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8 Top Data Science Platform Developers in the World

Top data science tools and platforms providers across the world. Useful information for data science and data analytics developers.

Data Science is a combination of multiple skills. AI and Machine Learning are part of data science. You can create AI and Machine Learning products with data. 

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The best Hadoop architecture selected new questions

[Hadoop+Jobs+Apply+Today]
(Hadoop Developer skills)
The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage.

The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly.

More : Top selected Hadoop Interview Questions

Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode.

Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm

HDFS

HDFS is a distributed file system layer that sits on top of the native file system for an operating system. For example, HDFS can be installed on top of ext3, ext4, or XFS file systems for the Ubuntu operating system.

It provides redundant storage for massive amounts of data using cheap, unreliable hardware. At load time, data is distributed across all the nodes. That helps in efficient MapReduce processing. HDFS performs better with a few large files (multi-gigabytes) as compared to a large number of small files, due to the way it is designed.

Files are "write once, read multiple times." Append support is now available for files with the new version, but HDFS is meant for large, streaming reads—not random access. High sustained throughput is favored over low latency.

Files in HDFS are stored as blocks and replicated for redundancy or reliability. By default, blocks are replicated thrice across DataNodes; so three copies of every file are maintained. Also, the block size is much larger than other file systems. For example, NTFS (for Windows) has a maximum block size of 4KB and Linux ext3 has a default of 4KB.

Compare that with the default block size of 64MB that HDFS uses.

Name Node

NameNode (or the "brain") stores metadata and coordinates access to HDFS. Metadata is stored in NameNode's RAM for speedy retrieval and reduces the response time (for NameNode) while providing addresses of data blocks. This configuration provides simple, centralized management—and also a single point of failure (SPOF) for HDFS. In previous versions, a Secondary NameNode provided recovery from NameNode failure; but current version provides capability to cluster a Hot Standby (where the standby node takes over all the functions of NameNode without any user intervention) node in Active/Passive configuration to eliminate the SPOF with NameNode and provides NameNode redundancy.

Since the metadata is stored in NameNode's RAM and each entry for a file (with its block locations) takes some space, a large number of small files will result in a lot of entries and take up more RAM than a small number of entries for large files.

Also, files smaller than the block size (smallest block size is 64 MB) will still be mapped to a single block, reserving space they don't need; that's the reason it's preferable to use HDFS for large files instead of small files.

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8 Top Data Science Platform Developers in the World

Top data science tools and platforms providers across the world. Useful information for data science and data analytics developers.

Data Science is a combination of multiple skills. AI and Machine Learning are part of data science. You can create AI and Machine Learning products with data. 

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