AWS Partner Network (APN) Blog

Category: Amazon EMR


Fully Managed Data Governance with Amazon EMR Integration with Apache Ranger and Privacera

Privacera is an AWS Partner that provides security and privacy tools for enterprises to secure and govern user access to databases and datastores in the cloud. PrivaceraCloud reduces the burden of self-managing Apache Ranger by providing Ranger as a hosted service. It provides centralized management of data access, authorization policies, and auditing. Learn how Amazon EMR can integrate with PrivaceraCloud to provide a fully-managed data governance solution.

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How Tamr Optimized Amazon EMR Workloads to Unify 200 Billion Records 5x Faster than On-Premises

Global business leaders recognize the value of advanced and augmented big data analytics over various internal and external data sources. However, technical leaders also face challenges capturing insights from data silos without unified master data. Learn how migrating Tamr’s data mastering solutions from on-premises to AWS allowed a customer to process billions of records five times faster with fully managed Amazon EMR clusters.

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Migrating Netezza Workloads to AWS Using Amazon EMR and Amazon Redshift

Data warehouse modernization has been a key aspect of many customers’ broader cloud transformation stories. Legacy data warehouse systems, however, present many challenges when dealing with today’s enterprise data needs. Learn how AWS and Infosys collaborated to transform a legacy Netezza platform on AWS for a large retail customer. With Infosys tools, processes, and industry knowledge, the collaboration between AWS and Infosys enables customers to transform their analytics platforms.

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Bigstream Provides Big Data Acceleration with Apache Spark and Amazon EMR

Apache Spark and its parallel processing framework, along with the ease of scaling up in public clouds, have pushed out the limits for data analytics. Learn how Bigstream addresses growing Spark needs, with software that optimizes existing CPU infrastructure and can also seamlessly incorporate advanced programmable hardware. With the same number of servers, Bigstream can accelerate Spark clusters 3x with software alone and 10x when introducing FPGAs.

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Bursting Your On-Premises Data Lake Analytics and AI Workloads on AWS

Developing and maintaining an on-premises data lake is a complex undertaking. To maximize the value of data and use it as the basis for critical decisions, the data platform must be flexible and cost-effective. Learn how to build a hybrid data lake with Alluxio to leverage analytics and AI on AWS alongside a multi-petabyte on-premises data lake. Alluxio’s solution is called “zero-copy” hybrid cloud, indicating a cloud migration approach without first copying data to Amazon S3.

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How nClouds Helps Accelerate Data Delivery with Apache Hudi on Amazon EMR

Apache Hudi on Amazon EMR is an ideal solution for large-scale and near real-time applications that require incremental data pipelines and processing. This post provides a step-by-step method to perform a proof of concept for Apache Hudi on Amazon EMR. Learn how a non-customer-facing PoC solution from nClouds set up a new data and analytics platform using Apache Hudi on Amazon EMR and other managed services, including Amazon QuickSight for data visualization.

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How SnapLogic eXtreme Helps Visualize Spark ETL Pipelines on Amazon EMR

Fully managed cloud services enable global enterprises to focus on strategic differentiators versus maintaining infrastructure. They do this by creating data lakes and performing big data processing in the cloud. SnapLogic eXtreme allows citizen integrators, those who can’t code, and data integrators to efficiently support and augment data-integration use cases by performing complex transformations on large volumes of data. Learn how to set up SnapLogic eXtreme and use Amazon EMR to do Amazon Redshift ETL.

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Implementing SAML AuthN for Amazon EMR Using Okta and Column-Level AuthZ with AWS Lake Formation

As organizations continue to build data lakes on AWS and adopt Amazon EMR, especially when consuming data at enterprise scale, it’s critical to govern your data lakes by establishing federated access and having fine-grained controls to access your data. Learn how to implement SAML-based authentication (AuthN) using Okta for Amazon EMR, querying data using Zeppelin notebooks, and applying column-level authorization (AuthZ) using AWS Lake Formation.

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Training Multiple Machine Learning Models Simultaneously Using Spark and Apache Arrow

Spark is a distributed computing framework that added new features like Pandas UDF by using PyArrow. You can leverage Spark for distributed and advanced machine learning model lifecycle capabilities to build massive-scale products with a bunch of models in production. Learn how Perion Network implemented a model lifecycle capability to distribute the training and testing stages with few lines of PySpark code. This capability improved the performance and accuracy of Perion’s ML models.

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Lower TCO and Increase Query Performance by Running Hive on Spark in Amazon EMR

Learn how Mactores helped Seagate Technology to use Apache Hive on Apache Spark for queries larger than 10TB, combined with the use of transient Amazon EMR clusters leveraging Amazon EC2 Spot Instances. It was imperative for Seagate to have systems in place to ensure the cost of collecting, storing, and processing data did not exceed their ROI. Moving to Hive on Spark enabled Seagate to continue processing petabytes of data at scale with significantly lower TCO.

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