AWS Partner Network (APN) Blog

Category: Analytics

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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Fivetran_AWS-Service-Ready

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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CloudZero_AWS-Partners

Improving Dataset Query Time and Maintaining Flexibility with Amazon Athena and Amazon Redshift

Analyzing large datasets can be challenging, especially if you aren’t thinking about certain characteristics of the data and what you’re ultimately looking to achieve. There are a number of factors organizations need to consider in order to build systems that are flexible, affordable, and fast. Here, experts from CloudZero walk through how to use AWS services to analyze customer billing data and provide value to end users.

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Accenture_AWS Solutions

Maximizing the Value of Your Cloud-Enabled Enterprise Data Lake by Tracking Critical Metrics

Successful data lake implementations can serve a corporation well for years. Accenture, an APN Premier Consulting Partner, recently had an engagement with a Fortune 500 company that wanted to optimize its AWS data lake implementation. As part of the engagement, Accenture moved the customer to better-suited services and developed metrics to closely monitor the health of its overall environment in the cloud.

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Zaloni_AWS Solutions

Turning Data into a Key Enterprise Asset with a Governed Data Lake on AWS

Data and analytics success relies on providing analysts and data end users with quick, easy access to accurate, quality data. Enterprises need a high performing and cost-efficient data architecture that supports demand for data access, while providing the data governance and management capabilities required by IT. Data management excellence, which is best achieved via a data lake on AWS, captures and makes quality data available to analysts in a fast and cost-effective way.

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