Don’t make costly mistakes as your business strives to make better use of data.

Heroku is a cloud platform as a service (PaaS) for efficiently building, deploying, monitoring, and scaling applications. Originally created to work with the Ruby programming language, Heroku is now part of the Salesforce platform and supports languages such as Java, Node.js, PHP, Python, and Scala. While Heroku makes it easy to develop production-ready applications fast, one question remains: how can you integrate your Heroku app data with the rest of your data infrastructure and workflows?

Ever since Salesforce acquired Heroku back in 2010, the two services have worked exceptionally well together. Businesses can use Heroku to build flexible and scalable applications while utilizing Salesforce to manage customer data and drive sales. And when you need to share data between these two platforms, there’s a dedicated add-on: Heroku Connect.

Personally identifiable information (PII) and protected health information (PHI) are two types of sensitive data that fall under one or more data privacy regulations. HIPAA and GDPR are examples of the regulations that govern what organizations can and need to do with PII and PHI. When you work with large data sets, it can be challenging to maintain compliance with these regulations.

The faster you can extract, transform, and load data from MongoDB, the better it is for your business processes and business intelligence systems. The problem is, most ETL solutions struggle to manage MongoDB’s dynamic schemas, NoSQL support, and JSON data types. That’s not the case with Xplenty – which was optimized for easy, no-fuss MongoDB integrations with ease: no custom code, no delays, no confusion.

Data integration has been around for decades in some form or fashion, as organizations are always looking for ways to combine their enterprise data and collect it in a centralized location. The most commonly used and dominant type of data integration is ETL (extract, transform, load). ETL first extracts data from one or more source systems, transforms it as necessary, and then loads it into a target warehouse or data lake.

Reverse ETL is an emerging piece of the modern data stack that enables you to productionize your analytics.

Reports and records. Sales sheets and spreadsheets. Files and financials. Your team has more big data than you can comprehend spread across multiple data sources in more locations than a James Bond movie. Isn't it time you kept this data somewhere safe? Moving data to a data warehouse like Snowflake is like keeping thousands of books in a library or a trove of treasure in an underground vault. Big data, your most prized asset, will be safe and snug.

ETL, long a mainstay of data integration, is labor-intensive, brittle, complex — and ripe to be supplanted by ELT.

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.