To implement a batch-processing big data system using Hadoop ecosystem components for analyzing large datasets. The project focuses on distributed storage, parallel processing, and extracting meaningful insights from structured and unstructured data sources.
Study Hadoop architecture including HDFS and MapReduce.
Install and configure a Hadoop cluster (single-node or multi-node).
Collect large datasets such as social media or sales data.
Load datasets into HDFS distributed storage.
Develop MapReduce programs for data aggregation.
Perform batch data processing tasks like word count and trend analysis.
Integrate Hive for SQL-like querying of large datasets.
Optimize job performance and resource allocation.
Implement data compression and partitioning strategies.
Monitor job execution using Hadoop utilities.
Store processed output in structured format.
Create summary reports and visualizations.
Ensure fault tolerance through replication mechanisms.
Compare performance with traditional database systems.
Document cluster setup and performance metrics.
