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.
distributed-computing
big-data-processing
data-storage
Apache Hadoop: Open Source Big Data Framework
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.
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.
Apache Hadoop Features
Features
- Distributed storage and processing of large datasets
- Fault tolerance
- Scalability
- Flexibility
- Cost effectiveness
Pricing
- Open Source
Pros
Handles large amounts of data
Fault tolerant and reliable
Scales linearly
Flexible and schema-free
Commodity hardware can be used
Open source and free
Cons
Complex to configure and manage
Requires expertise to tune and optimize
Not ideal for low-latency or real-time data
Not optimized for interactive queries
Does not enforce schemas
Official Links
The Best Apache Hadoop Alternatives
Top Ai Tools & Services and Big Data and other similar apps like Apache Hadoop
Here 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 key...
Apache Flink
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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 Java, Scala,...
Dispy
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Disco MapReduce
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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 and enrichment.Real-time...
