This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
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. A unique encryption key is applied to each tenant’s storage and automatically rotated every 365 days.
As a result, organizations are implementing security analytics to manage risk and improve DevSecOps efficiency. Fortunately, CISOs can use security analytics to improve visibility of complex environments and enable proactive protection. What is security analytics? Why is security analytics important? Here’s how.
This is explained in detail in our blog post, Unlock log analytics: Seamless insights without writing queries. There is no need to think about schema and indexes, re-hydration, or hot/cold storage. Using patent-pending high ingest stream-processing technologies, OpenPipeline currently optimizes data for Dynatrace analytics and AI at 0.5
This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. Kafka is optimized for high-throughput event streaming , excelling in real-time analytics and large-scale data ingestion. What is RabbitMQ? What is Apache Kafka?
Log management and analytics is an essential part of any organization’s infrastructure, and it’s no secret the industry has suffered from a shortage of innovation for several years. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes.
In this blog post, we explain what Greenplum is, and break down the Greenplum architecture, advantages, major use cases, and how to get started. Greenplum Database is an open-source , hardware-agnostic MPP database for analytics, based on PostgreSQL and developed by Pivotal who was later acquired by VMware. The Greenplum Architecture.
The latest Dynatrace report, “ The state of observability 2024: Overcoming complexity through AI-driven analytics and automation ,” explores these challenges and highlights how IT, business, and security teams can overcome them with a mature AI, analytics, and automation strategy.
Log monitoring, log analysis, and log analytics are more important than ever as organizations adopt more cloud-native technologies, containers, and microservices-based architectures. What is log analytics? Log analytics is the process of evaluating and interpreting log data so teams can quickly detect and resolve issues.
Today’s digital businesses run on heterogeneous and highly dynamic architectures with interconnected applications and microservices deployed via Kubernetes and other cloud-native platforms. Common questions include: Where do bottlenecks occur in our architecture? Dynatrace extends its unique topology-based analytics and AIOps approach.
Analytical Insights Additionally, impression history offers insightful information for addressing a number of platform-related analytics queries. Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset.
Traditionally, though, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs. IT pros want a data and analytics solution that doesn’t require tradeoffs between speed, scale, and cost. Enter Grail-powered data and analytics.
In serverless and microservices architectures, messaging systems are often used to build asynchronous service-to-service communication. Messaging systems are typically implemented as lightweight storage represented by queues or topics. Seamless observability of messaging systems is critical for DevOps teams. New to Dynatrace?
Grail combines the big-data storage of a data warehouse with the analytical flexibility of a data lake. With Grail, we have reinvented analytics for converged observability and security data,” Greifeneder says. Logs on Grail Log data is foundational for any IT analytics. Grail and DQL will give you new superpowers.”
Grail needs to support security data as well as business analytics data and use cases. With that in mind, Grail needs to achieve three main goals with minimal impact to cost: Cope with and manage an enormous amount of data —both on ingest and analytics. Grail architectural basics. Work with different and independent data types.
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.
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. Unlike data warehouses, however, data is not transformed before landing in storage.
These traditional approaches to log monitoring and log analytics thwart IT teams’ goal to address infrastructure performance problems, security threats, and user experience issues. Further, these resources support countless Kubernetes clusters and Java-based architectures. where an error occurred at the code level.
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. From a technical perspective, however, cloud-based analytics can be challenging. Research has found that 99% of organizations have embraced a multicloud architecture.
Simpler UI Testing with CasperJS ( Architects Zone – Architectural Design Patterns & Best Practices). Using MongoDB as a cache store ( Architects Zone – Architectural Design Patterns & Best Practices). Google Analytics Becomes A Robust Testing Platform With Content Experiments API ( Google Analytics Blog).
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. Log management and analytics have become a particular challenge. Data lakehouse architecture addresses data explosion.
A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. Understanding distributed storage is imperative as data volumes and the need for robust storage solutions rise.
Causal AI—which brings AI-enabled actionable insights to IT operations—and a data lakehouse, such as Dynatrace Grail , can help break down silos among ITOps, DevSecOps, site reliability engineering, and business analytics teams. Logs are automatically produced and time-stamped documentation of events relevant to cloud architectures.
Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. 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.
In what follows, we explore some key cloud observability trends in 2023, such as workflow automation and exploratory analytics. From data lakehouse to an analytics platform Traditionally, to gain true business insight, organizations had to make tradeoffs between accessing quality, real-time data and factors such as data storage costs.
A modern observability and analytics platform brings data silos together and facilitates collaboration and better decision-making among teams. But making decisions about which data to store in easily accessible hot storage upfront requires IT pros to know which questions they want to ask ahead of time and ensure that data is indexed.
Architecture. Firstly, the synchronous process which is responsible for uploading image content on file storage, persisting the media metadata in graph data-storage, returning the confirmation message to the user and triggering the process to update the user activity. Sending and receiving messages from other users.
Cassandra serves as the backbone for a diverse array of use cases within Netflix, ranging from user sign-ups and storing viewing histories to supporting real-time analytics and live streaming. Data Model At its core, the KV abstraction is built around a two-level map architecture.
Trace your application Imagine a microservices architecture with hundreds of dependencies. There is no need to think about schema and indexes, re-hydration, or hot/cold storage. This architecture also means you’re not required to determine your log data use cases beforehand or while analyzing logs within the new logs app.
Cloud-native technologies and microservice architectures have shifted technical complexity from the source code of services to the interconnections between services. Heterogeneous cloud-native microservice architectures can lead to visibility gaps in distributed traces. Dynatrace news. Deep-code execution details.
Enterprise data stores grow with the promise of analytics and the use of data to enable behavioral security solutions, cognitive analytics, and monitoring and supervision. ” This data is excluded from storage, but teams can still gain value from data enrichment beforehand. Why perform exclusion at two points? Encryption.
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. Suppose a single Grail environment is central storage for pre-production and production systems.
Across the cloud operations lifecycle, especially in organizations operating at enterprise scale, the sheer volume of cloud-native services and dynamic architectures generate a massive amount of data. In general, generative AI can empower AWS users to further accelerate and optimize their cloud journeys. What is predictive AI? What is AIOps?
The increasing complexity of cloud service architectures requires a rock-solid understanding of the activity, health status, and security of cloud services. Many AWS services and third party solutions use AWS S3 for log storage.
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). Transparency.
Security policies give authorized users access to Dynatrace capabilities, such as Grail and its add-ons, exploratory analytics, and Dynatrace AppEngine. The new architecture enables more granularity in permission management and provides the dynamics necessary to serve modern access management use cases.
Power business analytics with Dynatrace Banks that can deploy vertically integrated risk management solutions will deliver unprecedented agility, precision, and control for risk management functions. Combine traditional fraud detection techniques with data science and analytics to combat financial crime more effectively.
Traditional log management solution challenges Survey data suggests that teams need a modern approach to log management and analytics, which requires a unified log management solution. Dynatrace Log Management and Analytics provides a unified and comprehensive log management solution. during 2021–2026.
Network traffic growth is the main reason for increasing spending, largely because of the adoption of hybrid and multi-cloud architectures. It’s more complex than it sounds.” As cloud entities multiply, along with greater reliance on microservices and serverless architectures, so do the complex relationships and dependencies among them.
In previous blog posts, we introduced the Key-Value Data Abstraction Layer and the Data Gateway Platform , both of which are integral to Netflix’s data architecture. We do not use it for metrics, histograms, timers, or any such near-real time analytics use case. Those use cases are well served by the Netflix Atlas telemetry system.
AI-powered precise answers and timely insights with ad-hoc analytics. Successful platform adoption requires a platform to be easy to use and integrate with other architectures, applications, and data sources. Learn more about the Dynatrace platform and its Cloud Done Right architecture. Automation at scale.
Output plugins deliver logs to storage solutions, analytics tools, and observability platforms like Dynatrace. Detailed performance analysis for better software architecture and resource allocation. First and foremost, both approaches equally enrich Kubernetes logs with metadata for enhanced analytics.
All this is easier said than done because: Kubernetes-based dynamic architecture is becoming the norm. This allows you to create flexible and powerful log storage configurations on any level by utilizing the unique autodiscovery capabilities of Dynatrace OneAgent or a custom setup. Try it out yourself.
This “Enterprise Data Model/Architect Agent” employs generative AI techniques for autonomous enterprise data modeling and architecture. Clark Wright, Staff Analytics Engineer at Airbnb, talked about the concept of Data Quality Score at Airbnb.
Problems include provisioning and deployment; load balancing; securing interactions between containers; configuration and allocation of resources such as networking and storage; and deprovisioning containers that are no longer needed. How does container orchestration work? The post What is container orchestration?
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content