AWS Partner Network (APN) Blog

Category: Analytics

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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MongoDB_AWS Solutions

MongoDB Atlas Data Lake Lets Developers Create Value from Rich Modern Data 

With the proliferation of cost-effective storage options such as Amazon S3, there should be no reason you can’t keep your data forever, except that with this much data it can be difficult to create value in a timely and efficient way. MongoDB’s Atlas Data Lake enables developers to mine their data for insights with more storage options and the speed and agility of the AWS Cloud. It provides a serverless parallelized compute platform that gives you a powerful and flexible way to analyze and explore your data on Amazon S3.

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How to Create a Continually Refreshed Amazon S3 Data Lake in Just One Day

Data management architectures have evolved drastically from the traditional data warehousing model, to today’s more flexible systems that use pay-as-you-go cloud computing models for big data workloads. Learn how AWS services like Amazon EMR can be used with Bryte Systems to deploy an Amazon S3 data lake in one day. We’ll also detail how AWS and the BryteFlow solution can automate modern data architecture to significantly accelerate delivery and business insights at scale.

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Driving Hybrid Cloud Analytics with Amazon Redshift and Denodo Data Virtualization

A data integration architecture that can virtually connect multiple data platforms provides business users with immediate access to data, with far less IT friction than traditional methods, so you can make faster, more data-driven decisions. The Denodo Platform for AWS can aid organizations in managing their data by providing an alternative data integration method. With Denodo, data is presented in real-time and without the need to replicate data to a new consolidated repository.

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How to Enable Mainframe Data Analytics on AWS Using Model9

Mainframe proprietary storage solutions such as VTLs hold valuable data locked in a platform with complex tools. This can lead to higher compute and storage costs, and make it harder to retain existing employees or train new ones. When mainframe data is stored in a cloud storage service, however, it can be accessed by a rich ecosystem of applications and analytics tools. Model9 enables mainframe customers to backup and archive directly to AWS.

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Datacoral_AWS Solutions

Building Serverless Data Pipelines on Amazon Redshift By Writing SQL with Datacoral

Amazon Redshift is a powerful yet affordable data warehouse, and while getting data out of Redshift is easy, getting data into and around Redshift can pose problems as the warehouse grows. Datacoral is a serverless data platform that manages metadata changes, data transformations, and orchestrating pipelines for data consumers. In this post, learn how to write Redshift SQL to represent data flow, and how serverless data pipelines get automatically generated for that data flow.

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Reltio_AWS Solutions

Leveraging Multi-Model Architecture to Deliver Rich Customer Relationship Profiles with Reltio Cloud

Building a true Customer 360 requires gaining a comprehensive view of customer behavior and preferences by aggregating data from all of these sources, and more. With a single source of truth, a true Customer 360 delivers complete a real-time customer view to all parts of the organization—sales, marketing, service, support, etc. This consistent and contextual insight can help enterprises delight customers with personalized experience and timely offers through each touch point in the customer journey.

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