Improving Transactional Applications with Analytics

MariaDB

Today, most web and mobile applications are limited to “lightweight” analytics because general-purpose databases can be optimized for transactional or analytical workloads, but not both – and since transactional processing is critical, applications have to compromise on analytics. However, what if an e-commerce application could let customers know which products are soon to be sold out based on clickstream data, shopping carts, current inventory and recent purchases as well as historical buying patterns and emerging shopping trends? In this webinar, attendees will learn how to leverage MariaDB ColumnStore to provide transactional applications with real-time analytics on historical data.
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Spotlight

Business intelligence (BI) has been around for a long time, and over the years has taken on many different forms – reporting, OLAP, ad hoc, performance management, predictive analytics, data mining, etc. For someone who’s new to the concept of BI, these various solutions can be quite confusing. Many potential users struggle to understand the differences between the numerous technologies and methodologies and find it difficult to prioritize them. But, the fact is that each facet of BI is important, and each plays a vital role in a company’s overall information strategy. However, few organizations truly understand how these different tools and techniques should be used together to drive efficiency and effectiveness across the entire enterprise. After more than 25 years in the industry, I have learned that BI is used in three distinct ways – strategically, analytically, and operationally. These three “levels” of business intelligence, while unique in their own way, are not mutually exclusive.

OTHER ON-DEMAND WEBINARS

Simplifying Analytics with Natural Language

Tableau

What if you could directly ask questions of your data? Ask Data, Tableau’s new natural language capability, allows people to get insights by simply conversing with their data. In this latest Data Science Central webinar, members of Tableau’s Ask Data team will demonstrate how they are lowering the barrier to analytics and leveraging Natural Language Processing (NLP) as a tool for visual analysis. Looking to quickly make smarter, data-informed decisions and empower others to do the same? Watch this detailed overview of Ask Data’s capabilities to learn more.
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Choosing an Analytical Cloud Data Platform

As organizations move into the cloud, the choices for handling high-scale data for analytical use are flourishing and evolving. How do we address BI/analytics, data science, security/application monitoring, and log data management workloads? Do we really need potentially overlapping warehouse, data lake, and security and observability capabilities on top of object storage, or can an evolved data lake or emerging “lakehouse” platform do it all? Join Doug Henschen, VP and principal analyst at Constellation Research and Thomas Hazel, Chief Technology and Science Officer at ChaosSearch for a broad-ranging discussion on the challenges and strategy considerations that go into choosing the right platform.
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Data Management for Successful AI

AI is seen by many as the best way to secure the future of their organisations, but there is significant public concern about its possible detrimental impact. Some are concerned about the concentration of power in the hands of huge tech companies, while some see automation as a threat to their employment.
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COMPETE WITH THE GIANTS 7 ELEMENTS OF A DATA STRATEGY

analytics8.com

For most companies, data is viewed as a problem instead of an asset. Data is often stuck in systems that dont talk to each other, manual processes affect data quality, and analytics tools arent providing clear insights. But those companies who use their data to drive business strategy are out-performing their competitors. To be more competitive in any industry, you must take advantage of the ever-growing amount of available data and that starts with a Data Strategy. A documented roadmap that clearly defines company goals and the specifics on how to get there will put you on the path towards data driven decision making.
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Spotlight

Business intelligence (BI) has been around for a long time, and over the years has taken on many different forms – reporting, OLAP, ad hoc, performance management, predictive analytics, data mining, etc. For someone who’s new to the concept of BI, these various solutions can be quite confusing. Many potential users struggle to understand the differences between the numerous technologies and methodologies and find it difficult to prioritize them. But, the fact is that each facet of BI is important, and each plays a vital role in a company’s overall information strategy. However, few organizations truly understand how these different tools and techniques should be used together to drive efficiency and effectiveness across the entire enterprise. After more than 25 years in the industry, I have learned that BI is used in three distinct ways – strategically, analytically, and operationally. These three “levels” of business intelligence, while unique in their own way, are not mutually exclusive.

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