Adopting a cloud-based data warehouse is your shortcut to superior marketing analytics and a 360-degree view of your customers.

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Off-the-shelf customer data platforms have serious shortcomings. Consider data warehouses instead.

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. With the ability to quickly provision on-demand and the lower fixed and administrative costs, the costs of operating a cloud data warehouse are driven mostly by the price-performance of the specific data warehouse platform.

Requests to Central IT for data warehousing services can take weeks or months to deliver. Central IT teams at large organizations face a proliferation of IT projects arising from the complexities of markets and from the needs of internal lines of business (LoBs). At the same time, Central IT must juggle cost and risk.

In a previous blog post on CDW performance, we compared Azure HDInsight to CDW. In this blog post, we compare Cloudera Data Warehouse (CDW) on Cloudera Data Platform (CDP) using Apache Hive-LLAP to EMR 6.0 (also powered by Apache Hive-LLAP) on Amazon using the TPC-DS 2.9 benchmark. Amazon recently announced their latest EMR version 6.1.0 with support for ACID transactions. This benchmark is run on EMR version 6.0 as we couldn’t get queries to run successfully on version 6.1.0.

Migrating a data warehouse from a legacy environment requires a massive upfront investment in resources and time. There is a lot to consider before and during migration. You may need to replan your data model, use a separate platform for tasks scheduling, and handle changes in the application’s database driver. Therefore, organizations must take a strategic approach to streamline the process. This article presents a step-by-step approach for migrating a data warehouse to the cloud.

I met Matthew in New York City about a year ago. We sat in a private conference room and he told me the story of his pharma startup. A small group of researchers set out to solve the black-box enigma of certain kinds of vicious cancers. There are so many cancers, so their vision was to focus on especially heinous ones. Fast forward to their recent FDA approval of their “Hail Mary” procedure and treatment methodology for stage-four patients of a particular cancer.

Let’s precisely define the different kinds of data repositories to understand which ones meet your business needs. October 29, 2020 A data repository serves as a centralized location to combine data from a variety of sources and provides users with a platform to perform analytical tasks. There are several kinds of data repositories, each with distinct characteristics and intended use cases. Let’s discuss the peculiarities and uses of data warehouses, data marts and data lakes.

Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. In this blog post, we compare Cloudera Data Warehouse (CDW) on Cloudera Data Platform (CDP) using Apache Hive-LLAP to Microsoft HDInsight (also powered by Apache Hive-LLAP) on Azure using the TPC-DS 2.9 benchmark.