An e-book on how you can utilize and interpret data in Azure
Businesses store more data than ever before. But what can you do with all this information? To gain insights and use the data to your advantage, you need to manage, transform, process, aggregate, and interpret your data. We guide you through the steps you need to take to improve your business with data!
Our Data Engineer Lisa Hoving will dive into the following topics:
How to transform data into valuable information: the 4 fases;
How to build a data lake;
Data Factory v.s. Databricks - if you're missing the 'spark';
Which Apache Spark tool is most cost-effective?
Simply fill in the form and receive our e-book!
Tags
Data and AI
Possibly interesting as well:
The basic principles of Data Science on Azure
Do you want to start a data science project on Azure? Starting from scratch? Don’t miss this e-book!
Breaking the bank on Azure: what Apache Spark tool is the most cost-effective?
Figuring out which tool has the lowest execution costs is often a complicated task. In this article, we break down the costs. With this information, you can decide which option is best for your company.
Data Factory vs. Databricks: When your love for data is missing some (Apache) Spark
How do you choose the data partner that suits you best? Azure Data Factory has great benefits when you start out, but Apache Spark allows you to explore deeper layers. So is Azure Databricks your data partner that brings the "Spark" back into your data project? One thing is for sure, in Azure you don't necessarily have to choose a monogamous data solution.
Today, companies save all kinds of data, even when they don’t yet have a use case for it. But where should all these different types of datasets be stored? This is where the Data Lake comes in!
What your data journey looks like depends on your use case. Nevertheless, within any data project, there are five generic steps that any company will take to get insights from their data. Have a look!
