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  2. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.

  3. List of in-memory databases - Wikipedia

    en.wikipedia.org/wiki/List_of_in-memory_databases

    Mnesia is a distributed, soft real-time database management system written in the Erlang programming language. It is distributed as part of the Open Telecom Platform. MonetDB: MonetDB Solutions, CWI: 2004 SQL, ODBC, JDBC, C, C++, Java, Python, PHP, Node.js, Perl, Ruby, R, MAL open-source MonetDB License, based on MPL 2.0 as of version Jul2015.

  4. MapReduce - Wikipedia

    en.wikipedia.org/wiki/MapReduce

    MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...

  5. ClickHouse - Wikipedia

    en.wikipedia.org/wiki/ClickHouse

    The system is a cluster of shards, where each shard is a group of replicas. ClickHouse uses asynchronous multi-master replication. Data is written to any available replica, then distributed to all the remaining replicas. ZooKeeper is used for coordinating processes, but it's not involved in query processing and execution.

  6. Stream processing - Wikipedia

    en.wikipedia.org/wiki/Stream_processing

    By way of illustration, the following code fragments demonstrate detection of patterns within event streams. The first is an example of processing a data stream using a continuous SQL query (a query that executes forever processing arriving data based on timestamps and window duration). This code fragment illustrates a JOIN of two data streams ...

  7. Embarrassingly parallel - Wikipedia

    en.wikipedia.org/wiki/Embarrassingly_parallel

    Some examples of embarrassingly parallel problems include: Monte Carlo analysis [9] Distributed relational database queries using distributed set processing. Numerical integration [10] Bulk processing of unrelated files of similar nature in general, such as photo gallery resizing and conversion.

  8. Graph database - Wikipedia

    en.wikipedia.org/wiki/Graph_database

    The above examples are a simple illustration of a basic relationship query. They condense the idea of relational models' query complexity that increases with the total amount of data. In comparison, a graph database query is easily able to sort through the relationship graph to present the results.

  9. Apache Impala - Wikipedia

    en.wikipedia.org/wiki/Apache_Impala

    Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. [1] Impala has been described as the open-source equivalent of Google F1 , which inspired its development in 2012.