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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

It aims to provide a framework to create low-latency streaming applications on the AWS Cloud using Amazon Kinesis Data Streams and AWS purpose-built data analytics services. In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices.

Analytics 116
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Dimensional modeling in Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model.

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Configure monitoring, limits, and alarms in Amazon Redshift Serverless to keep costs predictable

AWS Big Data

It automatically provisions and intelligently scales data warehouse compute capacity to deliver fast performance, and you pay only for what you use. Just load your data and start querying right away in the Amazon Redshift Query Editor or in your favorite business intelligence (BI) tool. Ashish Agrawal is a Sr.

Metrics 81
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Blending Art and Science: Using Data to Forecast and Manage Your Sales Pipeline

Sisense

For sales leaders, what’s hugely empowering is the ability to slice and dice data on the fly, understand what team and individual reps should be achieving, and easily measure the team from a data driven standpoint. To achieve this, first requires getting the data into a form that delivers insights.

Sales 91
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Accelerate Moving to CDP with Workload Manager

Cloudera

In this blog, we walk through the Impala workloads analysis in iEDH, Cloudera’s own Enterprise Data Warehouse (EDW) implementation on CDH clusters. We might find the root cause by realizing that a problem recurs at a particular time, or coincides with another event. . Analyze iEDH workloads with WM for upgrade and migration.

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Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. This post is designed to be implemented for a real customer use case, where you get full snapshot data on a daily basis.