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Make Big Data Collection Efficient with Hadoop Architecture and Design Tools

  Joseph Macwan      2015-09-17 05:24:44      2,646    0

Hadoop architecture and design is popular to spread small array of code to large number of computers. That is why big data collection can be made more efficient with hadoop architecture and design. Hadoop is an open source system where you are free to make changes and design new tools according to your business requirement.


Here we will discuss most popular tools under the category Hadoop development and how they are helpful for big projects.

Ambari and Hive– When you are designing a cluster, there is plenty of repetitive tasks take lots of efforts and time. Now Hadoop architecture and design can break down barriers and reduce cycle times with popular tool “Ambari” that offers a set of standard components for setting up Hadoop clusters.

Hive - “Ambari” helps you in managing and monitoring clusters efficiently. Once data is organized into clusters, Hive is responsible for extracting information from files to execute database queries.

Zookeeper- As Hadoop code runs over multiple machines, it is necessary to keep track of clusters to synchronize the work. That is precisely done by Zookeeper framework.

Hadoop Distributed File SystemHDFS framework allows Hadoop architecture and design to break down data collections into small nodes. Further large data files are broken into small blocks and each block is addressed by particular node. Go deep into explanation to know how blocks are distributed across multiple nodes.


Hbase and Sqoop– When data is organized into tables, it is stored by Hbase. Hbase will automatically share the store data across multiple nodes and overall access time is reduced. The framework is responsible for optimizing data storage and improved database accessibility. Mapping between different data tables is controlled by Sqoop command line tool.

Pig – Once data is stored into nodes, Pig plows through data files to handle data. Pig framework contains a set of standard functions to handle data files. However, you are free to add your own functions to Pig framework to make it more appreciable.

Avro and Spark – This is a special framework bundles data together using proper schema. Now data is more compact and optimized as compared to traditional formats. Spark is next generation framework speeds up Hadoop development by storing data into cache memory.

Quiet clearly, it is impossible to run any business effectively without data collections. When data files are endless, it is necessary to handle them smartly. The job is precisely done by Hadoop architecture and design tools crunching big data into manageable small blocks or data units.



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