What is Hadoop? Key Concepts, Architecture, and Applications

1
Hadoop Revolutionizing Big Data Processing
Hadoop is an open-source software framework that
enables the distributed processing of large
datasets across clusters of computers. It
provides a reliable and scalable platform for
data storage and analysis, empowering
organizations to gain valuable insights from
their ever-growing data.
2
Key Concepts of Hadoop
Distributed Processing
Fault Tolerance
Scalability
Hadoop divides data and computations across
multiple nodes, allowing for parallel processing
and improved efficiency.
Hadoop automatically detects and handles hardware
failures, ensuring data integrity and continuous
operations.
Hadoop's architecture allows for easy expansion
by adding more nodes, enabling the handling of
ever-increasing data volumes.
3
Hadoop Architecture
HDFS
1
Hadoop Distributed File System (HDFS) provides
reliable and scalable data storage across the
cluster.
MapReduce
2
The MapReduce programming model allows for
distributed data processing and analysis.
YARN
3
Yet Another Resource Negotiator (YARN) manages
the computational resources within the Hadoop
cluster.
4
Hadoop Ecosystem Components
Apache Hive
Apache Spark
Apache Kafka
A data warehousing solution that provides
SQL-like querying capabilities on top of Hadoop.
An in-memory data processing engine that offers
faster and more flexible analytics compared to
MapReduce.
A distributed streaming platform for building
real-time data pipelines and applications.
Apache Sqoop
A tool for efficiently transferring data between
Hadoop and structured data stores.
5
Hadoop Distributed File System (HDFS)
Fault-tolerant Storage
Scalable Architecture
Streaming Data Access
1
2
3
HDFS provides redundant storage of data across
multiple nodes, ensuring data resilience.
HDFS can scale to handle petabytes of data and
thousands of nodes in a cluster.
HDFS is optimized for high-throughput access to
data, enabling efficient batch processing.
Compatibility
4
HDFS is compatible with various Hadoop ecosystem
components for seamless integration.
6
MapReduce Programming Model
Map
Processes input data and generates key-value
pairs.
Shuffle
Rearranges the data based on the generated keys.
Reduce
Aggregates the data and produces the final output.
7
Hadoop Applications and Use Cases
Data Analytics
Machine Learning
Analyzing large and complex datasets for business
intelligence and decision-making.
Training and deploying machine learning models on
massive amounts of data.
Internet of Things (IoT)
Log Analysis
Processing and analyzing sensor data from
connected devices in real-time.
Aggregating and analyzing log data from various
sources for troubleshooting and security.
8
Benefits and Challenges of Hadoop
Benefits
Challenges
- Cost-effective data storage and processing -
Scalable and fault-tolerant architecture -
Flexible and adaptable to diverse data types -
Supports real-time and batch processing
- Complexity in setup and configuration - Steep
learning curve for developers - Data security and
governance concerns - Resource management and
optimization
9
To learn more about Hadoop
Visit the website What is Hadoop? Key Concepts,
Architecture, and its Applications
(techgabbing.com)