By Michael Wood
Publish yr note: First released in 2000
The Spanish conquest of the Americas within the sixteenth century was once the most vital and cataclysmic occasions in background. Spanish expeditions persisted very good hardships on the way to open up the lands of the 'New World', and few tales in historical past can fit those for drama and endurance.
In Conquistadors, Michael wooden follows within the footsteps of a few of the best of the Spanish adventurers traveling from the forests of Amazonia to Lake Titicaca, the deserts of North Mexico, the snowpeaks of the Andes and the heights of Machu Picchu. He reports the epic trips of Cortes, Pizarro, Orellana and Cabeza de Vaca, and explores the turbulent and terrifying occasions surrounding the Spanish conquest of the Aztec and Inca empires.
Wood brings those tales to vibrant existence, highlighting either the heroic accomplishments and the complicated ethical legacy of the ecu invasion. Conquistadors is Michael wooden at his most sensible - considerate, provocative and gripping background.
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Here, data is copied from the orig‐ inal source to an NFS-mounted file system exactly as before, but now the NAS has been replaced by a MapR cluster that holds the staging tables. All or some of the ETL process is run on the Hadoop cluster instead of the data warehouse, and then the work product of the ETL process is bulk loaded into the data warehouse using standard bulk import tools via another NFS mount of the Hadoop cluster. The lower panel of Figure 5-1 shows an alternative design for nonMapR Hadoop platforms.
This can be a good way to get realtime ingestion using standard file-oriented Linux tools, but only if the underlying distributed file system is a realtime file system. For the Hadoop-inproduction stories discussed in this book, we’ve drawn from the ex‐ perience of companies who use the MapR distribution for Hadoop and are therefore using a realtime distributed file system in the form of MapR-FS. A function that is needed in the case of streaming data ingestion (or export from a realtime analytics application) is to provide a queuing layer.
HDFS supports a feature called snapshots, but these snapshots are not consistent, nor are they precise. Files that are being updated when a snapshot is made can continue to change after the snapshot is com‐ pleted. If, however, you arrange to only snapshot directories that have no files being actively changed, then you may be able to use HDFS snapshots (sometimes called fuzzy snapshots) for the same purposes as the atomic and consistent snapshots such as those available with MapR. ” In the next chapter we provide a collection of tips drawn from the decisions that have helped to drive success in existing Hadoop and NoSQL projects.