Over time, the need to store, process, and analyze large amounts of data has increased. There are several distributed systems to deal with big data, but the most popular are *Spark* and *Hadoop*.

# Apache Spark

**Apache Spark** is an open-source, distributed, general-purpose, cluster-computing framework. It is the largest open-source project in data processing. Spark promises excellent performance and comes packaged with high-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing.

# Apache Hadoop

**Apache Hadoop** is an open-source framework that is a powerhouse when dealing with big data. It provides storage in the form of distributed file systems and equips users to process data in parallel. It’s a general-purpose form of distributed processing that has several components: *Hadoop Common*, *Hadoop Distributed File System (HDFS)*, *Hadoop MapReduce*, and *Hadoop Yet Another Resource Negotiator (YARN)*.

# Spark vs. Hadoop

*Spark* was released in 2014 and *Hadoop* came out in 2006. Both of these frameworks provide processing power to deal with big data, but there are some key differences between them. Here is a list of differences between *Spark* and *Hadoop*:

| **Spark**  | **Hadoop**  |
| - | - |
| Fast in-memory performance with reduced disk reading and writing operations.   | Slower performance, uses disks for storage, and depends on disk read and write speed.  |
| Suitable for iterative and live-stream data analysis. Works with #key# RDDs: Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. #key# and #key# DAGs: (Directed Acyclic Graph) DAG in Apache Spark is a set of Vertices and Edges, where vertices represent the RDDs and the edges represent the Operation to be applied on RDD. #key# to run operations.  | Best for batch processing. Uses MapReduce to split a large dataset across a cluster for parallel analysis.   |
| Tracks the RDD block creation process and can rebuild a dataset when a partition fails. *Spark* can also use a DAG to rebuild data across nodes.   | A highly fault-tolerant system that replicates data across nodes and uses them in case of an issue.   |
| A bit more challenging to scale because it relies on RAM for computations.  | Easily scalable by adding nodes and disks for storage.   |
| More user-friendly. Allows interactive shell mode. APIs can be written in Java, Scala, R, Python, andSpark SQL.   | More difficult to use with less supported languages. Uses Java or Python for MapReduce apps.  |

Spark vs. Hadoop

Spark offers in-memory performance suitable for iterative and live-stream data analysis, while Hadoop excels in fault-tolerant, disk-based batch processing.

Spark	Hadoop
Fast in-memory performance with reduced disk reading and writing operations.	Slower performance, uses disks for storage, and depends on disk read and write speed.
Suitable for iterative and live-stream data analysis. Works with RDDsResilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. and DAGs(Directed Acyclic Graph) DAG in Apache Spark is a set of Vertices and Edges, where vertices represent the RDDs and the edges represent the Operation to be applied on RDD. to run operations.	Best for batch processing. Uses MapReduce to split a large dataset across a cluster for parallel analysis.
Tracks the RDD block creation process and can rebuild a dataset when a partition fails. Spark can also use a DAG to rebuild data across nodes.	A highly fault-tolerant system that replicates data across nodes and uses them in case of an issue.
A bit more challenging to scale because it relies on RAM for computations.	Easily scalable by adding nodes and disks for storage.
More user-friendly. Allows interactive shell mode. APIs can be written in Java, Scala, R, Python, andSpark SQL.	More difficult to use with less supported languages. Uses Java or Python for MapReduce apps.