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

Many information professionals today are watching the burgeoning growth in data generation overwhelm their operational data stores. Traditional architectures struggle with large data volumes and unstructured data, such as information gleaned from log data or social media, and the amount of data these data stores must ingest and process today is creating performance bottlenecks. This is unacceptable in the current technological landscape, and organizations are scrambling to maintain an efficient operational data store (ODS) as the amount of information available—and necessary— to perform business-critical analyses steadily grows.

OTHER ON-DEMAND WEBINARS

Using DataOps for Data Pipeline Engineering Quality

tdwi.org

Data pipelines facilitate information flows and data exchange for a growing number of operational scenarios, including data extraction, transformation, and loading ETL into data warehouses and data marts, data migrations, production of BI reports, and application interoperability. When data engineers develop data pipelines, they may devise a collection of tests to guide the development process, but ongoing tests are not often put in place once those pipelines are put into production.
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Combining Data Management with Organizational Change

Global Data Strategy

Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
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Data Modeling at the Environment Agency of England Case Study

Global Data Strategy Ltd

The Environment Agency uses data models as a key part of their digital journey in reporting scientific results for water quality, fisheries, conservation and ecology, flood management, and more. Join special guest Becky Russell from the Environment Agency along with host Donna Burbank as they discuss how they were able to gain buy-in from various departments across the organization using data models and data standards
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Activate Your Data Governance Policy

DATAVERSITY

What does it mean to activate a Data Governance policy? Can an inactive policy be effective? Data Governance policies can address different things depending on the organization. Some policies are very general and introduce the awareness of formal Data Governance to the organization. Other policies address specific needs like Data Quality, data documentation, and data protection. Join Bob Seiner and a special guest for this RWDG webinar where they will tackle of the subject of how to develop and deploy an active Data Governance policy. Bob and his guest will provide specific examples of policy components and examples of how organizations use policies to govern their data.
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Spotlight

Many information professionals today are watching the burgeoning growth in data generation overwhelm their operational data stores. Traditional architectures struggle with large data volumes and unstructured data, such as information gleaned from log data or social media, and the amount of data these data stores must ingest and process today is creating performance bottlenecks. This is unacceptable in the current technological landscape, and organizations are scrambling to maintain an efficient operational data store (ODS) as the amount of information available—and necessary— to perform business-critical analyses steadily grows.

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