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Top 5 Challenges for Hadoop MapReduce in the Enterprise

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Reporting and analysis drives businesses in making the best possible decisions. The source of all these decisions is the data. There are two types of data: structured and unstructured. Most recently, IT has struggled to deliver timely analysis through data warehousing architectures designed for batch processing. And these same architectures are now starting to fail under the load of rapidly rising data volumes and new data types that beg for a continuous approach to data processing.

In order to meet these ‘big data’ conditions, both computational and storage solutions have evolved:

  • Emergence of new programming framework to enable distributed computing on large data sets (for example, MapReduce).
  • New data storage techniques (for example, file systems on commodity hardware, like the Hadoop file system, or HDFS) for structured and unstructured data.

Now that MapReduce is becoming accepted as a ‘working’ model, the next goal is to turn it into an enterprise-class solution. This process has been found to be difficult to solve, and this industry challenge has created an opportunity for IBM® Platform Computing™. IBM has written a technical white paper for overcoming common challenges when deploying Hadoop MapReduce – “Top 5 Challenges for Hadoop MapReduce in the Enterprise.”

Download this whitepaper today to learn best practices for addressing the following topics:

  • Lack of performance and scalability
  • Lack of flexible resource management
  • Lack of application deployment support
  • Lack of quality of service
  • Lack of multiple data source support

Platform Symphony provides an enhanced, low-latency MapReduce implementation designed for scale, flexibility and reliability. Designed to support multiple applications, organizations can dramatically increase utilization across all resources resulting in a high return on investment. Unlike other less sophisticated solutions that lack multiple MapReduce application support and scalable distributed workload engines, Platform Symphony’s distributed workload services are designed for high scalability, fast performance, and extreme application compatibility through its low-latency SOA architecture. MapReduce application workloads can now run with high reliability under powerful central management, thereby meeting ITs SLAs with high reliability and consistency.

Download this white paper from the insideBIGDATA White Paper Library.

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