Abhishek Tiwari

FEBRUARY 26, 2020

The evolution of your technology architecture should depend on the size, culture, and skill set of your engineering organization. There are no hard-and-fast rules to figure out interdependency between technology architecture and engineering organization but below is what I think can really work well for product startup.

Architecture 82

Architecture MVC Engineering Organization 82

Dzone - DevOps

SEPTEMBER 6, 2024

Considering data engineering and data science, Astro and Apache Airflow rise to the top as important tools used in the management of these data workflows. This article compares Astro and Apache Airflow, explaining their architecture, features, scalability, usability, community support, and integration capabilities.

Comparison 91

Comparison Microservices Data Engineering Software Development 91

CIO

FEBRUARY 14, 2023

Cloud-native apps, microservices and mobile apps drive revenue with their real-time customer interactions. It’s clear how these real-time data sources generate data streams that need new data and ML models for accurate decisions. It’s also used to deploy machine learning models, data streaming platforms, and databases.

Artificial Inteligence 174

Artificial Inteligence Strategy Data Machine Learning 174

CIO

SEPTEMBER 2, 2022

Full-stack software engineers are essentially high-level software engineers who are focused on designing, testing, and implementing software applications. Job duties include helping plan software projects, designing software system architecture, and designing and deploying web services, applications, and APIs. Data engineer.

Software Engineering 148

Software Engineering Data Engineering DevOps AWS 148

CIO

AUGUST 31, 2022

Full-stack software engineers are essentially high-level software engineers who are focused on designing, testing, and implementing software applications. Job duties include helping plan software projects, designing software system architecture, and designing and deploying web services, applications, and APIs. Data engineer.

Software Engineering 148

Software Engineering Data Engineering DevOps AWS 148

Apiumhub

SEPTEMBER 20, 2023

From software architecture to artificial intelligence and machine learning, ​​these conferences offer unparalleled insights, networking opportunities, and a glimpse into the future of technology. In this article, we´ll be your guide to the must-attend tech conferences set to unfold in October. For more information, visit the event site here.

Conference 98

Conference Artificial Inteligence UI/UX Machine Learning 98

Netflix Tech

FEBRUARY 14, 2025

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. The enriched data is seamlessly accessible for both real-time applications via Kafka and historical analysis through storage in an Apache Iceberg table.

Systems Review 66

Systems Review Technical Review Data Metrics 66

Altexsoft

MAY 27, 2020

But, in any case, the pipeline would provide data engineers with means of managing data for training, orchestrating models, and managing them on production. Machine learning production pipeline architecture. Here we’ll look at the common architecture and the flow of such a system. Generating predictions.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

O'Reilly Media - Ideas

JUNE 4, 2019

Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. Azure Architecture: Best Practices , June 28. Microservices Architecture and Design , July 8-9. Software Architecture Foundations: Characteristics and Tradeoffs , July 18. AI and machine learning.

Course 92

Course Training Artificial Inteligence Software Review 92

O'Reilly Media - Ideas

FEBRUARY 11, 2020

Key survey results: The C-suite is engaged with data quality. Data scientists and analysts, data engineers, and the people who manage them comprise 40% of the audience; developers and their managers, about 22%. Data quality might get worse before it gets better. An additional 7% are data engineers.

Weak Development Team 145

Weak Development Team Data Technical Review Survey 145

Altexsoft

JULY 19, 2022

In the last few decades, we’ve seen a lot of architectural approaches to building data pipelines , changing one another and promising better and easier ways of deriving insights from information. There have been relational databases, data warehouses, data lakes, and even a combination of the latter two. What data mesh IS.

Architecture 52

Architecture Data Analytics Data Engineering 52

O'Reilly Media - Ideas

MARCH 6, 2019

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , March 13. Data Modelling with Qlik Sense , March 19-20. Foundational Data Science with R , March 26-27. What You Need to Know About Data Science , April 1. Real-Time Data Foundations: Flink , April 17.

Course 15

Course Artificial Inteligence Training Machine Learning 15

Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Impedance mismatch between data scientists, data engineers and production engineers. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Kentik

NOVEMBER 16, 2015

KDE handles over 10B flow records/day with a microservice architecture that's optimized using metrics. Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. So it was critical to instrument every component leading to, around, and within our data engine.

Metrics 40

Metrics Microservices Linux Architecture 40

Apiumhub

NOVEMBER 17, 2022

Speakers include: Simon Brown – Creator of the famous C4 model, Author of “Software Architecture for Developers” & Founder of Structurizr. Russ Miles – Chaos Engineer Thought Leader & Author of multiple books including “Antifragile Software: Building Adaptable Software with Microservices”.

Technical Review 81

Technical Review Software Review Fractional CTO CTO Coach 81

Mentormate

JANUARY 11, 2022

Only the largest engineering organizations have the scale to make this kind of continuous investment. Human-Centered Design, Composable Architectures, and Citizen Builders. Custom and off-the-shelf microservices cover the complexity of security, scalability, and data isolation and integrate into complex workflows through orchestration.

Technical Review 98

Technical Review Trends Off-The-Shelf Software Review 98

Cloudera

MAY 8, 2023

Out of the box Cloudera Data platform (CDP) performs superbly but over time, if data architecture, data engineering, and DevOps best practices are not maintained, you can get stuck maintaining the wild, wild west. In this six-part series, we’re focused on improving the health of your environment.

Data Engineering 77

Data Engineering Microservices DevOps Engineering 77

O'Reilly Media - Ideas

JUNE 5, 2019

Get hands-on training in Docker, microservices, cloud native, Python, machine learning, and many other topics. Azure Architecture: Best Practices , June 28. Microservices Architecture and Design , July 8-9. Software Architecture Foundations: Characteristics and Tradeoffs , July 18. AI and machine learning.

Course 67

Course Training Artificial Inteligence Software Review 67

Apiumhub

APRIL 18, 2019

We can’t wait to attend them all: Best practices in a modern (microservices) environment by Alvaro García. Micro Frontend: the microservice puzzle extended to frontend by Audrey Neveu. Micro Frontend: the microservice puzzle extended to frontend by Audrey Neveu. Responsible Microservices by Nate Schutta.

Technical Review 57

Technical Review Microservices Software Review CTO Coach 57

OpenCredo

JULY 13, 2018

The Power of the Architecture-driven Organisation. Project Datatron: Architecture-driven Collaboration. This isn’t the only organisational problem an engineering consultant may encounter. At first it looked like a fairly straightforward data engineering problem. – Melvin Conway.

Architecture 40

Architecture Fractional CTO CTO Coach Engineering 40

O'Reilly Media - Ideas

MAY 1, 2019

Data science and data tools. Practical Linux Command Line for Data Engineers and Analysts , May 20. First Steps in Data Analysis , May 20. Data Analysis Paradigms in the Tidyverse , May 30. Data Visualization with Matplotlib and Seaborn , June 4. Microservices Caching Strategies , June 17.

Course 60

Course Training Artificial Inteligence Machine Learning 60

Abhishek Tiwari

JULY 12, 2018

In this post, we will discuss why you should avoid building data pipelines in first place. Depending on the use cases, it is quite possible that you can achieve similar outcomes by using techniques such as data virtualisation or simply building microservices. It can be used to power new analytics, insight, and product features.

Data 63

Data Software Review Technical Review Microservices 63

Cloudera

APRIL 26, 2023

Can you achieve similar outcomes with your on-premises data platform? Application modernization initiatives have led to cloud native architectures gaining popularity on premises, making it a sensible choice to extend to your data platform. This is exactly where cloud native architectures excel, and why they are so popular.

Cloud 64

Cloud Data Load Balancer Storage 64

O'Reilly Media - Ideas

MARCH 18, 2020

One-sixth of respondents identify as data scientists, but executives—i.e., The survey does have a data-laden tilt, however: almost 30% of respondents identify as data scientists, data engineers, AIOps engineers, or as people who manage them. All told, more than 70% of respondents work in technology roles.

Enterprise 139

Enterprise Survey Technical Review Weak Development Team 139

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.

Trends 49

Trends Artificial Inteligence Data Big Data 49

Kentik

FEBRUARY 7, 2023

But what are network operators to do when their cloud networks have to be distributed, both architecturally and geographically? Suddenly, this dense data mass is a considerable burden, and the same forces that happily drew in service and customer data find that that data is now trapped and extremely expensive and complicated to move.

Network 59

Network Cloud Data Data Center 59

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.

Big Data 63

Big Data Data Storage Microservices 63

Progress

DECEMBER 30, 2019

AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers. You’re developing a greenfield application that doesn’t require any external data or auth systems. You have existing backend services developed on AWS.

AWS 52

AWS DevOps Disaster Recovery Serverless 52

Progress

DECEMBER 30, 2019

AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers. You’re developing a greenfield application that doesn’t require any external data or auth systems. You have existing backend services developed on AWS.

AWS 52

AWS DevOps Disaster Recovery Serverless 52

Progress

DECEMBER 30, 2019

AWS Amplify is a good choice as a development platform when: Your team is proficient with building applications on AWS with DevOps, Cloud Services and Data Engineers. You’re developing a greenfield application that doesn’t require any external data or auth systems. You have existing backend services developed on AWS.

AWS 10

AWS DevOps Disaster Recovery Serverless 10

Hu's Place - HitachiVantara

JANUARY 3, 2019

In order to utilize the wealth of data that they already have, companies will be looking for solutions that will give comprehensive access to data from many sources. More focus will be on the operational aspects of data rather than the fundamentals of capturing, storing and protecting data.

Trends 86

Trends Artificial Inteligence Machine Learning Data Center 86

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. process data in real time and run streaming analytics. But to understand why Kafka is omnipresent we have to look at how it works — in other words, to get familiar with its concepts and architecture.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

Abhishek Tiwari

JULY 20, 2019

The engineering organisation described may not work for you because of a team of 8-10 people is still a very big overhead. In this model, software architecture and code ownership is a reflection of the organisational model. Thirdly, let engineers themselves choose the delivery teams and organise them around the initiative.

Engineering 61

Engineering Weak Development Team Software Review Technical Review 61

Apiumhub

AUGUST 12, 2021

This year you will have 6 unique tracks: Cloud Computing: IaaS, PaaS, SaaS DevOps: Microservices, Automation, ASRs Cybersecurity: Threats, Defenses, Tests Data Science: ML, AI, Big Data, Business Analytics Programming languages: C++, Python, Java, Javascript,Net Future & Inspire: Mobility, 5G data networks, Diversity, Blockchain, VR.

Industry 59

Industry Technical Advisors CTO Coach Azure 59

Capgemini

MARCH 31, 2024

Unlocking the potential of generative software engineering: Lessons from the past, projections for the future The transformative journey of software engineering, from procedural development to object-oriented programming, to cloud and microservices, revolutionized how we build and maintain software.

Software Engineering 52

Software Engineering Engineering Software Generative AI 52

Kentik

APRIL 11, 2016

Clustered computing for real-time Big Data analytics. The concept of parallel processing based on a “clustered” multi-computer architecture has a long history dating back at least as far as Gene Amdahl’s work at IBM in the 1960s. For more on how we make it work, see Inside the Kentik Data Engine.).

Big Data 40

Big Data Analytics Network Architecture 40