How much do you know about Domain-Driven Design (DDD)? It's a design approach to software development where the language and structure of software code match the business domain. The concept comes from a 2003 book by Eric Evans. And it influences software architects, information architects, data engineers, and computer science professionals who organize code and solve some seriously stressful software problems. Domain-Driven Design is a super-successful concept with brilliant business logic benefits.

When COVID hit, multinationals went into a tailspin, scrambling for solutions to pandemic-related problems like suspended flights, social distancing, and stay-at-home orders. How could global brands function when operations are so interconnected? One global sales and marketing brand stayed calm in the crisis, innovating localized strategies that strengthened remote regional teams.

Heroku is a powerful PaaS that helps developers easily and quickly launch and scale modern applications. In addition to its base features, it also offers Heroku Elements Marketplace, which has add-ons for expanding the functionality of the base platform. While Heroku has built-in logging for your applications in this platform, you may find that the default interface leaves something to be desired.

Most enterprises are leveraging vast reserves of data to improve their business insights and decision-making. However, as companies manage larger stores of data and move more and more information from operational databases to data warehouses, it creates an ever-mounting threat of data breaches.

How do you integrate data from Amazon RDS (Relational Database Service) with data from other sources such as S3, Redshift, or even MongoDB? The answer is Xplenty. Our data integration on the cloud lets you join data from various sources and then process it to gain new insights. What about storing the results back to RDS? No problem; Xplenty does that as well.

When a company is small, having a fully centralized data team may not be an issue. As you grow, however, problems can start to arise. You have one structure that’s supporting all of your business units, and they may not be able to dedicate sufficient time and resources to individual business units. This can lead to delays in surfacing important insights and decisions made on old or inaccurate data.

There’s a common misconception that as soon as a business signs up for a solution from a cloud service provider (CSP), that the CSP will automatically ensure all their dealings in that cloud environment are safe and secure. As dedicated as Cloud Service Providers are to cybersecurity, that’s simply not possible. Your cloud provider has no control over the customer data you share, the aptitude of your employees, or how you optimize your own on-premises security and firewalls.

One of Xplenty's most rewarding features is its ability to enact low-code and no-code digital transformation. Even with no experience in ETL or data integration, non-technical users can take advantage of Xplenty’s intuitive drag-and-drop interface to build robust, complex data pipelines to a data warehouse or data lake in the cloud.

Companies these days are handling more data than ever: an average of 163 terabytes (163,000 gigabytes), according to a survey by IDG. Efficiently storing, processing and analyzing this data is essential in order to glean valuable insights and make informed business decisions. Yet the question remains: What is the best way to store enterprise data? For many use cases, the most appealing choice is a relational database.

What is multi-cloud data analytics and why are so many companies getting on board? Cloud computing itself is now a well-established best practice, but a multi-cloud strategy is nearly as common these days. While 94 percent of organizations are now using cloud computing, 84 percent are using a multi-cloud data strategy. Multi-cloud is an especially fruitful data strategy for companies pursuing data analytics.

AWS Solution Architects are in red-hot demand, and the AWS Certification is the highest-paying certification in the United States. As such, you wear many hats as a Solution Architect for Amazon Web Services. You're a problem-solver, a creative genius, a multitasker, and a big-picture thinker. And you design AWS implementations better than anyone else you know. But there are some things about being an AWS Solution Architect that aren't so rosy. Amazon's ever-changing recommendations.

For the last few years, you have heard the terms "data integration" and "data management" dozens of times. Your business may already invest in these practices, but are you benefitting from this data gathering? Too often, companies hire specialists, collect data from many sources and analyze it for no clear purpose. And without a clear purpose, all your efforts are in vain. You can take in more customer information than all your competitors and still fail to make practical use of it.

Five major differences between Alteryx and Xplenty: Alteryx Analytics and Xplenty both offer no-code/low-code ETL solutions that simplify the process of extracting, transforming, and loading data. Nevertheless, there are distinct differences between Alteryx and Xplenty that could affect your decision to choose one over the other.

The growth of data has been exponential. By 2023, it's anticipated that approximately 463 exabytes (EB) will be created every day. To put this into perspective, one exabyte is a unit equivalent to 1 billion gigabytes. By 2021, 320 billion emails will be sent daily, many of which contain personal information. Data collected around the globe contains the type of information that businesses leverage to make more informed decisions.

Building a data lake in Amazon S3 using AWS Spectrum to query the data from a Redshift cluster is a common practice. However, when it comes to boosting performance, there are some tricks that are worth learning. One of those is using data in Parquet format, which Redshift considers a best practice. Here's how to use Parquet format with Xplenty for the best data lake performance.