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 250

Azure Storage Big Data Analytics 250

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

Dynatrace

OCTOBER 4, 2022

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. Several pain points have made it difficult for organizations to manage their data efficiently and create actual value.

Analytics 246

Analytics Artificial Intelligence Storage Serverless 246

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. With the latest Data Mesh Platform, data movement in Netflix Studio reaches a new stage.

Big Data 258

Big Data Government Processing Analytics 258

Dynatrace

FEBRUARY 16, 2023

How do you get more value from petabytes of exponentially exploding, increasingly heterogeneous data? The short answer: The three pillars of observability—logs, metrics, and traces—converging on a data lakehouse. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022.

Analytics 198

Analytics Innovation Metrics Database 198

The Netflix TechBlog

JULY 15, 2021

Data Engineers of Netflix?—?Interview 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.

Data Engineering 200

Data Engineering Engineering Entertainment Big Data 200

Dynatrace

OCTOBER 4, 2022

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? How does a data lakehouse work?

Artificial Intelligence 221

Artificial Intelligence Storage Analytics Government 221

The Netflix TechBlog

JANUARY 13, 2022

By Vikram Srivastava and Marcelo Mayworm Netflix has one of the most complex data platforms in the cloud on which our data scientists and engineers run batch and streaming workloads. As our subscribers grow worldwide and Netflix enters the world of gaming , the number of batch workflows and real-time data pipelines increases rapidly.

Big Data 243

Big Data Infrastructure Metrics Games 243

The Netflix TechBlog

OCTOBER 27, 2020

By Tianlong Chen and Ioannis Papapanagiotou Netflix has more than 195 million subscribers that generate petabytes of data everyday. 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.

Latency 254

Latency Storage Big Data Tuning 254

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. By Werner Vogels on 18 August 2011 04:00 PM. Comments ().

Big Data 94

Big Data Analytics AWS Cloud 94

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 232

Analytics Education Innovation Engineering 232

The Netflix TechBlog

OCTOBER 28, 2021

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis 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. Pallavi Phadnis is a Senior Software Engineer on the Product Data Science and Engineering team.

Data Engineering 183

Data Engineering Engineering Big Data Software Engineering 183

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 306

Azure Big Data Storage Analytics 306

The Netflix TechBlog

JUNE 20, 2024

A summary of sessions at the first Data Engineering Open Forum at Netflix on April 18th, 2024 The Data Engineering Open Forum at Netflix on April 18th, 2024. At Netflix, we aspire to entertain the world, and our data engineering teams play a crucial role in this mission by enabling data-driven decision-making at scale.

Data Engineering 209

Data Engineering Engineering Entertainment Software Engineering 209

Dynatrace

AUGUST 10, 2021

This year’s conference agenda was packed full of choices, including: Keynotes : Topics included accelerating digital transformation, with Dynatrace CIO Mike Maciag, and Spatial Collapse: The Great Acceleration of Turning Data Into an Asset, with Tricia Wang from Sudden Compass. We’ve all heard it: data is one of your biggest assets.

DevOps 246

DevOps Innovation Big Data Cloud 246

Dynatrace

APRIL 20, 2023

Modern organizations ingest petabytes of data daily, but legacy approaches to log analysis and management cannot accommodate this volume of data. based financial services group, discussed how the bank uses log monitoring on the Dynatrace platform with an emphasis on observability and security data.

Analytics 200

Analytics Infrastructure Storage Architecture 200

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 200

Software Software Analytics Big Data 200

Uber Engineering

MARCH 21, 2024

Discover real-time query analytics and governance with DataCentral: Uber’s big data observability powerhouse, tackling millions of queries in petabyte-scale environments.

Big Data 68

Big Data Government Analytics 68

The Netflix TechBlog

APRIL 29, 2019

Device interaction for the collection of health and other operational data is yet another Python application. We are heavy users of Jupyter Notebooks and nteract to analyze operational data and prototype visualization tools that help us detect capacity regressions. We also use Python to detect sensitive data using Lanius.

Open Source 280

Open Source Network Infrastructure Big Data 280

DZone

MAY 25, 2024

For most people looking for a log management and analytics solution, Elasticsearch is the go-to choice. The same applies to InfluxDB for time series data analysis. As NetEase expands its business horizons, the logs and time series data it receives explode, and problems like surging storage costs and declining stability come.

Best Practices 130

Best Practices Big Data Games Storage 130

The Netflix TechBlog

JUNE 7, 2021

At much less than 1% of CPU and memory on the instance, this highly performant sidecar provides flow data at scale for network insight. 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 327

Network Transportation AWS Cloud 327

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

Dynatrace

APRIL 21, 2021

The Business Insights team at Dynatrace has been working with our largest Digital Experience Monitoring customers to help them turn the Core Web Vitals data they’re collecting with Dynatrace into actionable insights they can use to optimize pages ahead of this June 2021 change in Google’s search ranking algorithm. 28-day lookbacks.

Traffic 264

Traffic Metrics Mobile Analytics 264

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 193

Software Software Testing Big Data 193

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. To achieve these AIOps benefits, comprehensive AIOps tools incorporate four key stages of data processing: Collection. Aggregation.

Artificial Intelligence 298

Artificial Intelligence Cloud Innovation Strategy 298

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 229

DevOps Big Data Cloud Innovation 229

Highly Scalable

MARCH 10, 2015

Retail is one of the most important business domains for data science and data mining applications because of its prolific data and numerous optimization problems such as optimal prices, discounts, recommendations, and stock levels that can be solved using data analysis methods.

Retail 152

Retail C++ Analytics Metrics 152

The Netflix TechBlog

MARCH 2, 2021

Stephanie Lane , Wenjing Zheng , Mihir Tendulkar Source credit: Netflix Within the rapid expansion of data-related roles in the last decade, the title Data Scientist has emerged as an umbrella term for myriad skills and areas of business focus. Learning through data is in Netflix’s DNA. It can be hard to know from the outside.

Analytics 219

Analytics C++ Innovation Engineering 219

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

DZone

DECEMBER 21, 2020

Setting up a data warehouse is the first step towards fully utilizing big data analysis. Still, it is one of many that need to be taken before you can generate value from the data you gather. An important step in that chain of the process is data modeling and transformation.

Big Data 196

Big Data Google Scalability Processing 196

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. Implementation.

Big Data 57

Big Data Analytics Latency Azure 57

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

Dynatrace

JULY 6, 2022

Scripts and procedures usually focus on a particular task, such as deploying a new microservice to a Kubernetes cluster, implementing data retention policies on archived files in the cloud, or running a vulnerability scanner over code before it’s deployed. Big data automation tools. Batch process automation.

Artificial Intelligence 202

Artificial Intelligence Tuning Strategy Big Data 202

Dynatrace

APRIL 19, 2023

At Dynatrace Perform 2023 , Ben Rushlo, Business Insights leader at Dynatrace, and Navid Mehdiabadi, BCLC’s APM expert, discuss how the right business insights are crucial to making data-driven decisions and improving business outcomes. “It’s a journey in Dynatrace,” Rushlo said. “Our players just see the frontend.

Entertainment 165

Entertainment Analytics Healthcare Games 165

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

All Things Distributed

DECEMBER 13, 2016

The council has deployed IoT Weather Stations in Schools across the City and is using the sensor information collated in a Data Lake to gain insights on whether the weather or pollution plays a part in learning outcomes. The British Government is also helping to drive innovation and has embraced a cloud-first policy for technology adoption.

AWS 157

AWS Cloud Artificial Intelligence IoT 157

Dynatrace

DECEMBER 15, 2022

Complex cloud computing environments are increasingly replacing traditional data centers. In fact, Gartner estimates that 80% of enterprises will shut down their on-premises data centers by 2025. Collect raw data in virtual and nonvirtual environments from multiple feeds, normalize and structure the data, and aggregate it for alerts.

Artificial Intelligence 215

Artificial Intelligence DevOps Hardware Virtualization 215