Big data analytics tools are gaining steam -- helping enterprises gather insights and draw crucial conclusions...
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from an ocean of data. But turning that data into results isn't easy. Big data projects take immense storage, compute power and software services. Using a public cloud provider's big data infrastructure lets enterprises quickly and cost effectively make new queries and test new ideas -- all without having to purchase servers or deploy and manage the underlying software. And there is a multitude of AWS big data services -- each with its own uses and benefits.
Amazon Elastic MapReduce (EMR) is one of the core AWS big data services, designed to scale, distribute and process huge amounts of data. EMR relies on a managed open source Hadoop framework that can create enormous parallel processing clusters built from commodity hardware to support scalable Elastic Compute Cloud instances. EMR also supports the open source Apache Spark framework. A business can spin up a Hadoop or Spark cluster, load data and analytics software, optimize the cluster size to balance cost and time, and retrieve the results.
Regardless of how much computing power a developer provisions for a big data project, the benefit of that data is meaningless without the ability to organize and analyze it. AWS big data services and big data analytics tools help big data projects take shape. The real issue is deciding which type of analytical work to perform.
Amazon Elasticsearch Service handles these types of analytic tasks, and it is often included in big data projects that involve application monitoring and real-time or streaming data analysis. The ability to continuously ingest, organize and process large volumes of data via AWS big data services is becoming paramount as enterprises build real-time applications, sensor devices and the internet of things capabilities into their technologies.
Services such as Amazon Kinesis Analytics enable businesses to run SQL queries against this type of data to look for patterns and engagement. And this complements Amazon Kinesis Firehose, which allows IT teams to move real-time data to AWS.
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Data analytics also requires the use of query engines, such as Apache Presto, which support SQL queries against data reaching into the petabytes. Presto also has the ability to combine data from multiple sources -- regardless of which database holds the data. Prior to analytics, data is stored in a warehouse such as Hive or in a relational database such as Amazon DynamoDB.
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