By the end of this two-part series, we will dive into what data warehouse architecture is and how to implement one for your organization. Part one will look at architectural layers and common data warehouse components, while part two dives into multi-tiered data warehouse architecture.

Data warehouses and data lakes represent two of the leading solutions for enterprise data management in 2023. While data warehouses and data lakes may share some overlapping features and use cases, there are fundamental differences in the data management philosophies, design characteristics, and ideal use conditions for each of these technologies.

In the digital age, data is the lifeblood of any organization. The way you store and analyze your data can significantly impact your success. This is where data warehouses come into the picture. Data warehouses are essential for businesses of all sizes, as they provide a central repository for data from a variety of sources, which can then be used for analysis and reporting. This data can be used to make better business decisions, improve operational efficiency, and identify new opportunities.

In today's digital era, a data warehouse stands as a pivotal cornerstone for businesses. A data warehouse is defined as a digital repository that houses an organization's vast amounts of data, it serves as both a vault and a library, ensuring data is not only safely stored but also easily accessible. Being able to access your company’s data is critical to business success.

The new, automated open-source data migration tool moves Teradata, Hive, Redshift, and Oracle data warehouses from on-premises to BigQuery.

Step right up, ladies and gentlemen, and witness the grand spectacle of the digital age! In a world where data is king, where information reigns supreme, and cloud data warehouses are multiplying like rabbits, there's a technology initiative like no other— data warehouse modernization! This article is the second in the series "Seven Data Integration and Quality Scenarios for Qlik and Talend," and answers everything you wanted to know about data warehouse modernization but were afraid to ask.

Step right up, ladies and gentlemen, and witness the grand spectacle of the digital age! In a world where data is king, where information reigns supreme, and cloud data warehouses are multiplying like rabbits, there's a technology initiative like no other— Data Warehouse Modernization! This article is the second in the series "Seven Data Integration and Quality Scenarios for Qlik and Talend," and answers everything you wanted to know about Data Warehouse Modernization but were afraid to ask.

In the age of big data, where humans generate 2.5 quintillion bytes of data every single day, organizations like yours have the potential to harness more powerful analytics than ever before. But gathering, organizing, and sorting data still proves a challenge. Put simply, there's too much information and not enough context. The most popular commercial data warehouse solutions like Amazon Redshift say they deliver structured, usable data for businesses. But is this true?

Data warehousing improves access to information, speeds up query-response times, and allows businesses to fetch deeper insights from big data. Previously, companies had to invest a lot in infrastructure to build a data warehouse. The advent of cloud technology has significantly reduced the cost of data warehousing for businesses.

Apache Impala and Apache Kudu make a great combination for real-time analytics on streaming data for time series and real-time data warehousing use cases. More than 200 Cloudera customers have implemented Apache Kudu with Apache Spark for ingestion and Apache Impala for real-time BI use cases successfully over the last decade, with thousands of nodes running Apache Kudu.