Between October 2024 and February 2025, Gatepoint Research surveyed 100 IT and data engineering executives, including 58% CxOs and VPs, 35% directors, and 7% senior managers from companies ranging from Fortune 1000 enterprises to smaller firms. Today's data...
While ETL and ELT solutions can be helpful to data warehousing teams for some data movement scenarios, data automation software provides teams with much broader, far-reaching capabilities and benefits across the entire data warehousing lifecycle. ELT Tools and Data...
Experience the transformative impact of integrating WhereScape with Databricks. Organizations today face a myriad of data management challenges, from ensuring data compliance to building scalable, agile systems that drive informed decision-making. The combined power...
Experience the transformative impact of WhereScape's data automation across diverse industries. Organizations face unique challenges from stringent data compliance to the need for agile decision-making frameworks. Our suite of automation tools is meticulously designed...
This white paper explores the core principles, best practices, and integration possibilities of Data Vault 2.0, highlighting its relevance in contemporary data warehousing.
WhereScape RED 10.0.0.0 Key features include: PostgreSQL metadata repository unifying all products on a single platform New Scheduler with scalable cross-platform architecture Refreshed Look & Feel the first of several planned UI upgrades Linux execution within...
This whitepaper by eminent data experts BARC Research & Eckerson Group outlines Data Warehouse and Data Vault Adoption Trends describes how to design and build them, and explains how they increase both productivity and business agility. It recommends that...
WhereScape® RED provides a productivity boost by building, prototyping, and enhancing data warehouses 10 to 100 times faster than traditional products. This white paper examines each of the following factors that influence project duration and speed of delivery: Rapid...
Since the beginning of the Big Data revolution, IT teams have struggled with how to optimally organize big data sources and big data sets given their limited resources, and big data’s historically manual, artisanal handling. To move forward with big data...