Systems | Development | Analytics | API | Testing

For decades, the technologies and systems that deliver analytics have undergone massive change. What hasn’t changed, however, is the goal: using data-driven insights to drive actions. Insight to action has been a consistent vision for the industry. Everyone from data practitioners to technology developers have sought this elusive goal, but as Chief Data Strategy Officer Cindi Howson points out, it has remained unfulfilled — until now.

Over the course of my next two blog posts, I would like to share my thoughts around a debate raging in data architecture circles. The bone of contention? That the 21st century needs a new data management paradigm for modern analytics. First up, I’ll frame the argument and explain the two prominent approaches of data hub and data fabric. Then, I’ll cover data mesh and compare all three architectures. As always, I’d love to get your input, feedback, queries and comments!

The following is Part III of a three-part series. Welcome to the final installment of a three-part series discussing the areas to take seriously when you want to drive business with analytics. In Part I of this series, I discussed how to prioritize data accessibility and how to address the challenges that come with it. Those challenges include: Part II discussed where the disconnect is and addressed how organizations can bridge the gap.

About the report The Cloudera Enterprise Data Maturity Report is a global survey of 3,150 business and IT decision makers assessing organizations’ maturity when it comes to their current capabilities and handling of data and analytics.

Stitch is pleased to announce the availability of Microsoft SQL Server as a destination. MS SQL Server joins nine other data destinations (including Microsoft Azure Synapse) that Stitch supports to help execute all your data modeling and analysis projects. Stitch customers can immediately benefit from the new destination, which supports both Azure SQL Server and standard SQL Server editions reaching as far back as SQL Server 2012.

When choosing any SaaS application, you must start with a clear understanding of your business requirements. Then ask yourself the following questions: Develop a framework for data processing requirements, and you'll find a data warehouse solution that provides the right amount of power, functionality, and high performance for data analytics. Keep the answers to these questions in mind when reading through this article.

While Redshift is arguably the best data warehouse on the market, it can come with a hefty price tag. We’ve created this Redshift pricing guide to help you evaluate Redshift cheaply, create a budget for full implementation, and optimize your Redshift set up so that you’re getting the most bang for your data buck. Ready to get started? Think of this blog post as a “choose your own adventure” guide.

Amazon Redshift makes it easier to uncover transformative insights from big data. Analytical queries that once took hours can now run in seconds. Redshift allows businesses to make data-driven decisions faster, which in turn unlocks greater growth and success. For a CTO, full-stack engineer, or systems architect, the question isn’t so much what is possible with Amazon Redshift, but how. How do you ensure optimal, consistent runtimes on analytical queries and reports?