Overview about Apache Hadoop

An overview about Apache Hadoop

An open-source framework written in Java which allows users to store as much as terabytes or even petabytes of Big Data – both structured and un-structured – across a cluster of computers. The unique storage mechanism which uses a distributed file system (HDFS) to map data across any part of a cluster.

The architecture of Hadoop

There are four main architectural features of Hadoop:

Hadoop Common

Apache Hadoop Common
Hadoop Common is called the Core of Hadoop and is formed of the utilities and libraries which support the other Hadoop modules. The Core also contains numerous JAR archives needed to start Hadoop.
Apache Hadoop Common
MapReduce in Hadoop

Hadoop MapReduce

MapReduce in Hadoop

This is the module which offers a key selling point of Hadoop, as it ensures scalability. When data is received by Hadoop it is executed over three different stages:

  • Map
    This is when the data is converted to key-value pairs which are known as Tuples.
  • Shuffle
    This is when the data is transferred from the Map stage to the Reduce stage.
  • Reduce
    Once the tuples have been received, they are processed and turned into a new set of tuples which are then stored via HDFS.

Hadoop Distributed File System (HDFS)

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This is the name given to the storage system used by Hadoop. It utilizes a master/slave set-up, where one primary machine controls a large number of other machines, making it possible to access big data quickly across the Hadoop clusters. By dividing the data into separate pieces, it stores them at speed on multiple nodes in one cluster.
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Hadoop YARN

Hadoop YARN

Hadoop YARN
In simple terms, YARN is a clustering platform which aids in the management of resources and the scheduling of tasks. It makes it possible for multiple data processing engines to operate within a single platform. These might include real-time streaming, interactive SQL, data science and batch processing.

The key benefits of using Hadoop

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Scalability

The structure of Hadoop means that it can scale horizontally, unlike traditional relational databases. This is because the data can be stored across a cluster of servers, from a single server to hundreds.

Speed

Speed

Faster data processing is made possible by the distributed file and powerful mapping offered by Hadoop.

Flexibility

Flexibility

Both your structured and unstructured data can be used to generate value by Hadoop. It can draw useful insights from sources such as social media, daily logs and emails.

Reliability

Reliability

The data stored by Hadoop is stored in replicate form across different servers in multiple locations, which increases reliability.

Advanced Data Analysis

Advanced Data Analysis

When utilizing Hadoop, it becomes simple to store, manage and process large data sets, bringing effective data analysis in-house.

Hadoop Services

Scalability

Consulting

Our consultants will come up with solutions for your data management challenges. These might include using it as a data warehouse, a data hub, an analytic sandbox or a staging environment.

Design & Development

Design & Development

Our experienced team can bring their knowledge in Hadoop Ecosystems to impact on your business. These will include Hive, Sqoop, Oozie, HBase, Pig, Flume and Zookeeper. Using these we can deliver scalable effective solutions based on Apache Hadoop.

Integration

Integration

The Hadoop solutions we develop can be integrated with enterprise applications such as Alfresco, CRM, ERP, Marketing Automation, Liferay, Drupal, Talend, and more.

Support and Maintenance

Support and Maintenance

Our round the clock support service means that your Hadoop systems are always going to be running.

Partner with Vsourz

If you’re looking for Hadoop Solutions, then Vsourz is the ideal people to deal with. We offer:
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Hire our Hadoop developers

The experts working at Vsourz offer an in-depth understanding of all the layers of a Hadoop stack. Our developers know everything they need to know about designing Hadoop clusters, the different modules of Hadoop architecture, performance tuning and setting up the top chain responsible for data processing in place.

We have skills and experience when it comes to working with Big Data tools such as Cloudera, Hortonworks, MapR and BigInsights, as well as relevant technologies like HDFS, HBase, Cassandra, Kafka, Spark, Storm, Scalr, Oozie, PIG, Hive, Avro, Zookeeper, Sqoop and Flume.

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