Do you have more data than you can analyze with your existing architecture? Are there questions that you could answer leveraging petabyte-scale data analytics? Would you like to use modern distributed frameworks such as Apache Spark without having to worry about cluster setup or configuration?
Then, Amazon EMR is what you’re looking for!
Get up to 2X faster time-to-insights, with performance optimized and open source API compatible versions of Apache Spark, Hive, and Presto.
Our team of experts can help you on this journey to better, faster and more scalable insights.
Amazon EMR serves as a managed cluster platform on AWS, streamlining the execution of big data frameworks like Apache Hadoop and Apache Spark.
Leveraging these frameworks and associated open-source projects such as Apache Hive and Apache Pig, it enhances the speed of processing large datasets for analytics and business intelligence tasks.
Additionally, EMR facilitates the transfer and manipulation of substantial data volumes to and from various AWS data stores and databases, such as Amazon S3 and Amazon DynamoDB.
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Analyze data using open-source ML frameworks such as Apache Spark MLlib, TensorFlow, and Apache MXNet. Connect to Amazon SageMaker Studio for large-scale model training, analysis, and reporting
Analyze events from streaming data sources in real-time to create long-running, highly available, and fault-tolerant streaming data pipelines.
Extract data from a variety of sources, process it at scale, and make it available for applications and users.
Run large-scale data processing and what-if analysis using statistical algorithms and predictive models to uncover hidden patterns, correlations, market trends, and customer preferences.
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