You don’t need to be a data science expert to make use of platform analytics to gain better insights into your business. From Google Analytics to deep-dive data analytics for your APIs, big data is your friend when it comes to understanding your company. The right analytics platform can open up your data in a user-friendly way that empowers you to fuel better-informed business decisions based on your performance, customer data and analytics.

ETL (Extract, Transform, Load) is an essential process for modern data management. With the increasing volume of data that businesses generate, it’s crucial to have efficient and scalable tools to handle the data pipeline. AWS provides a range of ETL tools, making it easy to extract data from various sources, transform it into a desired format, and load it into data storage systems. In this article, we’ll explore the top five tools for ETL on AWS data pipelines.

When it comes to data-driven business decisions, research and analysis are often used interchangeably. However, these terms are not synonymous, and understanding the difference between them is crucial for making informed decisions.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization?

Cloudera SQL Stream Builder (SSB) gives the power of a unified stream processing engine to non-technical users so they can integrate, aggregate, query, and analyze both streaming and batch data sources in a single SQL interface. This allows business users to define events of interest for which they need to continuously monitor and respond quickly. There are many ways to distribute the results of SSB’s continuous queries to embed actionable insights into business processes.