AWS Partner Network (APN) Blog

Category: Analytics

Mactores-AWS-Partners

Optimizing Presto SQL on Amazon EMR to Deliver Faster Query Processing

Seagate asked Mactores Cognition to evaluate and deliver an alternative data platform to process petabytes of data with consistent performance. It needed to lower query processing time and total cost of ownership, and provide the scalability required to support about 2,000 daily users. Learn about the the three migration options Mactores tested and the architecture of the solution Seagate selected. This effort improved the overall efficiency of Seagate’s Amazon EMR cluster and business operations.

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Machine Learning-4

How to Use Amazon SageMaker to Improve Machine Learning Models for Data Analysis

Amazon SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. Amazon SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.

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In-Depth Strategies for Building a Scalable, Multi-Tenant SaaS Solution with Amazon Redshift

Software-as-a-Service (SaaS) presents developers and architects with a unique set of challenges. One essential decision you’ll have to make is how to partition data for each tenant of your system. Learn how to harness Amazon Redshift to build a scalable, multi-tenant SaaS solution on AWS. This post explores trategies that are commonly used to partition and isolate tenant data in a SaaS environment, and how to apply them in Amazon Redshift.

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Cazena-AWS-Partners

Accelerating Apache and Hadoop Migrations with Cazena’s Data Lake as a Service on AWS

Running Hadoop, Spark, and related technologies in the cloud provides the flexibility required by these distributed systems. Cazena provides a production-ready, continuously optimized and secured Data Lake as a Service with multiple features that enables migration of Hadoop and Spark analytics workloads to AWS without the need for specialized skills. Learn how Cazena makes it easy to migrate to AWS while ensuring your data is as secure on the cloud as it is on-premises.

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Machine Learning-3

Gathering Market Intelligence from the Web Using Cloud-Based AI and ML Techniques

Many organizations face the challenge of gathering market intelligence on new product and platform announcements made by their partners and competitors—and doing so in a timely fashion. Harnessing these insights quickly can help businesses react to specific industry trends and fuel innovative products and offerings inside their own company.Learn how Accenture helped a customer use AWS to gather critical insights along with key signals and trends from the web using AI and ML techniques.

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Enabling Customer Attribution Models on AWS with Automated Data Integration

Attribution models allow companies to guide marketing, sales, and support efforts using data, and then custom tailor every customer’s experience for maximum effect. Combined together, cloud-based data pipeline tools like Fivetran and data warehouses like Amazon Redshift form the infrastructure for integrating and centralizing data from across a company’s operations and activities, enabling business intelligence and analytics activities.

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How Sisense Simplifies Complex Data Analytics for Analysts and Developers

Organizations these days are inundated with data. Learn how engineers and analysts can handle the critical challenges of gaining insights from large and complex data sources while also democratizing data for improved adoption across the organization. The Sisense platform simplifies end-to-end data and analytics, reducing time-to-insights by empowering data and IT teams to build advanced data models and perform advanced analysis for their needs.

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Xplenty_AWS-Competency

How to Use Xplenty with AWS KMS to Provide Field-Level Encryption in ETL Data Processing

Enterprises often choose to mask, remove, or encrypt sensitive data in the ETL step to minimize the risk of sensitive data becoming stored, logged, accessible, or breached from their data lake or data warehouse. Xplenty’s ETL and ELT platform allows customers to quickly and easily prepare their data for analytics using a simple-to-use data integration cloud service. Xplenty’s global service uses AWS KMS to create and control the keys used to encrypt or digitally sign your data.

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Behalf_AWS-Competency

How Behalf Met its Streaming Data Scaling Demands with Amazon Managed Streaming for Apache Kafka

To be a successful fintech startup, companies have to build solutions fast so the business can achieve its goals. However, you can’t compromise on security, reliability, or support. As an AWS Financial Services Competency Partner, Behalf is committed to delivering reliable, secure, low-cost payment processing and credit options to business customers. Learn how Behalf chose Amazon MSK to meet its increasing streaming data needs in a reliable and cost-efficient manner.

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How to Simplify AWS Monitoring with Logz.io’s Fully Managed ELK Stack and Grafana

Building scalable, resilient, and secure metrics and logging pipelines with the ELK Stack and Grafana requires engineering time and expertise. The Logz.io Cloud Observability Platform delivers both as a fully-managed service so engineers can use the open source monitoring tools they know on a single solution, without the hassle of maintaining them at scale. Logz.io provides advanced analytics to make the ELK Stack and Grafana faster, more integrated, and easier to use.

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