6 Big Data Applications That Sparked The Hadoop Ecosystem

Introduction

Hadoop is a framework for storing and processing large datasets in distributed clusters of computers. In the past few years, it has become one of the most popular Big Data platforms. The Hadoop ecosystem has grown to include many other applications built on top of Hadoop or using APIs provided by Hadoop ecosystem components such as Hive, Pig and Oozie. These applications help developers build real-time streaming applications, perform advanced text search functions, conduct interactive analytic queries and more.

Hadoop has become the de facto data storage and processing platform for many enterprises.

Hadoop is a framework for storing and processing large amounts of data. It’s open source, free, and used by many companies to store and process their data. The Hadoop ecosystem has grown rapidly over the years with many applications being developed by third parties to complement the core Hadoop platform.

Spark, a fast cluster-computing framework for analyzing Big Data, was originally built for Hadoop.

Spark, a fast cluster-computing framework for analyzing Big Data, was originally built for Hadoop. Spark is similar to MapReduce in that it can run in a distributed environment and it can be used for iterative algorithms (like Hive). However, Spark is orders of magnitude faster than MapReduce because it uses memory more efficiently and allows you to process datasets that are too large to fit on one machine’s memory.

Spark was developed by Matei Zaharia at UC Berkeley in 2010; he then founded Databricks Inc., which provides support services related to Spark development projects.

Kafka is a publish/subscribe messaging system that lets developers build real-time streaming applications.

Kafka is a publish/subscribe messaging system that lets developers build real-time streaming applications. It’s used for building data pipelines, integrating systems and processing data streams.

Kafka stores messages in partitions, which are distributed across multiple machines in order to handle large amounts of data. These partitions can be replicated for fault tolerance or broadcasted across different regions for high availability. Messages are delivered to consumers as soon as they’re available from the log (commit log).

The most common use case for Kafka is publishing events from various sources such as web servers, mobile applications and IoT devices into one central location where they can be consumed by applications such as Storm or Spark Streaming using the Kafka Connect API

Solr is a search engine used to query and navigate large collections of documents.

Solr is a search engine used to query and navigate large collections of documents. It’s used in a number of applications, including web search, enterprise content management (ECM) systems and document clustering.

Solr is designed for high performance and features like real-time indexing updates and replication across multiple servers. It also supports geospatial queries that allow users to search for information based on location data from latitude/longitude pairs or ZIP codes/postal codes within the database itself rather than using additional software like PostGIS or Elasticsearch for this purpose.

This makes it ideal for applications with large amounts of unstructured data such as:

• Social media analytics
• E-commerce analysis
• Healthcare analytics

Cassandra is a distributed database that can scale up to millions of nodes and trillions of rows.

Cassandra is a distributed database that can scale up to millions of nodes and trillions of rows. Cassandra was developed by Facebook, inspired by Google’s Bigtable paper, and released as an open source project in 2008. It’s designed to handle large amounts of structured, semi-structured, and unstructured data across commodity hardware or cloud infrastructure.

Storm is an open source real-time computation system. Unlike MapReduce, which runs batch jobs in Hadoop, Storm runs computations in real time on large data streams.

Storm is an open source real-time computation system. Unlike MapReduce, which runs batch jobs in Hadoop, Storm runs computations in real time on large data streams. It can process a million messages per second and has been used to process IoT data at Yahoo!.

Storm is used for applications such as:

• Real-time analytics
• Streaming analytics (analyzing live events)

There are many other applications growing out of the Hadoop ecosystem

The Hadoop ecosystem has spurred many other applications. The most notable include Spark, Kafka, Solr and Cassandra.

These applications are used in data analytics and Big Data processing to help companies make better-informed decisions. For example, you can use Spark as a fast cluster-computing framework for analyzing Big Data with ease. It’s designed to be compatible with existing tools such as Hive or Impala so that developers don’t have to learn new languages when they want to use it on top of Hadoop clusters.

Conclusion

This is just a sample of some of the most popular applications that have grown out of Hadoop. There are many others, including: Pig, Hive, Spark SQL and more.