Dynatrace

MAY 1, 2023

Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information. Here are the six steps of a typical ITOA process : Define the data infrastructure strategy. Identify data use cases and develop a scalable delivery model with documentation.

Analytics 193

Analytics Artificial Intelligence Big Data Open Source 193

Dynatrace

OCTOBER 4, 2022

Limited data availability constrains value creation. Modern IT environments — whether multicloud, on-premises, or hybrid-cloud architectures — generate exponentially increasing data volumes. Grail addresses today’s challenges of big data and cloud everywhere: Grail is highly scalable, cost-effective, and super-fast.

Analytics 240

Analytics Artificial Intelligence Storage Serverless 240

Dynatrace

APRIL 20, 2023

Logs highlight observability challenges Ingesting, storing, and processing the unprecedented explosion of data from sources such as software as a service, multicloud environments, containers, and serverless architectures can be overwhelming for today’s organizations.

Analytics 193

Analytics Infrastructure Storage Efficiency 193

Dynatrace

APRIL 25, 2023

Exploratory analytics with collaborative analytics capabilities can be a lifeline for CloudOps, ITOps, site reliability engineering, and other teams struggling to access, analyze, and conquer the never-ending deluge of big data. These analytics can help teams understand the stories hidden within the data and share valuable insights.

Analytics 206

Analytics Big Data Media Operating System 206

Dynatrace

JANUARY 16, 2023

Accordingly, for classic database use cases, organizations use a variety of relational databases and document stores. Specifically, they provide asynchronous communications within microservices architectures and high-throughput distributed systems.

Open Source 347

Open Source Java Operating System Programming 347

The Netflix TechBlog

SEPTEMBER 10, 2019

Instead of relying on engineers to productionize scientific contributions, we’ve made a strategic bet to build an architecture that enables data scientists to easily contribute. The two main challenges with this approach are establishing an easy contribution framework and handling Netflix’s scale of data.

Metrics 215

Metrics Architecture Infrastructure Innovation 215

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. In addition, this approach is more tailored for both structured as well unstructured data sets. Classic ETL.

Big Data 40

Big Data Retail Storage Google 40