Remove Azure Remove Processing Remove Speed Remove Storage
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What is a data lakehouse? Combining data lakes and warehouses for the best of both worlds

Dynatrace

A data lakehouse features the flexibility and cost-efficiency of a data lake with the contextual and high-speed querying capabilities of a data warehouse. Data warehouses offer a single storage repository for structured data and provide a source of truth for organizations. Massively parallel processing. Data management.

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Transactions with Azure Table Persistence

Particular Software

For data storage alone, Azure offers: Table Storage, CosmosDB, SQL Server, Blob Storage, and more. With the latest release of Azure Table Persistence for NServiceBus, we offer full transactionality across Outbox, Synchronized Storage Session as well as Sagas. No more worrying about consistency.

Azure 52
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Enhance data management with Grail: Ultimate guide to custom buckets and security policies

Dynatrace

Buckets are similar to folders, a physical storage location. Debug-level logs, which also generate high volumes and have a shorter lifespan or value period than other logs, could similarly benefit from dedicated storage. This improves query speeds and reduces related costs for all other teams and apps.

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Azure Virtual Machines for SQL Server Usage

SQL Performance

One initial, easy step to moving your SQL Server on-premises workloads to the cloud is using Azure VMs to run your SQL Server workloads in an infrastructure as a service (IaaS) scenario. One important choice you will still have to make is what type and size of Azure virtual machine you want to use for your existing SQL Server workload.

Azure 72
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What is Greenplum Database? Intro to the Big Data Database

Scalegrid

Greenplum Database is a massively parallel processing (MPP) SQL database that is built and based on PostgreSQL. High performance, query optimization, open source and polymorphic data storage are the major Greenplum advantages. Greenplum uses an MPP database design that can help you develop a scalable, high performance deployment.

Big Data 321
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Easy SLA and SLO reporting for all your API endpoints with public synthetic HTTP monitors

Dynatrace

With today’s high expectations for the speed and availability of applications, you need a deep understanding of real user experiences to make the best business decisions. Here is the first batch of 15 public locations for HTTP monitoring: Chicago (Azure) ?, Virginia (Azure), N. London (AWS), London (Azure), Frankfurt (AWS) ?,

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Implementing AWS well-architected pillars with automated workflows

Dynatrace

And how can you verify this performance consistently across a multicloud environment that also uses Microsoft Azure and Google Cloud Platform frameworks? This process enables you to continuously evaluate software against predefined quality criteria and service level objectives (SLOs) in pre-production environments.

AWS 236