Hadoop is an open-source framework which helps to manage huge volumes of data in a fraction of a second, where traditional methods fail. Support from multiple machines is required to execute the process simultaneously in a distributed way. Elasticsearch is the combination of Logstash and Kibana. Where Logstash is responsible for extracting data from all data sources, then Elasticsearch will analyze the data, and lastly, Kibana provides the actionable insights from it. This makes the applications more powerful for working with complex research demands or requirements. In this blog, Let us compare Hadoop and Elasticsearch and find out their differences. So let's dive into the topic.
Hadoop is an open-source Apache framework and is used for storing processes and analyzing data of enormous volume. Hadoop is Java-based and is not Online Analytical Processing. This is used in offline or batch processing. It is utilized by Google, Facebook, Twitter, Yahoo, Linked In and numerous others. In addition, it may be scaled simply by adding nodes within the cluster.
Elasticsearch is a Java-based NoSQL database. It's real-time analytics and distributed engine built to store logs. This is a very scalable document storage engine. Like MongoDB, ElasticSearch will store information in the form of a document. This allows users to run advanced queries to carry out in-depth analysis and store the data centrally. It is integrated into the RESTful API, which helps to execute the request and respond to the request.
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Following are the list of points that show the differences between Hadoop and ElasticSearch:
Architecture: Hadoop is a free software framework which follows the master-slave architecture to store and process data with MapReduce programming model and HDFS (Hadoop Distributed File System) accordingly. HDFS is a parallel file system with high performance that was designed for Big Data processing needs. However, Elasticsearch is based on the REST architecture and delivers API endpoints for performing CRUD operations on HTTP and performing cluster monitoring tasks. It enables us to manage, integrate, and query indexed data in a number of different ways.
Tool: Elasticsearch is a complete text search and analytic engine that is very scalable and distributed. It enables us in storing, searching and analyzing huge volumes of real-time data. While it is mainly used as a search engine, Elasticsearch can be used as an analytical framework through its powerful data storage and aggregation system. However, Hadoop is a strong distributed processing framework that started as a software project for supporting a web search engine and has become an ecosystem of applications and tools for analyzing huge volumes of data.
Use: Elasticsearch is a complete-text search engine that is its primary use. However, it is used as the analytics framework through its powerful aggregation system. It may be used as a powerful analysis engine to run all the queries, which would typically be executed in real-time in offline or a batch. It is responsible not only for search but for complex aggregations as well. However, Hadoop is primarily used as a tool for storing data and running applications on commodity hardware clusters Utilizing the most trusted storage system in the world, HDFS.
Principle: Elasticsearch offers a JSON-based complete query DSL for exposing Lucene's power for reading and writing queries in the simplest way. The majority of NoSQL data stores rely on JSON for data storage because the JSON format is extremely flexible, concise, and easily understandable. However, Hadoop is built on the MapReduce programming model to process enormous datasets on commodity hardware clusters. In Hadoop, MapReduce is a programming paradigm which is used for accessing large amounts of data stored on thousands of servers within a Hadoop cluster.
Setup: The installation of Hadoop into a production environment is simple and scalable. Configuration of Hadoop clusters is smoother compared to ElasticSearch. However, implementation of ElasticSearch requires proactively estimating the amount of data. Additionally, the initial configuration needs a trial and hit method. There are many setting requirements that must be changed when the quantity of data increases. The configuration of the ElasticSearch cluster is more susceptible to errors.
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Complexity: In Hadoop, Working with MapReduce is relatively complex; however, in ElasticSearch, DSL based on JSON is easily understandable and implemented.
Working Principle: Hadoop is Based on MapReduce while Elasticsearch is Based on JSON and, therefore, Domain-specific language
Schema: Hadoop uses NoSQL technology, which makes it easy to upload the data into any key-value format. However, ElasticSearch recommends that data should be in a generic key-value format prior to uploading.
Bulk Upload: In Hadoop, Bulk uploading is not a problem. However, there is a buffer limit for ElasticSearch, but it might be extended after analyzing the failure that occurred at that time.
Analytics Usage: Hadoop with HBase does not have such advanced and analytical search abilities as ElasticSearch. However, ElasticSearch Analytics is very advanced, and search queries have reached maturity.
Supported Programming Languages: Hadoop does not have various programming languages to support it. However, ElasticSearch is supported by many programming languages like Go, Ruby, Lua, etc.
Reliability: Hadoop operates reliably from the test environment to the production environment. However, ElasticSearch is reliable in an environment of small and medium-size. This is not part of a production environment, where there are a lot of data centres and clusters.
Preferred Usage: Hadoop is preferred for batch processing. However, ElasticSearch is preferred for real-time queries and results.
Finally, to choose one between Hadoop and ElasticSearch, it really depends on the type of data, the volume, and the use case you're dealing with. If the focus is on simple search and web analytics, then Elasticsearch is preferred. While there is a strong demand for scaling, a large amount of data and compatibility with the third-party tools, then Hadoop is preferred. But the integration of Hadoop with ElasticSearch opens up a new world for both big and heavy applications.
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In this blog, we have compared Hadoop and Elasticsearch and seen their differences. We hope you found this information helpful. If you are willing to know any information related to ElasticSearch, feel free to comment below.
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