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Data Vault 2.0 modeling methodology has gained immense popularity since its launch in 2013. It’s a hybrid model that combines the benefits of Third Normal Form (3NF) and star schema architectures, making it a dream solution for data warehousing engineers. But is it worth implementing for your data warehouse architecture? The answer isn’t straightforward, as there are many factors to consider. So, let’s dive in and explore whether Data Vault 2.0 is right for you.

Microsoft Power BI is a fantastic tool for data visualization and business intelligence. But as any data analyst can attest, before you can build metrics and dashboards, you need to spend more than 1/3 of your time preparing and cleaning the dataset for your Power BI instance. Luckily, there is a better way to cut down on this time-consuming task. In this article you will learn.

Future uses of data will require an automated data movement platform, not just data pipelines as single point solutions.

Data movement refers to the ability to transfer data through a variety of methods from one source or system in your company to another destination.

Data pipeline tools help you create and manage pipelines that extract, transform and move data into a central location. Find out what the best tools are here.

Learn the major benefits of ELT tools and get a detailed analysis of the top eight solutions you can use.

Business metrics provide a quantifiable way to measure the success of a business. They help organizations to track their progress internally while also serving as a way to communicate the performance of a business to stakeholders and external parties. There are hundreds of metrics that could be factored into these calculations, but they need to be specific to an organization to be effective.

IT and data executives find themselves in a quandary about deciding how to wrangle an exponentially increasing volume of data to support their business requirements – without breaking an increasingly finite IT budget. Like an overeager diner at a buffet who’s already loaded their plate with the cheap carbs of potatoes and noodles before they reach the protein-packed entrees, they need to survey all of the data options on the menu before formulating their plans for this trip.

Who doesn’t love Apache Airflow? The Python-based open-source tool allows us to schedule and automate workflows with DAGs (Directed Acyclic Graphs). Data teams use Airflow for a myriad of use cases: from building ETL data pipelines to launching machine learning apps. The open-source tool makes workflow management easy: it is extensible, easy to monitor from the intuitive user interface in real time, and it allows you to build dependencies between jobs.