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Medallion Architecture: Efficient Batch and Stream Processing Data Pipelines With Azure Databricks and Delta Lake

DZone

In today's data-driven world, organizations need efficient and scalable data pipelines to process and analyze large volumes of data. Medallion Architecture provides a framework for organizing data processing workflows into different zones, enabling optimized batch and stream processing.

Azure 246
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Key Advantages of DBMS for Efficient Data Management

Scalegrid

Enhanced data security, better data integrity, and efficient access to information. Despite initial investment costs, DBMS presents long-term savings and improved efficiency through automated processes, efficient query optimizations, and scalability, contributing to enhanced decision-making and end-user productivity.

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Perform 2023 Guide: Organizations mine efficiencies with automation, causal AI

Dynatrace

They now use modern observability to monitor expanding cloud environments in order to operate more efficiently, innovate faster and more securely, and to deliver consistently better business results. Further, automation has become a core strategy as organizations migrate to and operate in the cloud.

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The state of observability in 2024: Accelerating transformation with AI, analytics, and automation

Dynatrace

Kubernetes adds to the complexity of technology stacks Alongside the challenges of managing multicloud environments, IT and security teams struggle to maintain visibility into cloud-native architectures as Kubernetes continues to become the platform of choice for modern applications.

Analytics 187
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Building an elastic query engine on disaggregated storage

The Morning Paper

Building an elastic query engine on disaggregated storage , Vuppalapati, NSDI’20. For such workloads, shared-nothing architectures beget high cost, inflexibility, poor performance, and inefficiency, which hurts production applications and cluster deployments. joins) during query processing. Disaggregation (or not).

Storage 112
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Pioneering customer-centric pricing models: Decoding ingest-centric vs. answer-centric pricing

Dynatrace

The rise of cloud-native microservice architectures further exacerbates this change. Dynatrace has developed the purpose-built data lakehouse, Grail , eliminating the need for separate management of indexes and storage. All data is readily accessible without storage tiers, such as costly solid-state drives (SSDs).

Retail 233
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Measuring the importance of data quality to causal AI success

Dynatrace

It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration. Modern, cloud-native architectures have many moving parts, and identifying them all is a daunting task with human effort alone.