2. Introduction
What is Hadoop?
Hadoop Applications
Hadoop Architecture
Importance
Advantages
Disadvantages
When to use Hadoop?
Reference
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3. Hadoop is an Apache open source framework
written in java that allows distributed
processing of large datasets across clusters of
computers using simple programming models.
A Hadoop frame-worked application works in
an environment that provides distributed
storage and computation across clusters of
computers.
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4. Hadoop is sub-project of Lucene (a collection of
industrial-strength search tools), under the
umbrella of the Apache Software Foundation.
Hadoop parallelizes data processing across
many nodes (computers) in a compute cluster,
speeding up large computations and hiding I/O
latency through increased concurrency.
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5. Making Hadoop Applications More Widely
Accessible
A Graphical Abstraction Layer on Top of Hadoop
Applications
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7. Ability to store and process huge amounts of
any kind of data, quickly
Computing power
Fault tolerance
Flexibility
Low cost
Scalability
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8. Scalable
Cost effective
Flexible
Fast
Resilient to failure
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9. Security Concerns
Vulnerable By Nature
Not Fit for Small Data
Potential Stability Issues
General Limitations
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12. Ambari, Zookeeper (managing & monitoring)
HBase, Cassandra (database)
Hive, Pig (data warehouse and query
language)
Mahout (machine learning)
Chukwa, Avro, Oozie, Giraph, and many more
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13. Generally, always when “standard tools” don’t
work anymore because of sheer data size
(rule of thumb: if your data fits on a regular
hard drive, your better off sticking to
Python/SQL/Bash/etc.!)
Aggregation across large data sets: use the
power of Reducers!
Large-scale ETL operations (extract,
transform, load)
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