Dynatrace

JUNE 26, 2023

In what follows, we define software automation as well as software analytics and outline their importance. What is software analytics? This involves big data analytics and applying advanced AI and machine learning techniques, such as causal AI. We also discuss the role of AI for IT operations (AIOps) and more.

Software 183

Software Software Analytics Big Data 183

Highly Scalable

AUGUST 20, 2013

The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. Towards Unified Big Data Processing. Elmagarmid, Data Streams Models and Algorithms. Marz, “Big Data Lambda Architecture”. Apache Spark [10]. References.

Big Data 154

Big Data Processing Lambda Database 154

DZone

FEBRUARY 23, 2023

Introduction With big data streaming platform and event ingestion service Azure Event Hubs , millions of events can be received and processed in a single second. Any real-time analytics provider or batching/storage adaptor can transform and store data supplied to an event hub.

Azure 294

Azure Big Data Analytics Storage 294

The Netflix TechBlog

JULY 15, 2021

Interview with Kevin Wylie This post is part of our “Data Engineers of Netflix” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Kevin Wylie is a Data Engineer on the Content Data Science and Engineering team. What drew you to Netflix?

Data Engineering 189

Data Engineering Engineering Entertainment Big Data 189

The Morning Paper

DECEMBER 13, 2020

An overview of end-to-end entity resolution for big data , Christophides et al., It’s an important part of many modern data workflows, and an area I’ve been wrestling with in one of my own projects. . ACM Computing Surveys, Dec. 2020, Article No.

Big Data 83

Big Data Open Source Processing Analytics 83

All Things Distributed

AUGUST 18, 2011

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. Driving down the cost of Big-Data analytics.

Big Data 112

Big Data Analytics AWS Scalability 112

DZone

FEBRUARY 1, 2023

Built on Azure Blob Storage, Azure Data Lake Storage Gen2 is a suite of features for big data analytics. Azure Data Lake Storage Gen1 and Azure Blob Storage's capabilities are combined in Data Lake Storage Gen2.

Azure 243

Azure Storage Big Data Analytics 243

DZone

AUGUST 23, 2023

In the era of big data, efficient data management and query performance are critical for organizations that want to get the best operational performance from their data investments.

Big Data 130

Big Data Analytics Innovation Efficiency 130

DZone

JULY 17, 2023

Apache Spark is a leading platform in the field of big data processing, known for its speed, versatility, and ease of use. Understanding Apache Spark Apache Spark is a unified computing engine designed for large-scale data processing. However, getting the most out of Spark often involves fine-tuning and optimization.

Big Data 130

Big Data Performance Open Source Tuning 130

The Morning Paper

SEPTEMBER 8, 2019

Experiences with approximating queries in Microsoft’s production big-data clusters Kandula et al., I’ve been excited about the potential for approximate query processing in analytic clusters for some time, and this paper describes its use at scale in production. VLDB’19. Approximate query support.

Big Data 57

Big Data Analytics Latency Azure 57

The Netflix TechBlog

OCTOBER 28, 2021

Netflix’s unique work culture and petabyte-scale data problems are what drew me to Netflix. During earlier years of my career, I primarily worked as a backend software engineer, designing and building the backend systems that enable big data analytics.

Data Engineering 173

Data Engineering Engineering Big Data Software Engineering 173

Dynatrace

OCTOBER 4, 2022

Generally, the storage technology categorizes data into landing, raw, and curated zones depending on its consumption readiness. The result is a framework that offers a single source of truth and enables companies to make the most of advanced analytics capabilities simultaneously. Support diverse analytics workloads.

Artificial Intelligence 205

Artificial Intelligence Storage Analytics Government 205

DZone

JUNE 6, 2023

This can include the use of cloud computing, artificial intelligence, big data analytics, the Internet of Things (IoT), and other digital tools. The digital transformation of businesses involves the adoption of digital technologies to change the way companies operate and deliver value to their customers.

Artificial Intelligence 130

Artificial Intelligence Systems IoT Testing 130

Scalegrid

FEBRUARY 8, 2024

Within Amazon S3’s offerings are features like metadata tagging, different classes of data movement and storage options, configuring control over access permissions, and ensuring safety against disasters through data replication mechanisms.

Storage 130

Storage Systems Azure Big Data 130

Dynatrace

APRIL 19, 2023

Business Insights is a managed offering built on top of Dynatrace’s digital experience and business analytics tools. The Business Insights team helps customers manage or configure their digital experience environment, extend the Dynatrace platform through data analytics, and bring human expertise into optimization.

Entertainment 155

Entertainment Analytics Healthcare Games 155

The Netflix TechBlog

JANUARY 13, 2022

This blog will explore these two systems and how they perform auto-diagnosis and remediation across our Big Data Platform and Real-time infrastructure. The streaming platform recently added Data Mesh , and we need to expand Streaming Pensive to cover that. In the future, we are looking to automate this process.

Big Data 238

Big Data Infrastructure Metrics Architecture 238

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

ScaleOut Software

JUNE 15, 2020

When analyzing telemetry from a large population of data sources, such as a fleet of rental cars or IoT devices in “smart cities” deployments, it’s difficult if not impossible for conventional streaming analytics platforms to track the behavior of each individual data source and derive actionable information in real time.

Analytics 52

Analytics IoT Lambda Big Data 52

Dynatrace

APRIL 20, 2023

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. Dynatrace Grail unifies data from logs, metrics, traces, and events within a real-time model.

Analytics 183

Analytics Infrastructure Storage Retail 183

DZone

DECEMBER 28, 2020

Digital transformation is yet another significant focus point for the sectors and the enterprises that are ranking top on cloud and business analytics. Nowadays, Big Data tests mainly include data testing, paving the way for the Internet of Things to become the center point. Besides, AI and ML seem to reach a new level.

Software 186

Software Software Testing Big Data 186

ScaleOut Software

JULY 19, 2021

Real-Time Device Tracking with In-Memory Computing Can Fill an Important Gap in Today’s Streaming Analytics Platforms. The Limitations of Today’s Streaming Analytics. How are we managing the torrent of telemetry that flows into analytics systems from these devices? The list goes on.

IoT 78

IoT Analytics Big Data Architecture 78

Dynatrace

OCTOBER 25, 2022

“AIOps platforms address IT leaders’ need for operations support by combining big data and machine learning functionality to analyze the ever-increasing volume, variety and velocity of data generated by IT in response to digital transformation.” – Gartner Market Guide for AIOps platforms. Evolution of modern AIOps.

DevOps 213

DevOps Big Data Cloud Innovation 213

The Netflix TechBlog

SEPTEMBER 18, 2020

Part of our series on who works in Analytics at Netflix?—?and and what the role entails by Julie Beckley & Chris Pham This Q&A provides insights into the diverse set of skills, projects, and culture within Data Science and Engineering (DSE) at Netflix through the eyes of two team members: Chris Pham and Julie Beckley.

Analytics 223

Analytics Education Innovation Engineering 223

Dynatrace

APRIL 21, 2021

On the Dynatrace Business Insights team, we have developed analytical views and an approach to help you get started. To do this effectively, you need a big data processing approach. The three challenges to optimizing Core Web Vitals is exactly why the Dynatrace Business Insights team have built the Insights Analytics Engine.

Traffic 263

Traffic Metrics Mobile Analytics 263

Dynatrace

JULY 6, 2022

This kind of automation can support key IT operations, such as infrastructure, digital processes, business processes, and big-data automation. Big data automation tools. These tools provide the means to collect, transfer, and process large volumes of data that are increasingly common in analytics applications.

Artificial Intelligence 185

Artificial Intelligence Strategy Tuning Big Data 185

Scalegrid

MARCH 14, 2024

Workloads from web content, big data analytics, and artificial intelligence stand out as particularly well-suited for hybrid cloud infrastructure owing to their fluctuating computational needs and scalability demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

Highly Scalable

MAY 1, 2012

Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce.

Analytics 191

Analytics Traffic Big Data Efficiency 191

DZone

MAY 5, 2021

Anytime, every time or sometime you would have heard someone going around with data analysis and saying maybe this could have happened because of this, maybe users did not like the feature or maybe we were wrong all the time. Any analysis and prediction in data analytics across industries experience what I call maybe syndrome.

Analytics 195

Analytics Testing Big Data Code 195

Dynatrace

DECEMBER 15, 2022

AIOps (artificial intelligence for IT operations) combines big data, AI algorithms, and machine learning for actionable, real-time insights that help ITOps continuously improve operations. Collect raw data in virtual and nonvirtual environments from multiple feeds, normalize and structure the data, and aggregate it for alerts.

Artificial Intelligence 198

Artificial Intelligence DevOps Hardware Virtualization 198

Dynatrace

APRIL 11, 2023

The schema and index-dependent approach of traditional databases can’t keep pace or provide adequate analytics of these hyperscale environments. One key to augmenting DevSecOps collaboration is to take a platform approach that converges observability and security with big data analytics that can scale without compromising data fidelity.

Cloud 183

Cloud DevOps Open Source Retail 183

Dynatrace

AUGUST 10, 2021

She dispelled the myth that more big data equals better decisions, higher profits, or more customers. Investing in data is easy but using it is really hard”. The fact is, data on its own isn’t meaningful. Tricia quoted the statistic that companies typically use 3% of their data to inform decisions.

DevOps 246

DevOps Big Data Innovation Cloud 246

Dynatrace

JULY 5, 2022

AIOps combines big data and machine learning to automate key IT operations processes, including anomaly detection and identification, event correlation, and root-cause analysis. With greater visibility into systems’ states and a single source of analytical truth, teams can collaborate more efficiently.

Artificial Intelligence 282

Artificial Intelligence Cloud Innovation Strategy 282

DZone

MAY 30, 2019

With big data on the rise and data algorithms advancing, the ways in which technology has been applied to real-world challenges have grown more automated and autonomous. Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing.

Artificial Intelligence 100

Artificial Intelligence FinTech Social Media Storage 100

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. See the health of your big data resources at a glance. Azure HDInsight supports a broad range of use cases including data warehousing, machine learning, and IoT analytics.

Azure 146

Azure Cloud Big Data Virtualization 146

Dynatrace

MARCH 24, 2023

Using Marathon, its data center operating system (DC/OS) plugin, Mesos becomes a full container orchestration environment that, like Kubernetes and Docker Swarm, discovers services, balances loads, and manages application containers. Mesos also supports other orchestration engines, including Kubernetes and Docker Swarm.

Infrastructure 182

Infrastructure Open Source Operating System Cloud 182

The Netflix TechBlog

JUNE 7, 2021

Cloud Network Insight is a suite of solutions that provides both operational and analytical insight into the cloud network infrastructure to address the identified problems. Network Availability: The expected continued growth of our ecosystem makes it difficult to understand our network bottlenecks and potential limits we may be reaching.

Network 325

Network Transportation AWS Cloud 325

The Netflix TechBlog

MARCH 2, 2021

At Netflix, our data scientists span many areas of technical specialization, including experimentation, causal inference, machine learning, NLP, modeling, and optimization. Together with data analytics and data engineering, we comprise the larger, centralized Data Science and Engineering group.

Analytics 207

Analytics Innovation C++ Engineering 207