This post explains what a data connector is and provides a framework for building connectors that replicate data from different sources into your data warehouse
Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.
Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
5 stages of learning Data Science and how to ace each of them
A data lake is totally different from a data warehouse in terms of structure and function. Here is a truly quick explanation of "Data Lake vs Data Warehouse".
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?