Three software applications which can be utilised for Big Data and briefly explain the key characteristics of your chosen applications.

Three software applications which can be utilised for Big Data and briefly explain the key characteristics of your chosen applications.

Apache Hadoop. A distributed processing framework called Apache Hadoop allows large datasets to be processed across clusters of computers. The Hadoop Distributed File System or HDFS stores and processes large amounts of data by using a distributed processing framework called MapReduce. With Hadoop, large amounts of data can be processed across a large number of nodes with fault tolerance, scalability, and high availability. Finance, healthcare, and e-commerce are among the industries that use batch processing to process large datasets.

 

Apache Hadoop has the following characteristics:

Scalability: As data grows, Hadoop's scalability allows it to scale horizontally, making it ideal for dealing with large datasets.

Fault tolerance: Due to Hadoop's use of multiple nodes to replicate data, it provides fault tolerance and ensures the durability and reliability of data.

Distributed processing: With Hadoop, you can process large datasets efficiently by using a distributed processing framework called MapReduce (or Hadoop).

Flexibility: The Hadoop platform can be used for both structured and unstructured data, as well as for building custom big data solutions using other tools and technologies.

 

Apache Spark: It is an open-source framework for data processing that can perform in-memory operations, which makes it faster than traditional batch processing frameworks like Hadoop. Data processing tasks such as batch processing, streaming processing, machine learning, and graph processing can all be performed with this platform. A wide range of developers and data scientists can use Spark thanks to the APIs it provides in Java, Scala, Python, and R.

Apache Spark has the following characteristics:

In-memory processing: Comparatively to traditional disk-based processing frameworks, Spark stores intermediate data in memory and enables faster data processing.

Polyglot support: A variety of programming languages are supported by Spark, including Java, Scala, Python, and R. This makes Spark accessible to developers with different backgrounds in programming.

Fault tolerance: As a result of Spark's Resilient Distributed Dataset (RDD) abstraction, it provides built-in fault tolerance, which automatically restores data when nodes fail.

Advanced analytics: There is a rich set of library components for Spark that support machine learning (MLlib), graph processing (GraphX), and stream processing (Spark Streaming), making it suitable for many types of big data analytics projects.

 

Elasticsearch: Known for processing and analysing large volumes of data in real-time, Elasticsearch is an open-source distributed search engine. A RESTful API is provided for interacting with data based on the Lucene search engine. Large datasets generated by web applications, IoT devices, and social media are commonly analysed using Elasticsearch for log analytics, monitoring, and searching.

 

Among the key features of Elasticsearch are:

Real-time processing: The real-time indexing, searching, and analytics of large datasets are possible with elastic search since it is optimized for real-time data processing.

Distributed and scalable: In addition to the ability to scale horizontally as data grows, elastic search is designed to deploy across multiple nodes.

Full-text search capabilities: As well as supporting complex search queries, filtering, and aggregation, Elasticsearch provides powerful and flexible full-text search capabilities.

Data visualization: The Elasticsearch platform comes with integrated data visualization capabilities, like Kibana, which allows users to explore and analyse data interactively with interactive dashboards.