The Health Insurance Portability and Accountability Act, or HIPAA, is a federal regulation in the United States that protects healthcare data containing personal health information, or PHI. It also covers Electronic PHI, or E-PHI, which are digital records of this information. The ability to effectively using healthcare data is essential for improving patient outcomes, quality of care, resource allocation, revenues, and other operations.

A Big Data stack has several layers that take your data from source to analytics tools. Extract, Transform, Load tools integrate data from sources into a data warehouse or lake. Business intelligence solutions use centralized data for their analytic needs. An ETL tool such as Xplenty offers a user-friendly experience for ingesting data from many sources, transforming it as needed, and sending it to the next layer. Here’s how you can implement this handy tool in your organization.

The major differences between Jitterbit, MuleSoft, and Xplenty: Extract, Transform, and Load (ETL) streamlines data integration by consolidating data from multiple sources, turning it into useful formats, and loading it into a centralized location. The world's most successful organizations use ETL to tame big data, produce visual data flows, and garner business-critical analytics. But with so many ETL tools on the market, which one should you choose?

Today we are delighted to introduce our new Workspaces feature that allows users to organize and group their packages together. It’s always been a bit challenging to organize packages within Xplenty especially if you have hundreds of packages, or if there are many users using the account. Finally, all those issues should be addressed by the new Xplenty Workspace feature.

According to the Ponemon Institutes, "Cost of a Data Breach" report, a data breach's total global cost averaged $3.86 million in 2020. However, a data breach's implications go far beyond financial losses; it can severely hinder an organization's operational capacity and compliance structures.

According to The Economist, “the world’s most valuable resource is no longer oil, but data.” Despite the value of enterprise data, much has been written about the so-called “data science shortage”: the supposed lack of professionals with knowledge of how to use and manipulate big data. A 2018 study by LinkedIn estimated that there were more than 151,000 unfilled jobs in the U.S. requiring data science skills.

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain futured. However, the sudden surge in demand comes with its own set of challenges.

The key differences between Hadoop vs. SQL: Organizations rely on big data to power their business, but many teams struggle with the complexities of data management. Thankfully, Hadoop and SQL handle large data sets more efficiently. These tools manage data in unique ways, which makes it difficult for us to compare them on a like-for-like basis. However, organizations looking to streamline their tech stacks might have reason to choose one over the other. In this article, we compared Hadoop vs.

Large and small businesses face daily reminders that their data’s security is under attack by digital pirates, hackers, and cybercriminals. These threats require an immediate response, whether it's an unexpected hacking threat or a newly discovered database vulnerability. In this guide, we’ll help you understand these threats and how to prevent them in 2021.

We're living in a data-driven age. In every sector, we've seen new companies emerge, executing lightning-fast strategies based on sophisticated analytics. These data mavericks have disrupted and sometimes even devoured their more traditional rivals. To stay afloat, you need a state-of-the-art data infrastructure. That means having the right platforms, the right data pipelines, and the right analytics engines. But when you have all that data, what do you actually do with it?