Systems | Development | Analytics | API | Testing

Why a Data Lakehouse alone is not the answer to modern analytics

Can the Lakehouse meet all your analytics needs or do you need a Data Lake and a Data Warehouse working in parallel? Join us on this live stream to learn when one works better than the other, or, do you really need the combination to win? Our speakers David, Justin, and Chris will debate the different use cases and architectures to determine what is necessary for a data-driven business.

SaaS in 60 - Catalog KPI and Qlik Lineage Connectors

Catalog KPIs: These KPIs help you understand key metrics of apps, data, notes, automations and monitored charts viewable in the catalog. The indicators represent usage and views of each item such as how many apps are using a particular data set, what items are being used most- including a trend indicator showing more, less or no change in views over a 28 day period.

Will cloud ecosystems finally make insight to action a reality?

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.

Data Hub, Fabric or Mesh? Part 1 of 2

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!

Design With Analytics in Mind for Data Governance

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.

Stitch builds on its Microsoft technology partnership

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.

Microsoft Azure vs Amazon Redshift

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.