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docs/wp/astronomy.md

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We believe this will be a very attractive solution to the field of astronomy going forward, coping with the predicted expansion of data volumes in the field due to the new projects that are scheduled to begin in the near future.
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[:fontawesome-solid-print: PDF](/download/wp/kdb_in_astronomy.pdf)
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## Further reading
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docs/wp/capi/index.md

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This document covered multiple aspects of the C API interface for connecting with the kdb+ database. Topics covered included the creation of socket connections, execution of queries, error handling, memory management, and the creation and extraction of data from `K` objects such as lists, dictionaries and tables. Practical examples formed the basis for the construction of a C subscriber process, capable of consuming a kdb+ data feed, and a feedhandler process designed to publish data to a kdb+ tickerplant. Finally, the use of shared C libraries to extend the functionality of kdb+ was also demonstrated.
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[:fontawesome-solid-print: PDF](/download/wp/c_api_for_kdb.pdf)
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## Author
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docs/wp/card-counters/index.md

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ESP has evolved from an emerging technology to an essential platform of various industry verticals. The technology's most consistent growth has been in banking, serving fraud detection, algorithmic trading and surveillance. There has also been considerable growth in other industries including healthcare, telecommunications, manufacturing, utilities and aerospace.
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[:fontawesome-solid-print: PDF](/download/wp/card-counters-in-blackjack.pdf)
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## Authors
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docs/wp/columnar-database/index.md

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**Ciarán Gorman** is a financial engineer who has designed and developed data-management systems across a wide range of asset classes for top-tier investment banks.
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:fontawesome-solid-print:
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[PDF](/download/wp/columnar_database_and_query_optimization.pdf)
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docs/wp/compress/index.md

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In any given application, the metrics which will determine if and how compression should be used will be informed by the individual use case. Our tests have shown that for this particular setup, assuming some OS caching, the more CPU-intensive queries will see significantly less overall impact than simple reads on small, randomly distributed pieces of data. This implies that we could expect to see acceptable performance levels for intensive queries, while achieving worthwhile reductions in disk usage, i.e. our compression ratio of 1.5. Experimenting with logical block sizes suggested that we should keep this parameter at the low end of the spectrum for our queries, or we will see a fall-off in performance. This is reinforced by the relative lack of change in compression ratio across logical block sizes. Depending on the individual use case, other applications may choose to optimize for overall compression ratio or time taken to save the data.
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[:fontawesome-solid-print: PDF](/download/wp/compression_in_kdb.pdf)
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## Author
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docs/wp/corporate-actions.md

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All tests were run using kdb+ version 3.1 (2014.02.08)
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[:fontawesome-solid-print: PDF](/download/wp/temporal_data_a_kdb_framework_for_corporate_actions.pdf)
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docs/wp/data-management.md

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As mentioned in the introduction, a key feature of kdb+ is that it is a flexible offering that allows users to extend the functionality of their applications through the versatility of the q language. There are vanilla intraday- and historical-data processing architectures presented in existing documentation that cover many standard use cases. However, it is also common for a kdb+ system to quickly establish itself as a success within the firm, resulting in the need to process more types of data and requests from downstream users and applications. It is at this point that a systems manager is often faced with balancing how to ensure performance and scalability goals are met, while at the same time dealing with resource constraints and the need for maintainability. The seven cases and customization techniques covered in this paper provided examples of what you can do to help reach those goals.
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[:fontawesome-solid-print: PDF](/download/wp/kdb_data_management_sample_customisation_techniques_with_amendments.pdf)
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docs/wp/data-recovery.md

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All tests were run using kdb+ V3.1 (2014.03.27).
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[:fontawesome-solid-print: PDF](/download/wp/data_recovery_for_kdb_tick.pdf)
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docs/wp/data-visualization/index.md

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described in this paper to work around limitations, to connect a Tableau
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visualization front-end to a kdb+ back end.
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[:fontawesome-solid-print: PDF](/download/wp/data-visualization-a4.pdf)
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docs/wp/disaster-recovery/index.md

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downtime (i.e. high availability), recover quickly and accurately from
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these events, and most importantly prevent and minimize data loss.
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[:fontawesome-solid-print: PDF](/download/wp/disaster-recovery-a4.pdf)
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