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Knowing best practices for Amazon Web Services (AWS) data pipelines is essential for modern companies handling large datasets and requiring secure ETL (Extract, Transform, Load) processes. In this article, we discuss AWS data pipeline best practices to ensure top performance and streamlined processes — without complications that can impede the execution of data transfer.

When it was revealed in September 2017, the massive Equifax data breach made international headlines. As one of the three major credit agencies in the United States, Equifax is responsible for processing personally identifiable information (PII) such as individuals’ names, addresses, and social security numbers. According to Equifax, 143 million people were affected by the data breach, making it one of the biggest cybersecurity disasters in history.

The Healthcare Insurance Portability and Accountability Act (HIPAA) has been an important federal law in healthcare since 1996. Part of its purpose was to create standards meant to protect sensitive patient information, and it took on even more important once the digitalization of patient health records became widespread. Now it’s required for certain types of businesses to protect patient health information—or face fines that range from $100 to $50,000 per violation.

With technology innovations raging at incredible speeds over the past few decades, new and exciting platforms for gathering, storing, transforming, and manipulating data are entering the market every day. Apache Hadoop was one of these disrupters when it entered the market in 2006, offering distributed storage and big data processing using a network of many computers.

Organizations have access to massive amounts of data, but they don’t always give enough thought to how they’re going to keep it private and protected. Dozens of data privacy regulations are in effect or in development globally, and the average consumer is learning more about how much of their data gets collected and used by businesses. For this reason, companies need to focus on keeping data safe while it's under their control, but it’s easy to make mistakes.

Data analytics technology helps organizations make sense of an ever-increasing volume of data. As this technology matures, it gets better at delivering actionable insights and helping companies determine outcomes. Prescriptive analytics is a modern solution that builds upon other analytics technology and guides organizations to the right decisions for a particular situation.

Extract, Load, Transform (ELT) technology makes it easy for organizations to pull data from databases, applications, and other sources, and move it into a data lake. But companies pay for this convenience in many ways. ELT solutions can have a negative impact on data privacy, data quality, and data management.

Wherever your business operates, you may have to follow the GDPR or face massive fines. It’s an epic legal document but much of it boils down to some key principles. The best approach is to store and manage your data in a way that makes GDPR-compliance straightforward. Here’s what you need to know.

Picking the right business intelligence (BI) tool is essential to helping you beat your competitors, better serve your customers, and make smarter data-driven decisions. However, there's no one-size-fits-all tool for every enterprise. Not all BI users are created equal, and not all users should have the same level of access to sensitive and confidential data.

Written in Python, Apache Airflow is an open-source workflow manager used to develop, schedule, and monitor workflows. Created by Airbnb, Apache Airflow is now being widely adopted by many large companies, including Google and Slack. Being a workflow management framework, Apache Airflow differs from other frameworks in that it does not require exact parent-child relationships. Instead, you only need to define parents between data flows, automatically organizing them into a DAG (directed acyclic graph).