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21.06.2021 | History

5 edition of Architecting Modern Data Platforms found in the catalog.

Architecting Modern Data Platforms

A Guide to Enterprise Hadoop at Scale

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        StatementOReilly Media
        PublishersOReilly Media
        Classifications
        LC Classifications2019-05-12
        The Physical Object
        Paginationxvi, 115 p. :
        Number of Pages78
        ID Numbers
        ISBN 10149196927X
        Series
        1nodata
        2
        3

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Many queries result in chains of MapReduce jobs that can take many minutes or even hours to complete. For streaming data, in particular, the incumbent message broker technologies struggle to scale to the demands of big data.

Hive is thus ideally suited to offline batch jobs for extract, transform, load ETL Architecting Modern Data Platforms reporting; or other bulk data manipulations. When you buy books using these links the Internet Archive may earn a.

One of the principal design goals for Spark was to take full advantage of the memory on worker nodes, which is available in increasing quantities on commodity servers. These tables support most of the common data types that you know from the relational database world.

We are not able to cover every framework in detail—in many cases these have their own full book-level treatments—but we try to give a sense of what they do. The fields a document contains are defined in a schema. Large metric time series, such as those seen in IoT datasets• This results in relatively complex ingestion and orchestration pipelines.

For managed tables, Hive actively controls the data in the storage engine: if a table is created, Hive builds the structures in the storage engine, for example by making directories on HDFS.

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It also insulates long-running applications from Oozie server failures; because the job state is persisted in an underlying database, the Oozie server can pick up where it left off after a restart without affecting running actions. The client then reads the data directly from the DataNodes, preferring replicas that are local or close, in network terms.

We will come across other commonly used implementations in this book, such as cloud-based object storage offerings like Amazon S3. Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT• When providing a list of DataNodes for the pipeline, the NameNode takes into account a number of things, including available space on the DataNode and the location of the node—its rack locality.

Unlike with Hive, there is no centralized query server; each Impala daemon can accept user queries and acts as the coordinator node for the query. Machine roles Architecting Modern Data Platforms a cluster Usually we divide a cluster up into two classes of machine: master and worker.

If your network infrastructure is good enough, it is no longer essential to use the same underlying hardware for compute and storage. Internet Archive Open Library Book Donations 300 Funston Avenue San Francisco, CA 94118•