Elasticsearch and Hadoop have great use as search engines and database valuations. When it comes to bulk uploading, Hadoop overtakes, and Elasticsearch lags.
Hadoop, along with HBase, does not support analytical and advanced searches. Elasticsearch is most reliable for small and medium-sized examinations.
Also, Elasticsearch is dependent on JavaScript Object Notation, and Hadoop is developed on MapReduce. Elasticsearch Analytics is more advanced as compared to Hadoop.
Key Takeaways
- Elasticsearch excels at real-time data processing and search, whereas Hadoop focuses on large-scale data storage and batch processing.
- Elasticsearch is built on the Apache Lucene framework, while Hadoop is an open-source framework based on the MapReduce programming model.
- Hadoop offers greater scalability for massive data sets than Elasticsearch, better suited for smaller data sets requiring low-latency responses.
Elasticsearch vs Hadoop
Elasticsearch is a search and analytics engine designed for fast and scalable search, retrieval, and analysis of structured and unstructured data. Hadoop is a distributed processing framework designed to handle large volumes of data across clusters of commodity hardware.
Elasticsearch is Lucene’s library-based search engine. Elasticsearch is created in Java and contains JavaScript Object Notation.
Elasticsearch is compatible with all operating software loaded with Java VM. Also, Elasticsearch can be utilized as an analytics framework.
Elasticsearch has high limits with a massive bulk upload. Also, Elasticsearch provides a detailed query on Digital Subscriber Line mainly based on JavaScript Object Notation.
Hadoop is an open-source utility software that promotes computation with lots of bulk data. Hadoop initiated its journey on 1st April 2006. Doug Cutting and Mike Cafarella laid the foundation of Hadoop.
Hadoop utilizes MapReduce (programming model) for analyzing huge data collections. Also, Hadoop is administered as a gadget to store data and run applications in groups.
Comparison Table
| Parameters of Comparison | Elasticsearch | Hadoop |
|---|---|---|
| About | Elasticsearch is an “Open Source, Distributed, RESTful Search Engine. | Hadoop is an Open-source software for reliable, scalable, distributed computing. |
| Usage | Elasticsearch is mainly used as a search engine. | Hadoop is used to evaluate a large quantity of data. |
| Function | Elasticsearch delivers a full query on Digital Subscriber Line based on JavaScript Object Notation. | Hadoop utilizes MapReduce (programming model) for analyzing huge data collections. |
| Capability | Elasticsearch can be operated as a Full-text search engine and can also be utilized as an analytics framework. | Hadoop is utilized as a gadget to reserve data and run applications in groups. |
| Compatible | Elasticsearch is compatible with all operating software loaded with Java VM | Hadoop is compatible with Unix, Linux, and Windows. |
What is Elasticsearch?
Elasticsearch is well known as a search engine that is mainly based on the Lucene library. Elasticsearch was first introduced on 8th February 2010.
The primary and structural programming language is Java. Also, Elasticsearch has an HTTP-based web interface and JavaScript Object Notation documents.
Elasticsearch was assembled in Java and is available in .NET, Java, PHP, Ruby, and Python. Elasticsearch has been authorized by the dual license as the Elastic license and a source open Server Side Public License.
According to the ranking marked by DB-Engines, Elasticsearch ranks as the most prominent search engine. Originally, Shay Banon developed ‘Compass’ in 2004,, which was argued as a precursor of Elasticsearch.
After updating the Compass as Elasticsearch, Shay Banon formulated a familiar interface,, Javascript Object Notation, which is acceptable over HyperText Transfer Protocol.
JSON was more suitable than Java as a better option for programming language. The initial version of Elasticsearch was introduced in February 2010.
Furthermore, the name Elasticsearch was changed to Elastic in the year 2015. The primary usage of Elasticsearch is to search any kind of document.
Elasticsearch is developed with the help of Logstash, Kibana, and Beats. Also, Logstash is a data assortment and log-parsing engine, whereas Kibana is a visualization and analytics forum.
What is Hadoop?
On 1st April 2006, Doug Cutting and Mike Cafarella laid the foundations of Hadoop. Apache Software Foundation developed this open-source software.
The hadoop core is mainly divided into two segments. One is the storage segment, and the other is the processing segment.
The Hadoop Distributed File System (HDFS) is the primary storage segment, and MapReduce; the programming model acts as the processing segment.
Hadoop mainly functions by splitting the bulk files into smaller blocks and circulates these files across nodes in assortments. It further transfers assorted code into nodes to filter the data in parallel.
A small Hadoop assortment comprises multiple agent nodes and a single master. Furthermore, the controller node consists of a DataNode, Job Tracker, NameNode, and Task Tracker.
Also, the worker node performs the tasks of both TaskTracker and DataNode. However, Hadoop also accesses computer-only and data-only slave modes.
While talking about the bulk clusters, Hadoop Distributed File System nodes are administered through the NameNode server to analyze the file system index.
The subordinate NameNode is used to develop the snapshots, which prevent data loss and the file system’s corruption. According to G2.com, Hadoop is rated 4.3 out of 5 and is readily available in the market.
Also, G2.com is a renowned website for reviewing software.
Main differences between Elasticsearch and Hadoop
- Elasticsearch works on the principles of JavaScript Object Notation, whereas Hadoop works on the MapReduce principle.
- While looking at the programming language, Elasticsearch has a variety of programming languages, such as Ruby, Lua, and Go, whereas Hadoop doesn’t have this programming language.
- The Elasticsearch proves its compatibility with all Java VM software, whereas Hadoop is compatible with Linux, Windows, and Unix.
- Elasticsearch is mainly used for batch processing, whereas Hadoop is used for real-time results and queries.
- Elasticsearch has a limit in uploading bulk data, whereas Hadoop offers bulk data upload.
Last Updated : 13 July, 2023
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Sandeep Bhandari holds a Bachelor of Engineering in Computers from Thapar University (2006). He has 20 years of experience in the technology field. He has a keen interest in various technical fields, including database systems, computer networks, and programming. You can read more about him on his bio page.
The detailed comparison of Elasticsearch and Hadoop enriches the understanding of how they function. This is a good reference point for anyone working with these technologies.
The post does a great job comparing Elasticsearch and Hadoop. It was fascinating to see how these two technologies differ and excel in various aspects.
Absolutely fascinating. It’s valuable to see such detailed comparisons.
This article presents a very informative comparison between Elasticsearch and Hadoop. I loved the technical details provided about each of them. I learned a lot from this post.
I completely agree. This informational piece is very valuable to anyone seeking to understand the differing capabilities of these tools.
The article gives a comprehensive view of the differences between Elasticsearch and Hadoop. It’s easy to understand and explains the capabilities of each technology in a clear manner.
The technical details provided are quite complex and may not be easily understood by everyone. Perhaps simplifying the explanations would benefit a wider audience.
This article seems to lean more towards promoting Elasticsearch over Hadoop. I feel like it could provide a more balanced perspective to truly help the readers make an informed choice about these technologies.
I agree with you, not entirely unbiased but still very informative.