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By Yang W.P., Du M.W.

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Predictive analytics on larger data sam‐ ples tends to give you a more accurate result. You don’t always know what will be of importance in data at the time it is ingested, and the insights that can be gained from a later perspective will not be possible if the pertinent data has already been discarded. “Save more data” means saving data for longer time spans, from larger-scale systems, and also from new data sources. Saving data from more sources also opens the way to data exploration—ex‐ perimental analysis of data alongside your mainstream needs that 32 | Chapter 4: Decisions That Drive Successful Hadoop Projects may unlock surprising new insights.

Impala and Drill allow SQL queries, while Spark allows queries to be written in the Scala programming language or in SQL. Spark and Drill can also be tightly integrated to get the best of both. Spark queries can be executed in micro-batched or interactive fashion. Together, these tools provide some very interesting possibilities. Search Abuse—Using Search and Indexing for Interactive Query Search engines are not normally considered as processing elements, but they can actually be used very nicely for many forms of interactive queries.

Two of the tools listed here, Apache Kafka and Apache Flume, are useful to provide a queue. Some of the widely used tools for data ingestion in Hadoop are de‐ scribed in detail in the sections that follow. Apache Kafka Kafka is a robust pub-sub (publish, subscribe) framework that allows highly available, dependable message streaming. It is a paradox that a key feature of Kakfa is its small number of features. It has far fewer features and is much less configurable than Flume, which is described later.

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A backtracking method for constructing perfect hash functions from a set of mapping functions by Yang W.P., Du M.W.


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