Systems | Development | Analytics | API | Testing

If you’ve been online at all this week, chances are that you’ve heard about the Log4Shell zero-day (CVE-2021-44228) in Log4J, a popular Java logging library. The vulnerability enables Remote Code Execution (RCE), which allows attackers to run arbitrary code on the target’s machines. I know the first question that you all have is: “Is Kong affected by Log4Shell?” Let’s start with the good news: No Kong products are affected by this Log4J vulnerability.

Here at Appian, we have experienced tremendous growth in the past few years by helping our customers solve their most important business problems faster. We do this by providing a low-code platform that brings together humans, systems, and most recently, robots in support of any mission.

In this guide, we’ll go over the Redshift COPY command, how it can be used to import data into your Redshift database, its syntax, and a few troubles you may run into.

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?

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.

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.

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.

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.

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!