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How Hadoop Tools improvises Big Data?
- A Big data developer is liable for the actual coding/programming of Hadoop applications. Mentioned below is some information on Hadoop architecture
- It includes the variety of latest Hadoop features and tools
- Apache Hadoop enables excessive data to be streamlined for any distributed processing system over clusters of computers using simple programming models.
- Hadoop has two chief parts a data processing framework and a distributed file system for data storage.
- It stocks large files in the range of gigabytes to terabytes across different machines.
- Hadoop makes it easier to run applications on systems with a large number of commodity hardware nodes.
- ETL tool boosts developer productivity with a rich set of features including:
- Its graphical integrated development environment
- Drag-and-drop job design
- More than 900 components and built-in connectors
- Robust ETL functionality: string manipulations, automatic lookup handling
- Graphical extract-transform-load (ETL) designing system
- Powerful orchestration capabilities
- Complete visual big data integration tools
- Ad hoc queries
- Indexing
- Replication
- Load balancing
- Aggregation
- Server-side JavaScript execution
- Capped collections
- Index type including compaction and Bitmap index as of 0.10
- Variety of storage types such as plain text, RCFile, HBase, ORC, and others
- Operating on algorithms including DEFLATE, BWT, snappy, etc.
- Parallel import/export
- Import results of SQL query
- Connectors for all major RDBMS Databases
- Kerberos Security Integration
- Support for Accumulate
- Oracle Data Miner tool is an extension to Oracle SQL Developer, work directly with data inside the database using
- Graphical â??drag and dropâ?? workflow and component pallet
- Oracle Data Miner workflows capture and document the user's analytical methodology
- Oracle Data Miner can generate SQL and PL/SQL scripts
- Linear and modular scalability
- Convenient base classes for backing Hadoop
- Easy to use Java API for client access
- Block cache and Bloom Filters for real-time queries
- Query predicate push down via server-side Filters
- Support for exporting metrics
- Pig Tool has the following key properties:
- It is trivial to achieve parallel execution of simple
- Permits the system to optimize their execution automatically
- Users can create their own functions to do special-purpose processing.
- Managing and configuration of nodes
- Implement reliable messaging
- Implement redundant services
- Synchronize process execution