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Tag: Data Warehouse

How to Overcome Obstacles in Data Lake and Warehouse Strategies: 3 Best Practices for Enterprise Architects
In this sponsored post Kimberly Read, the enterprise architect at Faction, suggests that to support the business case for multi-cloud, enterprise architects can benefit by addressing three primary considerations. Multi-cloud initiatives—drawing on services from public and private clouds—can help organizations stay ahead of the curve.
The data lakehouse: just another crazy buzzword?
Data professionals have long debated the merits of the data lake versus the data warehouse. But this debate has become increasingly intense in recent times with the prevalence of data and analytics workloads in the cloud, the growing frustration with the brittleness of Hadoop, and hype around a new architectural
Delta Lake: the Foundation of Your Lakehouse
More and more, we have seen the term “lakehouse” referenced in today’s data community. Beyond our own work at Databricks, companies and news organizations alike have increasingly turned to this idea of a data lakehouse as the future for unified analytics, data science, and machine learning. But what is a …
Data Evolution in the Cloud: The lynchpin of competitive advantage
This report from our friends over at Snowflake reveals the extent to which the data sharing economy is powering business growth and how organizations are leveraging data from a range of sources to drive innovation, create better customer experiences, and meet regulatory requirements.
Infographic: The tools and technology of Data SEO
Data SEO uses many tools and technologies drawn from different branches of data science. To use data science in order to automate, predict or visualize an SEO strategy, marketers will need some or all of the tools featured in this infographic developed by our friends over at Oncrawl.
Why CRM and Data Warehouses Fail with Customer 360
This whitepaper, "Why CRM and Data Warehouses Fail with Customer 360," from our friends over at Profisee explains why achieving a complete view of the customer is so difficult and how customer relationship management (CRM) systems and data warehouses, especially in the insurance industry, do not manage customer-related data well. …
Data Platforms – A journey. The Yesteryears, Today, and What Lies Ahead
In this contributed article, Darshan Rawal, Founder and CEO of Isima, explains how the data ecosystem has exploded in the last decade to deal with multi-structured data sources. But the fundamental architecture of using queues, caches, and batches to support Enterprise Data Warehousing and BI hasn't. This article looks at …
Survey: 97% of Enterprises Seek to Accelerate Data Transformation, with Time Spent on Data Preparation A Barrier to Insights-Driven Decision-Making
Matillion, a leading provider of data transformation for cloud data warehouses (CDWs), and IDG Research have released findings of an IDG Research MarketPulse survey, “Gaining Time, Savings, and Insights via Cloud-Powered Data Transformation.” The research exposes the challenges companies face in leveraging enterprise data for analytics and identifies data portability, …
Databricks Launches SQL Analytics to Enable Cloud Data Warehousing on Data Lakes
Databricks, the data and AI company, announced the launch of SQL Analytics, which for the first time enables data analysts to perform workloads previously meant only for a data warehouse on a data lake. This expands the traditional scope of the data lake from data science and machine learning to …
How Automation Helps You Exploit the Value in Big Data
In this sponsored post, Simon Shah spearheads marketing at Redwood Software to support continued market growth and innovation for their cloud-based IT and business process automation solutions. He believes that by using automation to collect and manage your big data processes, you will truly exploit its value for the business.
Video Highlight: A Vision of Analytics — Challenge the Data Warehouse Status Quo
The pandemic and related disruptions have caused companies to think hard about their data and analytics strategies and how they get real-time answers. In the keynote presentation below, Yellowbrick CEO, Neil Carson, discusses why the single most important set of technologies a company should be investing in today is an …
Differentiating Between Data Lakes and Data Warehouses
The market for data warehouses is booming. One study forecasts that the market will be worth $23.8 billion by 2030. Demand is growing at an annual pace of 29%. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We …
The State of Data Management – Why Data Warehouse Projects Fail
Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and UK, this whitepaper explores the data management challenges organizations are facing, the vital role data warehouses play, and the road to success.
Data Warehouse Automation: Five Steps to Success
In this contributed article, Stan Geiger, director of multi-platform tools, which includes WhereScape data automation, at Idera, Inc., discusses how embracing data warehouse automation is not just a matter of implementing new tools or technologies.. Success can depend on building a number of key steps, ideas and processes into the …
83% of IT Leaders are Not Fully Satisfied with their Data Warehousing Initiatives, According to New Research from SnapLogic
New research published by SnapLogic, provider of the Intelligent Integration Platform, reveals that 83% of organizations are not fully satisfied with the performance and output of their data management and data warehousing initiatives. IT leaders cite a growing number of disconnected applications and data sources, outdated legacy systems, and slow …
What is the Difference Between Business Intelligence, Data Warehousing and Data Analytics
In this contributed article, Christopher Rafter, President and COO at Inzata,, writes that in the age of Big Data, you'll hear a lot of terms tossed around. Three of the most commonly used are "business intelligence," "data warehousing" and "data analytics." You may wonder, however, what distinguishes these three concepts …
Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance
This whitepaper from Imply Data Inc. explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they leveraged the same technology to create end-to-end real-time analytics architectures.
Introducing Apache Druid
Sponsored Post Apache Druid was invented to address the lack of a data store optimized for real-time analytics. Druid combines the best of real-time streaming analytics and multidimensional OLAP with the scale-out storage and computing principles of Hadoop to deliver ad hoc, search and time-based analytics against live data with …