Hadoop

Website

hadoop.apache.org

Pricing URL

-

$ Details

Categories

#Databases #Relational Databases #Big Data #NoSQL Databases

ElasticSearch

Website

elastic.co

Pricing URL

Official ElasticSearch Pricing

$ Details

-

Categories

#Search Engine #Custom Search Engine #Custom Search #RESTful Search Engine #Search API

Hadoop features and specs

• Scalability
  Hadoop can easily scale from a single server to thousands of machines, each offering local computation and storage.
• Cost-Effective
  It utilizes a distributed infrastructure, allowing you to use low-cost commodity hardware to store and process large datasets.
• Fault Tolerance
  Hadoop automatically maintains multiple copies of all data and can automatically recover data on failure of nodes, ensuring high availability.
• Flexibility
  It can process a wide variety of structured and unstructured data, including logs, images, audio, video, and more.
• Parallel Processing
  Hadoop's MapReduce framework enables the parallel processing of large datasets across a distributed cluster.
• Community Support
  As an Apache project, Hadoop has robust community support and a vast ecosystem of related tools and extensions.

Possible disadvantages of Hadoop

• Complexity
  Setting up, maintaining, and tuning a Hadoop cluster can be complex and often requires specialized knowledge.
• Overhead
  The MapReduce model can introduce additional overhead, particularly for tasks that require low-latency processing.
• Security
  While improvements have been made, Hadoop's security model is considered less mature compared to some other data processing systems.
• Hardware Requirements
  Though it can run on commodity hardware, Hadoop can still require significant computational and storage resources for larger datasets.
• Lack of Real-Time Processing
  Hadoop is mainly designed for batch processing and is not well-suited for real-time data analytics, which can be a limitation for certain applications.
• Data Integrity
  Distributed systems face challenges in maintaining data integrity and consistency, and Hadoop is no exception.

ElasticSearch features and specs

• Scalability
  ElasticSearch is highly scalable, allowing you to handle large volumes of data and distribute indexing and search tasks across multiple nodes.
• Real-Time Data
  It provides real-time indexing and searching capabilities, making it suitable for applications that require up-to-the-minute data retrieval and analysis.
• Full-Text Search
  ElasticSearch is well-known for its powerful full-text search capabilities, enabling complex search queries and supporting a wide range of search options.
• Complex Query Support
  It offers a rich query language allowing for complex and nested searching with filters, aggregations, and more.
• Distributed Architecture
  ElasticSearch is designed to be distributed by nature, making it resilient to node failures and allowing data and search requests to be distributed across a cluster.
• Open Source
  ElasticSearch is open-source, offering flexibility and a large community of developers that contribute to its continuous improvement and support.
• Analytics
  Besides search, it also supports powerful analytics and visualization tools, especially when integrated with Kibana, its visualization dashboard.
• Integrations
  ElasticSearch can easily integrate with various data sources and frameworks, enhancing its usability across different applications.

Possible disadvantages of ElasticSearch

• Complexity
  Operating ElasticSearch can be complex, particularly when dealing with large-scale deployments, requiring specialized knowledge and expertise.
• Resource Intensive
  ElasticSearch can be resource-intensive, requiring significant amounts of RAM and CPU, which can be costly for large-scale operations.
• Consistency
  As a distributed system, ElasticSearch can sometimes face consistency issues, especially in scenarios involving partitions or network failures.
• Security
  Though security features are available, they often require additional configurations and are more robust in the paid versions, which can be a concern for open-source users.
• Cost
  While the core ElasticSearch software is open-source, scaling and additional features (like security, monitoring, and machine learning) are part of the paid Elastic Stack offerings.
• Learning Curve
  There is a steep learning curve associated with mastering ElasticSearch and its query DSL (Domain Specific Language), which can be a barrier for new users.
• Maintenance
  Properly maintaining an ElasticSearch cluster requires ongoing management, monitoring, and tuning to ensure optimal performance.
• Backup and Restore
  Managing backups and restores can be cumbersome and is not as straightforward as in some other databases or data storage solutions.

What is Big Data and Hadoop?

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What is Elasticsearch?

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