O'Reilly

FEBRUARY 18, 2020

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Software architecture, infrastructure, and operations are each changing rapidly. Trends in software architecture, infrastructure, and operations.

Software Architecture 142

Software Architecture DevOps Data Engineering Architecture 142

The Netflix TechBlog

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. Some of the optimizations are prerequisites for a high-performance data warehouse.

Storage 208

Storage Latency Efficiency Data Engineering 208

VoltDB

DECEMBER 11, 2024

Edge computing has transformed how businesses and industries process and manage data. By bringing computation closer to the data source, edge-based deployments reduce latency, enhance real-time capabilities, and optimize network bandwidth. Leverage tiered storage systems that dynamically offload data based on priority.

IoT 52

IoT Energy Logistics Latency 52

The Netflix TechBlog

MARCH 5, 2019

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure.

Infrastructure 184

Infrastructure Cloud Scalability AWS 184

The Netflix TechBlog

OCTOBER 18, 2022

Meson was based on a single leader architecture with high availability. In the data domain, it is common to have a super large number of jobs within a single workflow. For example, a workflow to backfill hourly data for the past five years can lead to 43800 jobs (24 * 365 * 5), each of which processes data for an hour.

Java 207

Java Scalability Traffic Architecture 207

InfoQ

NOVEMBER 29, 2023

HubSpot adopted routing messages over multiple Kafka topics (called swimlanes) for the same producer to avoid the build-up in the consumer group lag and prioritize the processing of real-time traffic.

Processing 84

Processing Traffic Data Engineering Scalability 84

Simform

FEBRUARY 14, 2023

Stream processing has become a core part of enterprise data architecture today due to the explosive growth of data from sources such as IoT sensors, security logs, and web applications. This blog discusses the topic of stream processing in detail to help you navigate its landscape with ease.

Processing 52

Processing IoT Architecture Data Engineering 52

High Scalability

APRIL 14, 2020

T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Learn to balance architecture trade-offs and design scalable enterprise-level software. The result is a process that doesn't get you the best employees you could. Apply here.

Education 105

Education Software Engineering Scalability Engineering 105

High Scalability

MAY 12, 2020

T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Learn to balance architecture trade-offs and design scalable enterprise-level software. The result is a process that doesn't get you the best employees you could. Apply here.

Education 102

Education Software Engineering Scalability Engineering 102

High Scalability

APRIL 28, 2020

T riplebyte lets exceptional software engineers skip screening steps at hundreds of top tech companies like Apple, Dropbox, Mixpanel, and Instacart. Learn to balance architecture trade-offs and design scalable enterprise-level software. The result is a process that doesn't get you the best employees you could. Apply here.

Education 102

Education Software Engineering Scalability Engineering 102

The Netflix TechBlog

MARCH 4, 2024

In this way, no human intervention is required in the remediation process. We used feature hashing to process the non-numeric values because they come from a high cardinality and dynamic set of values. Multi-objective optimizations. Auto Remediation generates recommendations by considering both performance (i.e., user name).

Tuning 213

Tuning Efficiency Big Data Engineering 213

O'Reilly

MARCH 2, 2020

When a project is going off track because some requirement wasn’t understood properly, you need to fix that as soon as possible—not after a year-long development process. When processes change, who wins, who loses, and why? Software architecture, infrastructure, and operations are each changing rapidly.

Artificial Intelligence 22

Artificial Intelligence Software Architecture Programming C++ 22

High Scalability

MAY 26, 2020

Created by former senior-level AWS engineers of 15 years. Learn to balance architecture trade-offs and design scalable enterprise-level software. Etleap is analyst-friendly , enterprise-grade ETL-as-a-service , built for Redshift and Snowflake data warehouses and S3/Glue data lakes. Learn the Good Parts of AWS.

Education 100

Education AWS Software Engineering Scalability 100

O'Reilly

FEBRUARY 2, 2020

A model isn’t source code, and it isn’t data; it’s an artifact built from the two. Given source code and the training data, you could re-produce a model, but it almost certainly wouldn’t be the same because of randomization in the training process. Models almost certainly react to incoming data; that’s their point.

Software Architecture 79

Software Architecture Monitoring Software Engineering Code 79

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

Abhishek Tiwari

JULY 12, 2018

In recent times, in order to gain valuable insights or to develop the data-driven products companies such as Netflix, Spotify, Uber, AirBnB have built internal data pipelines. If built correctly, data pipelines can offer strategic advantages to the business. Depending on frameworks, data processing units (a.k.a

Latency 63

Latency Analytics Scalability Engineering 63

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Custom schedulers.

Big Data 63

Big Data Storage Benchmarking Hardware 63

Abhishek Tiwari

JULY 20, 2019

Specialisation could be around products, business process, or technologies. One way to create a Spotify model inspired engineering organisation is to organise long-lived squads by retail business process hubs - i.e. specialisation around business process. Let's take an example of retail as a domain of interest.

Engineering 61

Engineering Retail Airlines Healthcare 61

The Symphonia

JUNE 19, 2018

I was fortunate to be both presenting a 2-day workshop (on AWS Serverless Architectures and Continuous Deployment) as well as hosting a full-day Serverless track of talks. One of the catalysts for starting Symphonia was the massive interest in my article on “ Serverless Architectures ” that is published on Martin Fowler’s site.

Serverless 40

Serverless AWS DevOps Open Source 40

InfoQ

AUGUST 7, 2024

Canva evaluated different data massaging solutions for its Product Analytics Platform, including the combination of AWS SNS and SQS, MKS, and Amazon KDS, and eventually chose the latter, primarily based on its much lower costs. The company compared many aspects of these solutions, like performance, maintenance effort, and cost.

AWS 87

AWS Analytics Performance Data Engineering 87

Abhishek Tiwari

DECEMBER 23, 2017

Data integration generally requires in-depth domain knowledge, a strong understanding of data schemas and underlying relationships. This can be time-consuming and bit challenging if you are dealing with hundreds of data sources and thousands of event types (see my recent article on ELT architecture ).

Architecture 40

Architecture Engineering Processing Code 40

The Netflix TechBlog

SEPTEMBER 10, 2019

Our data scientists often want to apply their knowledge of the business and statistics to fully understand the outcome of an experiment. 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.

Metrics 218

Metrics Architecture Infrastructure Innovation 218

All Things Distributed

NOVEMBER 15, 2016

Previously, I wrote about Amazon QuickSight , a new service targeted at business users that aims to simplify the process of deriving insights from a wide variety of data sources quickly, easily, and at a low cost. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 123

Analytics Availability Media Social Media 123

Adrian Cockcroft

FEBRUARY 13, 2023

This is the AWS Professional Services built tooling that customers can use to track the carbon footprint of their operations and processes, along with a customer example. STP213 Scaling global carbon footprint management — Blake Blackwell Persefoni Manager Data Engineering and Michael Floyd AWS Head of Sustainability Solutions.

AWS 64

AWS Energy Architecture Programming 64

The Netflix TechBlog

JANUARY 6, 2025

A few questions to ask yourself when considering the information architecture of your appinclude: Do you have different user groups trying to accomplish different things? Data is a tool that enhances decision-making, complementing the deep expertise and industry knowledge of our creative professionals.

Analytics 199

Analytics Engineering Cache Entertainment 199

O'Reilly

NOVEMBER 26, 2024

Entirely new paradigms rise quickly: cloud computing, data engineering, machine learning engineering, mobile development, and large language models. It’s less risky to hire adjunct professors with industry experience to fill teaching roles that have a vocational focus: mobile development, data engineering, and cloud computing.

Education 92

Education Azure AWS Java 92