Allowed Implicit Conversions in Hive

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HBase Architecture

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      The HBase Architecture consists of servers in a Master-Slave relationship as shown below. Typically, the HBase cluster has one Master node, called HMaster and multiple Region Servers called HRegionServer. Each Region Server contains multiple Regions – HRegions. Just like in a Relational Database, data in HBase is stored in Tables and these Tables are stored in Regions. When a Table becomes too big, the Table is partitioned into multiple Regions. These Regions are assigned to Region Servers across the cluster. Each Region Server hosts roughly the same number of Regions. The HMaster in the HBase is responsible for Performing Administration Managing and Monitoring the Cluster Assigning Regions to the Region Servers Controlling the Load Balancing and Failover On the other hand, the HRegionServer perform the following work Hosting and managing Regions Splitting the Regions automatically Handling the read/write requests Communicating w...
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Pig UDF

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Pig provides extensive support for user defined functions (UDFs) as a way to specify custom processing. Pig UDFs can currently be implemented in three languages: Java, Python, JavaScript, Ruby and Groovy. The most extensive support is provided for Java functions. You can customize all parts of the processing including data load/store, column transformation, and aggregation. Java functions are also more efficient because they are implemented in the same language as Pig and because additional interfaces are supported such as the Algebraic Interface and the Accumulator Interface. Limited support is provided for Python, JavaScript, Ruby and Groovy functions. These functions are new, still evolving, additions to the system. Currently only the basic interface is supported; load/store functions are not supported. Furthermore, JavaScript, Ruby and Groovy are provided as experimental features because they did not go through the same amount of testing as Java or Python. At runtime note t...
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Weaknesses in Traditional Data Platforms

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Everyone understands that Hadoop brings high performance commercial computing to organizations using relatively low cost commodity storage.   What is accelerating the move to Hadoop are weaknesses in traditional relational and data warehouse platforms in meeting today's business needs.  Some key weaknesses of traditional platforms include: Late binding with schemas greatly increase the latency between receiving new data sources and deriving business value from this data.  The significantly high cost and complexity of SAN storage.  This high cost forces organizations to aggregate and remove a lot of data that contains high business value.  Important details and information are getting thrown out or hidden in aggregated data. The complexity of working with semi-structured and unstructured data. The incredible cost, complexity and ramifications of maintaining database administrators, storage and networking teams in traditional platforms.   There are lo...
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