Whitepapers
Datanami's white paper database contains reports from the leading thought-leaders and idea generators in the Datanami industry. 
Learn where Data, Analytics, and IT leaders are with their 2020 enterprise cloud strategy, their data and analytics priorities, and what their biggest challenges are. You’ll get key insights on their priorities around data virtualization, data governance, and data security across their on-premises and multi-cloud environments. Read more…
Kafka is not difficult to use, but it is tricky to optimize. It has evolved from essentially a message queue to a versatile streaming platform. Read the 10 golden rules of managing Kafka that will help you perfect your Kafka system and stay ahead of the curve. Read more…
There is a wide range of ML use cases that can help organizations grow. However, the technology is still primarily in the early mainstream adoption stage. At TDWI, they see that many organizations get stuck when they start to make use of the technology, which can prevent it from being used in other parts of (or even across) the organization. Read more…
This buyer’s guide lays out key considerations for organizations looking to apply data virtualization to their analytics use cases. Along with key features and capabilities, you’ll learn about the difference between data virtualization and query federation, drill down on caching techniques and get a detailed feature ranking worksheet for evaluating vendors in the space. Read more…
It’s not surprising that with the right data visualizations, you can tell a compelling data story and help your audience figure out what actions they should take next. To make this easier for everyone, GoodData asked data analytics experts for their insights and best practices regarding data visualizations, which are compiled into this ebook. Read more…
Data generated globally is growing at a massive rate that will increase exponentially as the predicted billions of IoT devices add their weight to the stack, reaching what analysts at IDC believe will be 79 trillion GBytes in 2025. Read more…
You have BIG Data. However, growing varieties, volumes, and velocities of data can strain your database and existing BI processes.
Trying to cope with these increases will unearth platform limitations that are not easily overcome and can be expensive to work around because legacy on-prem systems were never designed to handle the pressures of modern data demands. Read more…
After many fits and starts, the era of enterprise machine learning has finally arrived. According to 451 Research’s Voice of the Enterprise, AI and Machine Learning survey, 20% of enterprises have already deployed the technology and a further 33% plan to do so within one year. Read more…
Analytics and data are about people and empowering them. Analytics start with questions, from real people, at all levels of an organization. What if people in your organization could easily and accurately answer the 100, 200, or even 500 questions that would create significant value by using all the data in your organization at any time? Read more…
TigerGraph’s eBook “Native Parallel Graphs: The Next Generation of Graph Database for Real-Time Deep Link Analytics,” discusses what developers need to learn in order to leverage the power of graph analytics for the most complex business problems. Read more…
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June 22 - June 26