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Telefonica Background

With rapid development of the Internet era, enterprise data is increasing explosively. An urgent demand is developed on how to generate value from the complex and chaotic data and how to use the data to implement innovations and seize business opportunities. Obviously, conventional data processing is incompetent in this big data era.

MapReduce Service (MRS) provides storage and analysis capabilities for massive data and builds a reliable, secure, and easy-to-use operation and maintenance (O&M) platform. Users can apply for use Hadoop, Spark, HBase, and Hive services to quickly create clusters and provide storage and computing capabilities for massive data analysis or real-time processing. After data storage and computing are fulfilled, the cluster service can be terminated, and no fee will be charged accordingly. You can also choose to run clusters permanently.

 

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MRS delivers the following functions:

  • Analysis and computing of massive data

Hadoop 2.7.2: Based on Hadoop applying a distributed system infrastructure, MRS uses MapReduce to implement parallel computing of large data sets (TB-level or above).

Spark 1.5.1: Spark is a distributed batch processing framework. It provides analysis and mining and iterative memory computing capabilities and supports application development in multiple programming languages, including Scala, Java, and Python. Additionally, it provides Spark SQL, which enables data to be queried and analyzed using structured query language (SQL) statements.

HBase 1.0.2: Hadoop Database (HBase) is a column-based distributed storage system that features high reliability, performance, and scalability. HBase is designed to supplement relational databases in processing massive data.

Hive 1.3.0: Hive is a data warehouse framework built on Hadoop. It stores structured data using the Hive query language (HQL), a language like the SQL. Hive converts HQL statements to MapReduce or HDFS tasks for querying and analyzing massive data stored in Hadoop clusters.

  • Storage of massive data

Hadoop Distributed File System (HDFS) features high fault tolerance and provides high-throughput data access, applicable to the processing of large data sets. After being processed and analyzed, data is encrypted by using Secure Sockets Layer (SSL) and transmitted to the Object Storage Service (OBS) system or HDFS.