, data migration, and ingestion |
Sum up
Hadoop is a good solution if processing speed is not critical. For example, if data processing can be done overnight, it makes sense to consider using Hadoop’s MapReduce.
Hadoop allows you to offload large datasets from data warehouses, where processing is comparatively difficult, as Hadoop’s HDFS provides organizations with a better way to store and process data.
Spark is suitable for:
- Spark’s Resilient Distributed Datasets (RDDs) enable multiple in-memory mapping operations, making Spark a preferred option for real-time interactive data analysis.
- Spark’s in-memory processing and support for distributed databases like Cassandra or MongoDB are excellent for data migration and ingestion, especially when data is extracted from a source database and sent to another target system.
Using Hadoop and Spark Together
You often have to choose between Hadoop and Spark; however, in most cases, it is not necessary to choose because these two frameworks can coexist and work together very well. In fact, the main reason for developing Spark was to improve Hadoop rather than replace it.
As we’ve seen in the previous sections, Spark can be integrated with Hadoop using its HDFS storage system. Both allow faster data processing in a distributed environment. Similarly, you can allocate data on Hadoop and process it using Spark, or run tasks in Hadoop MapReduce.
Conclusion
Hadoop or Spark? Before choosing the framework, you must consider your architecture, and the technologies that compose it should align with the goal you want to achieve. Furthermore, Spark is fully compatible with the Hadoop ecosystem and seamlessly works with the Hadoop Distributed File System and Apache Hive.
