Big data is data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source.
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- Spark for Beginners
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- Apache Spark is an open source, scalable, massively parallel, in-memory execution environment for running analytics applications. Think of it as an in-memory layer that sits above multiple data stores, where data can be loaded into memory and analyzed in parallel across a cluster.
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- Apache HBase
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- Apache HBase is an open source NoSQL database that provides real-time read/write access to those large datasets. HBase scales linearly to handle huge data sets with billions of rows and millions of columns, and it easily combines data sources that use a wide variety of different structures and schemas. HBase is natively integrated with Hadoop and works seamlessly alongside other…
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- Apache Hadoop for Administrators
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- Apache Hadoop is an open source, scalable, massively parallel, in-memory database environment for data farms and data lakes. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local…
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- Apache Hadoop for Developers
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- Apache Hadoop is an open source, scalable, massively parallel, in-memory database environment for data farms and data lakes. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local…
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- Kafka for Developers
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- Learn the Apache Kafka Ecosystem, Core Concepts, Operations, Kafka API, Build Your Own Producers and Consumers
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