The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple
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Hadoop Application Architectures Mark Grover Ted Malaska Jonathan Seidman Gwe...jolangoldikp
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple
2. 01
02
03
Handles data storage across nodes, providing a distributed
file system for storing and accessing massive datasets.
A programming model for parallel data processing,
breaking down tasks into Map and Reduce phases for
efficient execution on distributed systems.
Manages job scheduling and resource allocation across the
Hadoop cluster, ensuring efficient utilization of resources
for parallel processing.
HDFS
MapReduce
YARN
Hadoop Architecture
3. Tracks metadata of files, including
file locations, permissions, and
other details, essential for
managing and accessing data
blocks.
Stores actual data blocks across the
cluster, replicating them for fault
tolerance and ensuring high
availability of data.
NameNode DataNode
Hadoop Distributed File System (HDFS)
4. Data is divided into key-value
pairs, where each key represents
a specific attribute or value, and
the corresponding value contains
associated data.
Aggregates outputs from the Map
phase, combining data with the
same key to perform calculations
or summaries, resulting in
reduced output.
Map Phase Reduce Phase
MapReduce
5. 01
02
Coordinates resources and schedules jobs across the
Hadoop cluster, ensuring optimal resource utilization and
efficient processing of distributed tasks.
Additional tools that complement Hadoop, providing data
processing, ingestion, and query functionalities for different
data formats and tasks.
YARN
Pig, Hive, Flume
YARN and Additional
Components
6. Handles petabytes of data on
clusters, scaling to handle massive
datasets and providing a
distributed platform for processing
large volumes of data.
Utilizes commodity hardware,
making it cost-effective for storing
and processing large amounts of
data, compared to expensive
specialized systems.
Supports a wide range of data
formats, providing flexibility in
handling different data types and
structures, facilitating efficient
data analysis across various
domains.
Advantages of Using Hadoop
7. Analyzing customer behavior for
personalized marketing,
uncovering patterns and insights
for targeted campaigns.
Processing data from millions of
sensors, extracting valuable
information for real-time
monitoring and analysis in
various industries.
Building models from large
datasets for predictive analytics,
enabling predictions and insights
based on historical data and
trends.
Data Science IoT Data Processing Machine Learning
Real-World Applications
of Hadoop
8. Requires expert setup and maintenance,
demanding technical expertise for installation,
configuration, and ongoing management.
Experiences a shift towards newer cloud-native
platforms, as these platforms offer greater
scalability, flexibility, and managed services.
Complexity
Cloud Adopton
AI can help to recover data that has been lost
or corrupted due to a cyber attack.
Speed issues
Challenges with Hadoop
9. 01
02
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Integration with cloud services, enabling compatibility with
cloud storage and processing for enhanced scalability and
flexibility.
Emergence of alternatives, such as Databricks Lakehouse
and Spark, offering newer technologies and features for
data processing and analysis.
Innovations in YARN and HDFS, focusing on improving
efficiency, scalability, and performance for handling massive
datasets.
Future Trends in Hadoop
10. Revolutionized distributed
computing, providing a powerful
framework for handling large
datasets and enabling efficient
analysis of big data.
Faces limitations in meeting the
evolving need for real-time
processing and low latency,
demanding faster processing
capabilities for certain applications.
Embraces future developments,
focusing on enhancing scalability,
efficiency, and integration with
cloud services to meet the evolving
needs of big data analytics.
Conclusion and Hadoop’s Legacy
