Organizations are losing out on data-driven decision-making opportunities when data stays in the data warehouse. While business intelligence solutions can surface insights from these data sets, it often reaches team members too late to be used for daily business operations. Reverse ETL empowers organizations to increase the value of their data warehouses through operationalization. Learn how this can transform the way companies use data and insights.

You will have to consider multiple solutions and their tradeoffs when setting up your enterprise data architecture. In this article, we contrast a data lake and a data warehouse side-by-side to make your choice easier.

How to use a cloud data warehouse to achieve HIPPA compliance, reduce risk and offload some of the operational burden. How do you balance an accessible data warehouse with data protection and HIPAA Compliance? To get the most value from your data, it should be available to everyone in your organization who can benefit from the data analysis, insights and value it holds.

In traditional data warehouses, specific types of data are stored using a predefined database structure. Due to this “schema on write” approach, prior to all data sources being consolidated into one warehouse, there needs to be a significant transformation effort. From there, data lakes emerge!

More and more businesses are opting to use data lakes or, more likely, data warehouses these days, which allow them to store, analyze, and utilize their data from one convenient destination. But beyond creating reports and in-depth analytics, how can you truly operationalize your data warehouse into an even more vital part of your business's digital stack? Reverse ETL could provide some opportunities to do just that.

Organizations may struggle with getting the full value of their data without knowing it. Their data science team uses data warehouses to power business intelligence solutions to create reports, dashboards, and other data visualizations. However, the time it takes for this information to reach teams makes it difficult to use for daily decision-making. Operationalizing data warehouses sends these insights directly into daily operations systems, thereby allowing for immediate access.

Data warehouses are a particular kind of database. Learn how they are uniquely suited to analytics.

Did you know Cloudera customers, such as SMG and Geisinger, offloaded their legacy DW environment to Cloudera Data Warehouse (CDW) to take advantage of CDW’s modern architecture and best-in-class performance? In addition to substantial cost savings upon moving to CDW, Geisinger is also able to search through hundreds of million patient note records in seconds providing better treatment to their patients.

Extracting your data from multiple sources and collecting it in a centralized data warehouse is hard enough—but how do you keep it safe once it arrives? Organizations use a variety of data security methods to protect sensitive and confidential information, including share-level security (all users sharing a password) and user-level security (with separate accounts and passwords for each user).