Apache Spark brings high-speed, in-memory analytics to Hadoop clusters, crunching large-scale data sets in minutes instead of hours Apache Spark got its start in 2009 at UC Berkeley’s AMPLab as a way ...
In theory, data lakes sound like a good idea: One big repository to store all data your organization needs to process, unifying myriads of data sources. In practice, most data lakes are a mess in one ...
Enterprise software development and open source big data analytics technologies have largely existed in separate worlds. This is especially true for developers in the Microsoft .NET ecosystem. The ...
The advent of scalable analytics in the form of Hadoop and Spark seems to be moving to the end of the Technology Hype Cycle. A reasonable estimate would put the technology on the “slope of ...
Big data refers to datasets that are too large, complex, or fast-changing to be handled by traditional data processing tools. It is characterized by the four V's: Big data analytics plays a crucial ...
Apache Spark and Apache Hadoop are both popular, open-source data science tools offered by the Apache Software Foundation. Developed and supported by the community, they continue to grow in popularity ...
In this special guest feature, Anand Venugopal, head of StreamAnalytix at Impetus Technologies, discusses real-time streaming analytics applications and how companies can use Apache Spark for data ...
A Spark application contains several components, all of which exist whether you’re running Spark on a single machine or across a cluster of hundreds or thousands of nodes. Each component has a ...
It’s rare in the world of software to see a single architecture dominate as comprehensively as the relational database model. The relational database (RDBMS)—actually a hybrid of Codd’s relational ...
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