Apache Hadoop

Apache Hadoop is an open source framework for storing and processing big data in a distributed computing environment. It provides massive storage and high bandwidth data processing across clusters of computers.

Apache Hadoop alternatives ➤

What is Apache Hadoop?

Apache Hadoop is an open source software framework for distributed storage and distributed processing of very large data sets on computer clusters. Hadoop was created by the Apache Software Foundation and is written in Java.

Some key capabilities and features of Hadoop include:

Massive scale - Hadoop enables distributed processing of massive amounts of data across clusters made up of commodity hardware.
Fault tolerance - Data and application processing is redundantly distributed across the cluster so that failures of individual computers does not result in loss of data or interrupt application processing.
Flexibility - New nodes can be added as needed and Hadoop will automatically distribute data and processing across the new nodes.
Low cost - Hadoop runs on commodity hardware reducing capital expenditure and operational costs compared to traditional data warehousing solutions.
Variety of data sources - Hadoop allows ingestion of structured and unstructured data from a wide variety of sources.

Some common uses cases of Hadoop include log file analysis, social media analytics, financial analytics, media analytics, risk modeling, recommendation systems, fraud detection, and more.

Overall, Apache Hadoop enables cost-effective and scalable data processing for big data applications across a cluster of computers.

Official Links

Official Website
hadoop.apache.org

The Best Apache Hadoop Alternatives

Top Apps like Apache Hadoop

Amazon Kinesis, Apache Flink, Apache Spark, dispy, Disco MapReduce, Upsolver are some alternatives to Apache Hadoop.

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Amazon Kinesis

Amazon Kinesis is a cloud-based managed service offered by Amazon Web Services (AWS) to allow for real-time streaming data ingestion and processing. It is designed to easily ingest and process high volumes of streaming data from multiple sources simultaneously, making it well-suited for real-time analytics and big data workloads.Some...

Apache Flink

Apache Flink is an open-source stream processing framework developed by the Apache Software Foundation. It is designed to perform high-throughput and low-latency data processing over unbounded and bounded data streams.Some key capabilities and features of Apache Flink include:Stateful stream processing - Flink maintains state across stream events and...

Apache Spark

Apache Spark is an open-source distributed general-purpose cluster-computing framework designed for large-scale data processing and analytics. Some key points about Apache Spark:It provides a fast and general engine for large-scale data processing that runs workloads 100x faster than Hadoop MapReduce in memory, or 10x faster on disk.It supports...

Dispy

Dispy is an open-source distributed and parallel computing framework for Python. It allows easy distribution of Python computations across multiple processors and computers. Some key features:It can distribute Python functions and scripts across nodes/processors for parallel executionIt has support for computation intensive jobs as well as interactive controlIt...

Disco MapReduce

Disco is an open-source MapReduce framework originally developed by Nokia for distributing the computing workloads of extremely large data sets across clusters of commodity hardware. It is designed to be scalable, fault-tolerant and easy to use.Some key features of Disco MapReduce include:Automatic parallelization and distribution of MapReduce jobsFault...

Upsolver

Upsolver is a cloud-native data lake platform optimized for streaming analytics workloads. It allows organizations to quickly and easily build, operate and scale streaming data pipelines and applications without having to manage infrastructure.Key capabilities and benefits of Upsolver include:No-code UI to build streaming pipelines with SQL, data transformation...