By Jonathan Franklin
"The most sensible survival e-book in a decade" (Outside magazine), 438 Days is the real tale of the fisherman who survived fourteen months in a small boat drifting seven thousand miles around the Pacific Ocean.
On November 17, 2012, a couple of fishermen left the coast of Mexico for a weekend fishing journey within the open Pacific. That evening, a violent typhoon ambushed them as they have been fishing 80 miles offshore. As gale strength winds and ten-foot waves pummeled their small, open boat from either side and approximately capsized them, captain Salvador Alvarenga and his crewmate lower away a two-mile-long fishing line and commenced a determined sprint via crashing waves as they sought the security of port.
Fourteen months later, on January 30, 2014, Alvarenga, now a bushy, wild-bearded and half-mad castaway, washed ashore on a virtually abandoned island at the some distance facet of the Pacific. He may slightly communicate and was once not able to stroll. He claimed to have drifted from Mexico, a trip of a few seven thousand miles.
438 Days is the first-ever account of 1 of the main notable survival tales nowa days. in accordance with dozens of hours of unique interviews with Alvarenga, his colleagues, search-and-rescue officers, the distant islanders who stumbled on him, and the scientific group that stored his lifestyles, 438 Days is an unforgettable examine of the resilience, will, ingenuity and backbone required for one guy to outlive greater than a yr misplaced and adrift at sea.
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Extra resources for 438 Days: An Extraordinary True Story of Survival at Sea
The move to Hadoop therefore future-proofs your architec‐ ture. Data Hub A significant fraction of MapR customers name the centralization of data—sometimes called a data hub or data lake—as one of the most important Hadoop use cases. The terms are loosely defined, but the centralization concept is fairly simple and very powerful: by bringing together data from a variety of sources and data types (structured, unstructured, or semistructured, including nested data) into a cen‐ tralized storage accessible by many different groups for various types of analysis or export to other systems, you widen the possibilities for what insights you can harvest.
These speed trade-offs have to be measured empirically by converting sample queries, and the benefits of conver‐ sion then have to be balanced against fixed conversion costs and the variable costs of running the process on the Hadoop cluster. The final reporting code on the data warehouse is often large enough, complex enough, and difficult enough to test that the trade-off is clearly on the side of leaving it in place, at least initially. Figure 5-1. Data warehouse optimization works by moving staging tables and ETL processing to a Hadoop cluster, as shown in panels B and C.
A largescale shift in thinking is required for the operations, analytics, and applications development teams. This change is what will let you build systems that make use of what Hadoop does well. It is undeniably very hard to change the assumptions that are deeply ingrained by years of experience working with traditional systems. The flexibility and in‐ novation of Hadoop systems is a great advantage, but to be fully real‐ ized, they must be paired with your own willingness to think in new ways.
438 Days: An Extraordinary True Story of Survival at Sea by Jonathan Franklin