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Collection Of Popular Big Data Tools On The Basis Of Popularity, Usefulness And Features
- HDFS: Hadoop Distributed File System, oriented at working with huge-scale bandwidth
- MapReduce: A highly configurable model for Big Data processing
- YARN: A resource scheduler for Hadoop resource management
- Hadoop Libraries: The needed glue for enabling third party modules to work with Hadoop
- Great horizontal scalability
- Built-in fault-tolerance
- Auto-restart on crashes
- Clojure-written
- Works with Direct Acyclic Graph(DAG) topology
- Output files are in JSON format
- Great liner scalability
- Simplicity of operations due to a simple query language used
- Constant replication across nodes
- Simple adding and removal of nodes from a running cluster
- High fault tolerance
- Built-in high-availability
- Stores any type of data, from text and integer to strings, arrays, dates and boolean
- Cloud-native deployment and great flexibility of configuration
- Data partitioning across multiple nodes and data centers
- Significant cost savings, as dynamic schemas enable data processing on the go
- R can run inside the SQL server
- R runs on both Windows and Linux servers
- R supports Apache Hadoop and Spark
- R is highly portable
- R easily scales from a single test machine to vast Hadoop data lakes
- Built-in support for ACID transactions
- Cypher graph query language
- High-availability and scalability
- Flexibility due to the absence of schemas
- Integration with other databases
- Clustering
- Classification
- Normalization
- Regression
- Programming primitives for building custom algorithms
- Program once, use anywhere
- Reuse the existing infrastructure for new projects
- No reboot or deployment downtime
- No need for backups or time-consuming updates