Systems | Development | Analytics | API | Testing

Businesses large and small depend on access to information in order to make smarter, data-driven decisions. And much of that data is personal, sensitive, or confidential. So how can you balance this demand for big data with the need to protect the individuals whom this data describes? When it comes to personally identifiable information (PII), there are multiple very good reasons why you should keep it securely under lock and key.

What do Xplenty, Snowflake software, and dbt (data build tool) have in common? When used together, they merge the best of ETL (extract, transform, load) and ELT (extract, load, transform) into a powerful, flexible and cost-effective ETLT (extract, transform, load, transform) strategy. In this guide, we’ll show you how to create an ETLT strategy with Xplenty, Snowflake software, and dbt. But first, we’ll explain why you'd want to use this strategy to build an ETLT data transformation stack.

DreamFactory specialises in REST API generation for any number of database types however an underrepresented benefit is the ability to mount and manage any third party API via a HTTP service connector. This gives you the ability to generate documentation and secure an existing API as if it were created natively within DreamFactory.

DreamFactory specialises in REST API generation for any number of database types however an underrepresented benefit is the ability to mount and manage any third party API via a HTTP service connector. This gives you the ability to generate documentation and secure an existing API as if it were created natively within DreamFactory.

Cloudera is trusted by regulated industries and Government organisations around the world to store and analyze petabytes of highly sensitive or confidential information about people, healthcare data, financial data or just proprietary information sensitive to the customer itself.

Companies today are faced with an analytics conundrum. On one hand, there’s a higher demand than ever for actionable business insights, but on the other there’s limited resources to deliver BI content to on-technical business end-users. To fill this gap, the industry is increasingly turning to the next generation of self-service analytics tools. These tools reduce time to insight, speed up insight to action, and also allow BI teams to focus on more strategic analytics work.