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.

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.

In a fast-paced world that produces more data than it can ingest, the right Python ETL tool makes all the difference. But not all Python tools are made the same. Some Python ETL tools are great for writing parallel load jobs for data warehousing, others are specialized for unstructured data extraction. In this article, we’ll explore the 7 best tools for ETL tasks and what business requirements they help you fulfill: Let’s dive right into the best tools and see how they compare.

Are you tired of sifting through mountains of data from different data sources before you find the data set you need to get your job done? It's time to simplify your life with data consolidation.