The General Data Protection Regulation (GDPR) is a landmark piece of legislation that affects how organizations can handle, process, and store the personal data of European Union (EU) citizens and residents. But what does the GDPR require exactly, and how can you be sure that your organization complies with it? We go over everything you need to know in this all-in-one guide to GDPR compliance.

ELT is a three-step process that first extracts raw, structured, and unstructured data from source databases, applications, data stores, and other repositories. It then loads that data into a data lake and transforms it as needed by analysts. Since it doesn't move the data to an intermediate staging area or transform it before loading, the extraction process is speedy. You don’t need to pick and choose what data loads into the data lake or wait for it to be processed.

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.

Businesses of all sizes and entire industries rely on analyzing personal data to beat their competitors and better serve their customers. For example, information from your CRM (customer relationship management) software can help you understand how to convert more leads in a particular area or from a specific demographic.

When you secure a new data engineering position, it’s important to get off on the right foot. You need to respond to new data sources, data types, data sets, and applications efficiently. In the era of Big Data, this can be more than challenging. During this early stage of your job transition, you need to impress, and you can do this by re-thinking your data engineering strategy.

Data analysis is a key component for operating a successful business in today's tech-savvy world. When analyzing data sets, however, every business has its own needs. While some companies employ data scientists to work with complex big data, others have fewer and less complicated data sources that even non-technical users can navigate. Your specific needs will influence the type of tool your company chooses for data management.

Data breaches can happen to any company, regardless of size or technical resources. In April 2021, Facebook’s reputation took a massive hit when a data breach impacted more than half a billion users. The worst kind of data breach involves personally identifiable information (PII). PII is essentially any data that contains sensitive details about real people, such as customers and employees.

Business intelligence (BI) tools have been a shot in the arm of the enterprise. Teams can create their own visualizations and enjoy self-service analytics, without needing IT to compile reports or wrangle big data. But, of course, there’s a catch. BI tools expose data to a wider range of people, which means there are new issues of BI security (BISEC) and privacy to think about, especially in the age of GDPR. Here’s what you need to know.

When you’re working with personally identifiable information (PII), you may have to follow data privacy laws that govern how you must handle it. If the PII comes from an educational institution, that law is the Family Educational Rights and Privacy Act, also known as FERPA.