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Enhancing Azure data analytics and Azure observability with Dynatrace Grail

Dynatrace

Azure observability and Azure data analytics are critical requirements amid the deluge of data in Azure cloud computing environments. requires Azure observability Data has become a pivotal asset in the current IT landscape, and AI has unequivocally become the linchpin for differentiation. Digital transformation 2.0

Azure 188
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Expanded Grail data lakehouse and new Dynatrace user experience unlock boundless analytics

Dynatrace

Grail – the foundation of exploratory analytics Grail can already store and process log and business events. Now we’re adding Smartscape to DQL and two new data sources to Grail: Metrics on Grail and Traces on Grail. Ensuring observability across these environments requires access to data at a massive scale.

Analytics 238
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Building an Optimized Data Pipeline on Azure Using Spark, Data Factory, Databricks, and Synapse Analytics

DZone

Data processing in the cloud has become increasingly popular due to its scalability, flexibility, and cost-effectiveness. This article will explore how these technologies can be used together to create an optimized data pipeline for data processing in the cloud.

Azure 246
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What is IT operations analytics? Extract more data insights from more sources

Dynatrace

With 99% of organizations using multicloud environments , effectively monitoring cloud operations with AI-driven analytics and automation is critical. IT operations analytics (ITOA) with artificial intelligence (AI) capabilities supports faster cloud deployment of digital products and services and trusted business insights.

Analytics 197
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The top four log analytics and log management best practices

Dynatrace

The growing challenge in modern IT environments is the exponential increase in log telemetry data, driven by the expansion of cloud-native, geographically distributed, container- and microservice-based architectures. Organizations need a more proactive approach to log management to tame this proliferation of cloud data.

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Dynatrace OpenPipeline: Stream processing data ingestion converges observability, security, and business data at massive scale for analytics and automation in context

Dynatrace

Organizations choose data-driven approaches to maximize the value of their data, achieve better business outcomes, and realize cost savings by improving their products, services, and processes. However, there are many obstacles and limitations along the way to becoming a data-driven organization. Understanding the context.

Analytics 200
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Dynatrace completed Data Privacy Framework self-certification

Dynatrace

In trans-Atlantic and global business relationships, the privacy frameworks and regulations in various regions must be aligned to allow efficient collaboration between enterprises and other involved institutions. To enable participating organizations to meet the EU requirements for transferring personal data to the U.S., What’s next?