In the new digital paradigm, companies from all industries are generating tremendous volumes of data, especially from open-source platforms, making it difficult to constantly capture, store, manage, scale, and analyze inputs from all touchpoints.
Now, more than ever, organizations need effective analytics implementations to convert data into insights that can be leveraged into actionable business decisions. With platforms such as Amazon EMR, Brillio and AWS help organizations in setting up fast, scalable, and secure big data processing, warehousing, and analysis mechanisms.
Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open-source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.
With EMR you can run petabyte-scale analysis at less than half of the cost of traditional on-premises solutions and over 3x faster than standard Apache Spark. You can run workloads on Amazon EC2 instances, Amazon Elastic Kubernetes Service (EKS) clusters, or on-premises using EMR on AWS Outposts.
Easy to use:
Analysts, data engineers, and data scientists can use EMR Notebooks to collaborate and interactively explore, process, and visualize data. Simply specify the version of EMR applications and the type of computing you want to use. EMR takes care of provisioning, configuring, and tuning clusters so that you can focus on running analytics.
EMR pricing is simple and predictable: you pay a per-instance rate for every second used, with a one-minute minimum charge.
Unlike the rigid infrastructure of on-premises clusters, EMR decouples compute and storage, giving you the ability to scale each independently and take advantage of the tiered storage of Amazon S3. With EMR, you can provision one, hundreds, or thousands of compute instances or containers to process data at any scale. The number of instances can be increased or decreased automatically using Auto Scaling (which manages cluster sizes based on utilization) and you only pay for what you use.
EMR automatically configures EC2 firewall settings, controls network access to instances, and launches clusters in an Amazon Virtual Private Cloud (VPC). Server-side encryption or client-side encryption can be used with the AWS Key Management Service or your own customer-managed keys.
You have complete control over your EMR clusters and your individual EMR jobs. You can launch EMR clusters with custom Amazon Linux AMIs and easily configure the clusters using scripts to install additional third-party software packages. EMR enables you to reconfigure applications on running clusters on the fly without the need to relaunch clusters.
Use EMR’s built-in machine learning tools, including Apache Spark MLlib, TensorFlow, and Apache MXNet for scalable machine learning algorithms, and use custom AMIs and bootstrap actions to easily add your preferred libraries and tools to create your own predictive analytics toolset.
Extract, transform, load (ETL):
EMR can be used to perform data transformation workloads (ETL) quickly and cost-effectively such as sort, aggregate, and join on large datasets.
Analyze clickstream data from Amazon S3 using Apache Spark and Apache Hive to segment users, understand user preferences, and deliver more effective ads.
Analyze events from Apache Kafka, Amazon Kinesis, or other streaming data sources in real-time with Apache Spark Streaming and Apache Flink to create long-running, highly available, and fault-tolerant streaming data pipelines on EMR. Move transformed data sets to S3 or HDFS and insights to Amazon Elasticsearch Service.
EMR Notebooks provide a managed analytic environment based on open-source Jupyter that allows data scientists, analysts, and developers to prepare and visualize data, collaborate with peers, build applications, and perform interactive analyses.
EMR can be used to process vast amounts of genomic data and other large scientific data sets quickly and efficiently. Researchers can access genomic data hosted for free on AWS.