Systems | Development | Analytics | API | Testing

Amazon Redshift supports native integration with Fivetran. Here’s how to set it up.

A data architecture expert gives a crash course on moving analytics to the cloud — including a step-by-step plan to ensure success.

Sometimes, the data you want to analyze lives in AWS S3 buckets by default. If that’s the case for the data you need to work with, good on you: You can easily ingest it into an analytics tool that integrates with S3. But what if you have a data source — such as logs generated by applications running in a Kubernetes cluster — that isn’t stored natively in S3? Can you manage and analyze that data in a cost-efficient, scalable way? The answer is yes, you can.

For all of the buzz surrounding both artificial intelligence and data-driven management, many companies have seen mixed results in their quest to harness the value of enterprise data. To avoid those pitfalls, we mixed best-of-breed and proprietary solutions to develop our enterprise data platform (EDP), focusing much of our attention on a combination of smart changes in technology, culture and process for data lakes.

Like most organizations, Yellowfin has a CRM tool. The data in your CRM should be able to help you understand how you’re selling and how you win. But everyone I speak to is frustrated by the analytics they get from their CRM. We realized very quickly that the reporting in our CRM tool wasn't meeting our needs, so we built our own solution.

For data analysts, business intelligence professionals, and CTOs to optimize and scale business operations, they must first understand the business data that is available to them. One of the best platforms to turn complicated data points from multiple platforms into a singular, coherent data set is Microsoft Power BI. However, you must first prepare the data sets to eliminate fragmentations and create structural consistency. This article explores how to prepare data for Microsoft Power BI.