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The Evolution of Back-End Complexity Until recently, back-end architectures were relatively straightforward: monolithic applications ruled the landscape, with everything neatly contained within a single codebase. Developers could understand and manage the entire systems intricacies.
Dynatrace continues to deliver on its commitment to keeping your data secure in the cloud. Enhancing data separation by partitioning each customer’s data on the storage level and encrypting it with a unique encryption key adds an additional layer of protection against unauthorized data access.
What developers want Developers want to own their code in a distributed, ephemeral, cloud, microservices-based environment. It packages the existing Dynatrace capabilities needed by developers in their day-to-day worksuch as logs, distributed traces, profiling data, exceptions, and more.
DevOps and security teams managing today’s multicloud architectures and cloud-native applications are facing an avalanche of data. This has resulted in visibility gaps, siloed data, and negative effects on cross-team collaboration. At the same time, the number of individual observability and security tools has grown.
For more: Read the Report Employing cloud services can incur a great deal of risk if not planned and designed correctly. In fact, this is really no different than the challenges that are inherit within a single on-premises data center implementation.
Organizations continue to turn to multicloud architecture to deliver better, more secure software faster. But IT teams need to embrace IT automation and new data storage models to benefit from modern clouds. As they enlist cloud models, organizations now confront increasing complexity and a data explosion.
Move beyond logs-only security: Embrace a comprehensive, end-to-end approach that integrates all data from observability and security. More technology, more complexity The benefits of cloud-native architecture for IT systems come with the complexity of maintaining real-time visibility into security compliance and risk posture.
It can scale towards a multi-petabyte level data workload without a single issue, and it allows access to a cluster of powerful servers that will work together within a single SQL interface where you can view all of the data. This feature-packed database provides powerful and rapid analytics on data that scales up to petabyte volumes.
Created by Grafana Labs in 2018, Loki has rapidly emerged as a compelling alternative to traditional logging systems, particularly for cloud-native and Kubernetes environments. We can then parse structured log data to be formatted for our customized analysis needs. Loki can provide a comprehensive log journey.
Snowflake is a powerful cloud-based data warehousing platform known for its scalability and flexibility. To fully leverage its capabilities and improve efficient data processing, it's crucial to optimize query performance. Snowflake’s architecture consists of three main layers:
Key takeaways from this article on modern observability for serverless architecture: As digital transformation accelerates, organizations need to innovate faster and continually deliver value to customers. Companies often turn to serverless architecture to accelerate modernization efforts while simplifying IT management.
As a result, organizations are weighing microservices vs. monolithic architecture to improve software delivery speed and quality. Traditional monolithic architectures are built around the concept of large applications that are self-contained, independent, and incorporate myriad capabilities. What is monolithic architecture?
Without observability, the benefits of ARM are lost Over the last decade and a half, a new wave of computer architecture has overtaken the world. ARM architecture, based on a processor type optimized for cloud and hyperscale computing, has become the most prevalent on the planet, with billions of ARM devices currently in use.
As organizations scale their data operations in the cloud, optimizing Snowflake performance on AWS becomes crucial for maintaining efficiency and controlling costs. This comprehensive guide explores advanced techniques and best practices for maximizing Snowflake performance, backed by practical examples and implementation strategies.
The Texas Risk and Authorization Management Program (TX-RAMP) provides a standardized approach for security assessment, certification, and continuous monitoring of cloud computing services that process the data of Texas state agencies.
That’s why cloud cost optimization is becoming a major priority regardless of where organizations are on their digital transformation journeys. In fact, Gartner’s 2023 forecast is for worldwide public cloud spending to reach nearly $600 billion. These costs also have an environmental impact. Utilization.
For IT infrastructure managers and site reliability engineers, or SREs , logs provide a treasure trove of data. But on their own, logs present just another data silo as IT professionals attempt to troubleshoot and remediate problems. Data volume explosion in multicloud environments poses log issues.
Many organizations are taking a microservices approach to IT architecture. However, in some cases, an organization may be better suited to another architecture approach. Therefore, it’s critical to weigh the advantages of microservices against its potential issues, other architecture approaches, and your unique business needs.
Some time ago, at a restaurant near Boston, three Dynatrace colleagues dined and discussed the growing data challenge for enterprises. At its core, this challenge involves a rapid increase in the amount—and complexity—of data collected within a company. Work with different and independent data types. Grail architectural basics.
In fact, according to a Dynatrace global survey of 1,300 CIOs , 99% of enterprises utilize a multicloud environment and seven cloud monitoring solutions on average. What is cloud monitoring? Cloud monitoring is a set of solutions and practices used to observe, measure, analyze, and manage the health of cloud-based IT infrastructure.
More than 90% of enterprises now rely on a hybrid cloud infrastructure to deliver innovative digital services and capture new markets. That’s because cloud platforms offer flexibility and extensibility for an organization’s existing infrastructure. What is hybrid cloudarchitecture?
As an executive, I am always seeking simplicity and efficiency to make sure the architecture of the business is as streamlined as possible. Simplify data ingestion and up-level storage for better, faster querying : With Dynatrace, petabytes of data are always hot for real-time insights, at a cold cost.
However, with these benefits come complexities in terms of cloud management, Kubernetes observability, and automation, making it imperative for enterprises to address these intricacies to enhance reliability, performance, and resource usage. So many tools can result in data inconsistencies.
Cloud application security is becoming more of a critical issue as cloud-based applications gain popularity. The cloud allows a modular approach to building applications, enabling development and operations teams to create and deploy feature-rich apps very quickly. What is cloud application security?
This method of structuring, developing, and operating complex, multi-function software as a collection of smaller independent services is known as microservice architecture. Easy to leverage API interfaces connect services with core functionality, allowing applications to communicate and share data.
This method of structuring, developing, and operating complex, multi-function software as a collection of smaller independent services is known as microservice architecture. Easy to leverage API interfaces connect services with core functionality, allowing applications to communicate and share data.
Growing awareness and increasing regulatory scrutiny have propelled carbon emissions data into the public consciousness. How can you reduce the carbon footprint of your hybrid cloud? Evaluating these on three levels—data center, host, and application architecture (plus code)—is helpful. A PUE of 1.0
Cloud observability can bring business value, said Rick McConnell, CEO at Dynatrace. Organizations have clearly experienced growth, agility, and innovation as they move to cloud computing architecture. But without effective cloud observability, they continue to experience challenges in their cloud environments.
As cloud environments become increasingly complex, legacy solutions can’t keep up with modern demands. As a result, companies run into the cloud complexity wall – also known as the cloud observability wall – as they struggle to manage modern applications and gain multicloud observability with outdated tools.
Self-Service Progressive Delivery of Microservices, Automated SLI/SLO based Quality Gates, Continuous Feedback through ChatOps and Automatic Remediation of Production Issues are some of the capabilities you expect from a modern cloud-native software delivery platform. The recent improvements released in Keptn 0.6,
While data lakes and data warehousing architectures are commonly used modes for storing and analyzing data, a data lakehouse is an efficient third way to store and analyze data that unifies the two architectures while preserving the benefits of both. What is a data lakehouse?
As an example, cloud-based post-production editing and collaboration pipelines demand a complex set of functionalities, including the generation and hosting of high quality proxy content. As described by the white paper Apple ProRes ( link ), the target data rate of the Apple ProRes HQ for 1920x1080 at 29.97 is 220 Mbps.
More organizations than ever are undertaking cloud migration as digital transformation continues to gain momentum across every industry in every region. But what does it take to migrate your existing applications to the cloud? What is cloud migration? However, it can also mean migrating from one cloud to another.
ln a world driven by macroeconomic uncertainty, businesses increasingly turn to data-driven decision-making to stay agile. They’re unleashing the power of cloud-based analytics on large data sets to unlock the insights they and the business need to make smarter decisions. All of these factors challenge DevOps maturity.
A critical security threat for cloud-native architectures SSRF is a web security vulnerability that allows an attacker to make a server-side application send requests to unintended locations. SSRF can lead to unauthorized access to sensitive data, such as cloud metadata, internal databases, and other protected resources.
As a leader in cloud infrastructure and platform services , the Google Cloud Platform is fast becoming an integral part of many enterprises’ cloud strategies. Simplified cloud complexity with fully automated observability of Google Cloud. Dynatrace news.
Considering the latest State of Observability 2024 report, it’s evident that multicloud environments not only come with an explosion of data beyond humans’ ability to manage it. It’s increasingly difficult to ingest, manage, store, and sort through this amount of data. You can find the list of use cases here.
Leveraging cloud-native technologies like Kubernetes or Red Hat OpenShift in multicloud ecosystems across Amazon Web Services (AWS) , Microsoft Azure, and Google Cloud Platform (GCP) for faster digital transformation introduces a whole host of challenges. Collecting data requires massive and ongoing configuration efforts.
Indeed, organizations view IT modernization and cloud computing as intertwined with their business strategy and COVID-19 recovery plans. As a result, reliance on cloud computing for infrastructure and application development has increased during the pandemic era. Data confirms Aggarwal’s conclusions.
AIOps capabilities help IT teams cope with the overwhelming complexity of multicloud and hybrid cloud environments. In a preview video for Dynatrace Perform 2022, Joel Alcon, Dynatrace product marketing director of services, and Lauren Horwitz, content director at Dynatrace, discuss the role of causal AI in cloud observability.
As organizations face an increasingly competitive, dynamic, and disruptive macroeconomic environment, they have turned to cloud services and digitization for an edge. But as they embrace digital transformation in the cloud, organizations often confront significant challenges. Even though the cloud brings enormous complexity.”
Creating an ecosystem that facilitates data security and data privacy by design can be difficult, but it’s critical to securing information. When organizations focus on data privacy by design, they build security considerations into cloud systems upfront rather than as a bolt-on consideration.
Software reliability and resiliency don’t just happen by simply moving your software to a modern stack, or by moving your workloads to the cloud. The fact is, Reliability and Resiliency must be rooted in the architecture of a distributed system. Let me start with the end-user impact.
While many companies now enlist public cloud services such as Amazon Web Services, Google Public Cloud, or Microsoft Azure to achieve their business goals, a majority also use hybrid cloud infrastructure to accommodate traditional applications that can’t be easily migrated to public clouds.
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