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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.

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 strategy.

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 IT leaders cite a growing number of disconnected applications and data sources, outdated legacy systems, and slow and manual data movement as reasons for their frustration, all of which are stalling progress and costing them millions.

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 from each other so let’s take a look.

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.

Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance

This whitepaper from Imply Data Inc. introduces Apache Druid and 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 sub-second end-to-end response times. Today, […]

Introducing Apache Druid

This whitepaper provides an introduction to Apache Druid, including its evolution,
core architecture and features, and common use cases. Founded by the authors of the Apache Druid database, Imply provides a cloud-native solution that delivers real-time ingestion, interactive ad-hoc queries, and intuitive visualizations for many types of event-driven and streaming data flows.

Hope is Not a Strategy for Deriving Value from a Data Lake

In this contributed article, Christian Kleinerman, VP of Product at Snowflake, discusses how the data lake has evolved since arriving on the technology scene 10+ years ago and why organizations need more than “hope” to unlock the potential it promises.