Dynatrace

JUNE 29, 2022

To drive better outcomes using hybrid cloud architectures, it helps to understand their benefits—and how to orchestrate them seamlessly. What is hybrid cloud architecture? Hybrid cloud architecture is a computing environment that shares data and applications on a combination of public clouds and on-premises private clouds.

Infrastructure 187

Infrastructure Cloud Azure AWS 187

The Netflix TechBlog

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. The most notable example is memory configuration errors. the retry success probability) and compute cost efficiency (i.e.,

Tuning 210

Tuning Efficiency Big Data Engineering 210

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202

Java Scalability Traffic Architecture 202

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

ACM Sigarch

APRIL 8, 2019

Computer architecture is an important and exciting field of computer science, which enables many other fields (eg. big-data processing, machine learning, quantum computing, and so on). For those of us who pursued computer architecture as a career, this is well understood. Why is that? Should we be alarmed as a community?

Architecture 74

Architecture Open Source Hardware Software Engineering 74

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. Logs on Grail Log data is foundational for any IT analytics.

Analytics 191

Analytics Innovation Metrics Database 191

The Netflix TechBlog

MAY 4, 2023

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. For example, if some fields in the responses are timestamps, those will differ.

Traffic 339

Traffic Latency Tuning Systems 339

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

The Netflix TechBlog

JULY 26, 2021

This happens at an unprecedented scale and introduces many interesting challenges; one of the challenges is how to provide visibility of Studio data across multiple phases and systems to facilitate operational excellence and empower decision making. We will cover a few core concepts in the Data Mesh Schema domain. See example below.

Big Data 253

Big Data Government Analytics Processing 253

The Netflix TechBlog

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Nonetheless, Netflix data landscape (see below) is complex and many teams collaborate effectively for sharing the responsibility of our data system management.

Infrastructure 219

Infrastructure Big Data Transportation Architecture 219

Dynatrace

JULY 6, 2022

Vulnerability management is one example of a DevSecOps workflow that teams should automate to ensure vulnerability scans run regularly. This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools.

Artificial Intelligence 194

Artificial Intelligence Tuning Strategy Big Data 194

Dynatrace

JANUARY 11, 2023

As cloud and big data complexity scales beyond the ability of traditional monitoring tools to handle, next-generation cloud monitoring and observability are becoming necessities for IT teams. For example, uptime detection can identify database instability and help to improve mean time to restoration. What is cloud monitoring?

Cloud 227

Cloud Monitoring Best Practices Infrastructure 227

The Morning Paper

OCTOBER 26, 2020

Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. These two narratives of reference architecture and ingestion/indexing system are interwoven throughout the paper. Why do we need a new reference architecture?

Cloud 104

Cloud Big Data Latency Architecture 104

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., We’re not told how Seer figures out that a major architectural change has happened. ASPLOS’19. It then provides the cluster manager with recommendations on how to avoid the performance degradation altogether.

Big Data 45

Big Data Cloud Performance Hardware 45

Dynatrace

APRIL 11, 2023

Cloud application security remains challenging because organizations lack end-to-end visibility into cloud architecture. As organizations migrate applications to the cloud, they must balance the agility that microservices architecture brings with the complexity and lack of transparency that can also come with it.

Cloud 192

Cloud DevOps Open Source Retail 192

The Netflix TechBlog

OCTOBER 27, 2020

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3. An Example of Schema Mapping.

Latency 243

Latency Storage Big Data Tuning 243

ScaleOut Software

JULY 19, 2021

Today’s streaming analytics architectures are not equipped to make sense of this rapidly changing information and react to it as it arrives. This data is also periodically uploaded to a data lake for offline batch analysis that calculates key statistics and looks for big trends that can help optimize operations.

IoT 78

IoT Analytics Big Data Architecture 78

The Netflix TechBlog

NOVEMBER 20, 2023

To compensate for that, ETL workflows often use a lookback window, based on which they reprocess the data in that certain time window. For example, a job would reprocess aggregates for the past 3 days because it assumes that there would be late arriving data, but data prior to 3 days isn’t worth the cost of reprocessing.

Processing 210

Processing Big Data Efficiency Engineering 210

The Netflix TechBlog

JUNE 1, 2021

For example?—?clinical clinical data was often small enough to fit into memory on an average computer and only in rare cases would its computation require any technical ingenuity or massive computing power. However, most challenges that came with my role were domain-related but not as technically demanding.

Data Engineering 130

Data Engineering Engineering Big Data Healthcare 130

Dynatrace

JULY 6, 2020

Our customers have frequently requested support for this first new batch of services, which cover databases, big data, networks, and computing. Let’s look at the Azure DB for MariaDB overview as an example. See the health of your big data resources at a glance. Azure Virtual Network Gateways.

Azure 150

Azure Cloud Big Data Virtualization 150

The Netflix TechBlog

NOVEMBER 3, 2023

An example of a Data Mesh pipeline which moves and transforms data using Union, GraphQL Enrichment, and Column Rename Processor before writing to an Iceberg table. The existing Data Mesh Processors have a lot of overlap with SQL.

Processing 265

Processing Engineering Infrastructure Latency 265

Dynatrace

OCTOBER 17, 2022

Artificial intelligence for IT operations, or AIOps, combines big data and machine learning to provide actionable insight for IT teams to shape and automate their operational strategy. Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. Achieving autonomous operations.

DevOps 202

DevOps Artificial Intelligence Healthcare Innovation 202

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. Take this situation as an example: When multiple problems happen in parallel the introduction of the “unhealthy situation” concept can reduce the number of support tickets. But that didn’t work for me. Visualizing problem noise.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

OCTOBER 14, 2021

Gartner defines AIOps as the combination of “big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.” Let’s say, for example, an application is experiencing a slowdown in receiving its search requests. What is AIOps?

Artificial Intelligence 218

Artificial Intelligence DevOps Innovation Metrics 218

The Netflix TechBlog

OCTOBER 15, 2019

For example, XA transactions block execution if the application process fails during the prepare phase; moreover, XA provides no deadlock detection and no support for optimistic concurrency-control schemes. In Netflix the microservice architecture is widely adopted and each microservice typically handles only one type of data.

Transportation 192

Transportation Architecture Processing Storage 192

Abhishek Tiwari

DECEMBER 27, 2017

A common theme across all these trends is to remove the complexity by simplifying data management as a whole. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Unified data management architecture.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

ScaleOut Software

NOVEMBER 24, 2020

However, telematics architectures face challenges in responding to telemetry in real time. Current Telematics Architecture. The volume of incoming telemetry challenges current telematics systems to keep up and quickly make sense of all the data. Challenges for Current Architectures.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Percona

JANUARY 19, 2023

However, there are cases where the same column is defined on multiple indexes in order to serve different query patterns, and sometimes some of the indexes created for the same column are redundant, leading to more overhead when inserting or deleting data (as indexes are updated) and increased disk space for storing the indexes for the table.

Open Source 84

Open Source Storage Database Big Data 84

All Things Distributed

JUNE 26, 2016

Here are the benefits of a comprehensive platform, with customer examples: A connected platform to sense the business environment. Examples of continuous sensing are found in the managed cloud platform built by Rachio on AWS IoT to enable the secure interaction of its connected devices with cloud applications/other devices.

AWS 90

AWS Cloud Healthcare Blockchain 90

Rigor

JANUARY 13, 2020

Take, for example, The Web Almanac , the golden collection of Big Data combined with the collective intelligence from most of the authors listed below, brilliantly spearheaded by Google’s @rick_viscomi. Information Architecture. Progressive Web App Dev by Example. Using Webpagetest. Progressive Web Apps Dean.

Performance 75

Performance Social Media Website Website Performance 75

All Things Distributed

SEPTEMBER 26, 2014

Be sure to bring your questions about AWS architecture, cost optimization, services and features, and anything else AWS-related. Topics include Introduction to AWS, Big Data, Compute & Networking, Architecture, Mobile & Gaming, Databases, Operations, Security, and more. And don’t be shy—walk-ins are welcome too.

AWS 111

AWS Games Education Innovation 111

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. Different audience.

Big Data 40

Big Data Retail Storage Google 40

The Morning Paper

JUNE 23, 2019

The stateless + RInk (S+RInK) architecture attempts to provide the best of both worlds: to simultaneously offer both the implementation and operational simplicity of stateless application servers and the performance benefits of servers caching state in RAM. We’ve seen similar high marshalling overheads in big data systems too.)

Cache 79

Cache Latency Google Lambda 79

All Things Distributed

DECEMBER 8, 2016

Some examples of how current customers use AWS are: Cost-effective solutions. It adopted Amazon Redshift, Amazon EMR and AWS Lambda to power its data warehouse, big data, and data science applications, supporting the development of product features at a fraction of the cost of competing solutions.

AWS 155

AWS Cloud Lambda Innovation 155

The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 203

Storage Latency Efficiency Data Engineering 203

All Things Distributed

DECEMBER 20, 2017

In today's era of global digitalization there are many examples that show that IT does matter. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster. More than mere support.

Artificial Intelligence 79

Artificial Intelligence Social Media Logistics AWS 79

All Things Distributed

APRIL 27, 2011

To our shareowners: Random forests, naïve Bayesian estimators, RESTful services, gossip protocols, eventual consistency, data sharding, anti-entropy, Byzantine quorum, erasure coding, vector clocks. Look inside a current textbook on software architecture, and youll find few patterns that we dont apply at Amazon.

Technology 151

Technology Technology AWS Storage 151