Netflix Tech

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, what drew you to data engineering?

Data Engineering 80

Data Engineering Engineering Data Big Data 80

Netflix Tech

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 83

Data Engineering Engineering Data Generative AI 83

TechCrunch

FEBRUARY 7, 2022

Throughout the COVID-19 recovery era, location data is set to be a core ingredient for driving business intelligence and building sustainable consumer loyalty. Scalable and data-rich location services are helping consumer-facing business drive transformation and growth along three strategic fronts: Creating richer consumer experiences.

Business Intelligence 230

Business Intelligence AWS Data Engineering Sustainability 230

Altexsoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Which Big Data tasks does Spark solve most effectively? How does it work?

Big Data 98

Big Data Tools Data Storage 98

Daffodil Software

AUGUST 29, 2022

Businesses can save plenty of time and millions of dollars when they use data science to better understand and improve their processes. With the age of the democratization of data, there have been several emerging trends defining enterprise data manipulation and data engineering.

Trends 98

Trends Data Data Engineering Engineering 98

TechCrunch

JUNE 17, 2021

There, they could see firsthand both the promise that data held for helping make decisions around a product, or for measuring how something is used, or to plan future features, but also the demands of harnessing it to work, and getting everyone on the same page to do so. How to ensure data quality in the era of Big Data.

Metrics 247

Metrics Tools Data Big Data 247

CIO

JUNE 7, 2022

It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems.

Analytics 203

Analytics Data Analysis Business Analytics 203

CIO

JANUARY 6, 2023

Database developers should have experience with NoSQL databases, Oracle Database, big data infrastructure, and big data engines such as Hadoop. These candidates will be skilled at troubleshooting databases, understanding best practices, and identifying front-end user requirements.

LAN 215

LAN Systems Administration How To Software Engineering 215

CIO

MARCH 21, 2022

E-commerce: Now that websites collect more than purchase data, data scientists help e-commerce businesses improve customer service, find trends, and develop services or products. Finance: Data on accounts, credit and debit transactions, and similar financial data are vital to a functioning business.

Analytics 205

Analytics Data Technical Review Analysis 205

Hu's Place - HitachiVantara

JANUARY 3, 2019

Against this backdrop there are five trends for 2019 that I would like to call out. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared.

Trends 86

Trends Artificial Inteligence Machine Learning Data Center 86

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. Key challenges.

Big Data 63

Big Data Data Storage Microservices 63

Altexsoft

MAY 14, 2021

Big Data enjoys the hype around it and for a reason. But the understanding of the essence of Big Data and ways to analyze it is still blurred. This post will draw a full picture of what Big Data analytics is and how it works. Big Data and its main characteristics. Key Big Data characteristics.

Big Data 78

Big Data Analytics Tools Applications 78

CIO

JUNE 13, 2024

The rising demand for data analysts The data analyst role is in high demand, as organizations are growing their analytics capabilities at a rapid clip. In July 2023, IDC forecast big data and analytics software revenue would hit $122.3 And they must be able to recognize trends and patterns. CAGR through 2027.

Data 148

Data Analytics Transportation Business Intelligence 148

Mobilunity

MARCH 19, 2025

As a dedicated team provider, Mobilunity confirms this trend as more companies contact us for staff augmentation. Developers gather and preprocess data to build and train algorithms with libraries like Keras, TensorFlow, and PyTorch. Data engineering. Expansion of Data Science and Big Data projects.

Trends 40

Trends Technical Review Development Software Review 40

Gorilla Logic

APRIL 27, 2022

Are you a data engineer or seeking to become one? This is the first entry of a series of articles about skills you’ll need in your everyday life as a data engineer. With SQL, you can also work with complex data types like arrays and JSON objects. This blog post is for you. CTE (Common Table Expression).

Data Engineering 59

Data Engineering Engineering Data Windows 59

ParkMyCloud

MAY 29, 2019

Trends in cloud jobs can be overall indicators into trends in the cloud computing space. Here are some trends we’re seeing. Cloud Talent Demand Trends. BI Analyst can also be described as BI Developers, BI Managers, and Big Data Engineer or Data Scientist. What trends are you seeing?

Trends 72

Trends Cloud IoT Artificial Inteligence 72

Agile Engine

DECEMBER 27, 2023

From emerging trends to hiring a data consultancy, this article has everything you need to navigate the data analytics landscape in 2024. What is a data analytics consultancy? Big data consulting services 5. 4 types of data analysis 6. Data analytics use cases by industry 7. Emerging trends 9.

Big Data 52

Big Data Analytics Data Analysis 52

Apiumhub

APRIL 8, 2021

With the Data Science industry continually evolving, there can be a lot to keep up with. New trends are coming up quite frequently, and if you want to do a good job and improve your skills, you must keep yourself up-to-date. He also manages the LinkedIn group Awesome Ways Big Data Is Used to Improve Our World. Kirk Borne.

Artificial Inteligence 110

Artificial Inteligence Technical Advisors Data Machine Learning 110

CIO

AUGUST 18, 2022

Traditionally, organizations have maintained two systems as part of their data strategies: a system of record on which to run their business and a system of insight such as a data warehouse from which to gather business intelligence (BI). You can intuitively query the data from the data lake.

Data 209

Data Technical Advisors Technical Review Artificial Inteligence 209

CTOvision

FEBRUARY 12, 2015

Simply view your data as a graphic and use your own talents to interpret what they could mean. Any data can be explored, from Excel spreadsheets to Hadoop big data. It’s built on breakthrough technology that translates pictures of data into optimized database queries. It’s up to you and your data. --.

Software 110

Software Data Analysis Big Data 110

Altexsoft

DECEMBER 9, 2020

Diagnostic analytics identifies patterns and dependencies in available data, explaining why something happened. Predictive analytics creates probable forecasts of what will happen in the future, using machine learning techniques to operate big data volumes. Often, no technologies are involved in data analysis.

Analytics 102

Analytics Technical Review Technology Applications 102

Abhishek Tiwari

DECEMBER 27, 2017

There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole. For instance, Alluxio, originally known as Tachyon, can potentially use Arrow as its in-memory data structure.

Trends 49

Trends Artificial Inteligence Data Big Data 49

Kentik

JULY 19, 2017

Because “package tracking” in a large network is a big data problem, and traditional network management tools weren’t built for that volume of data. Act 3: Big Data SaaS to the Rescue. Kentik offers an easy-to-use big data SaaS that’s purpose-built to deliver real-time network traffic intelligence.

Big Data 40

Big Data Network Data Systems Review 40

Erik Bernhardsson

OCTOBER 27, 2015

I was featured in Peadar Coyle’s interview series interviewing various “data scientists” – which is kind of arguable since (a) all the other ppl in that series are much cooler than me (b) I’m not really a data scientist. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering.

Data 101

Data Big Data Artificial Inteligence Machine Learning 101

DataRobot

OCTOBER 3, 2017

Enterprise data architects, data engineers, and business leaders from around the globe gathered in New York last week for the 3-day Strata Data Conference , which featured new technologies, innovations, and many collaborative ideas. DataRobot Data Prep. Try now for free.

Trends 52

Trends Azure Conference Media 52

Erik Bernhardsson

OCTOBER 27, 2015

I was featured in Peadar Coyle’s interview series interviewing various “data scientists” – which is kind of arguable since (a) all the other ppl in that series are much cooler than me (b) I’m not really a data scientist. So I think for anyone who wants to build cool ML algos, they should also learn backend and data engineering.

Data 100

Data Big Data Artificial Inteligence Machine Learning 100

Hu's Place - HitachiVantara

MAY 10, 2019

A Big Data Analytics pipeline– from ingestion of data to embedding analytics consists of three steps Data Engineering : The first step is flexible data on-boarding that accelerates time to value. This will require another product for data governance. This is colloquially called data wrangling.

Data 90

Data Artificial Inteligence Machine Learning Weak Development Team 90

Cloudera

APRIL 1, 2024

This recognition underscores Cloudera’s commitment to continuous customer innovation and validates our ability to foresee future data and AI trends, and our strategy in shaping the future of data management. Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics.

Cloud 105

Cloud Artificial Inteligence Generative AI Analytics 105

Apiumhub

JUNE 22, 2021

Data Innovation Summit topics. Same as last year, the event offers six workshops (crash-course) themes, each dedicated to a unique domain area: Data-driven Strategy, Analytics & Visualisation, Machine Learning, IoT Analytics & Data Management, Data Management and Data Engineering.

Innovation 84

Innovation Data Technical Review Artificial Inteligence 84

Cloudera

NOVEMBER 2, 2020

Correlations across data domains, even if they are not traditionally stored together (e.g. real-time customer event data alongside CRM data; network sensor data alongside marketing campaign management data). The extreme scale of “big data”, but with the feel and semantics of “small data”.

Data 97

Data Analytics Storage Big Data 97

Altexsoft

JULY 23, 2020

As a logical reaction to this problem, a new trend — MLOps — has emerged. It facilitates collaboration between a data science team and IT professionals, and thus combines skills, techniques, and tools used in data engineering, machine learning, and DevOps — a predecessor of MLOps in the world of software development.

Artificial Inteligence 145

Artificial Inteligence Machine Learning DevOps Tools 145

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

Cloudera

JULY 18, 2023

In the realm of big data analytics, Hive has been a trusted companion for summarizing, querying, and analyzing huge and disparate datasets. But let’s face it, navigating the world of any SQL engine is a daunting task, and Hive is no exception. I want to check the overall trend for Hive queries, but where can I check it?

Systems Review 73

Systems Review Metrics Trends Performance 73

Cloudera

DECEMBER 14, 2020

One trend that we’ve seen this year, is that enterprises are leveraging streaming data as a way to traverse through unplanned disruptions, as a way to make the best business decisions for their stakeholders. . Today, a new modern data platform is here to transform how businesses take advantage of real-time analytics.

Analytics 71

Analytics Big Data Government Cloud 71

Datavail

JUNE 8, 2023

Data Summit 2023 was filled with thought-provoking sessions and presentations that explored the ever-evolving world of data. From the technical possibilities and challenges of new and emerging technologies to using Big Data for business intelligence, analytics, and other business strategies, this event had something for everyone.

Database Administration 52

Database Administration Data Artificial Inteligence Analytics 52

Apiumhub

NOVEMBER 17, 2022

It is an excellent opportunity for tech leaders, developers, architects, and business innovation leaders to learn about the latest tech trends, get informed, and meet like-minded people in the industry. Jesse Anderson – Data Engineer, Creative Engineer, and Managing Director of Big Data Institute.

Technical Review 81

Technical Review Software Review Fractional CTO CTO Coach 81

Altexsoft

FEBRUARY 8, 2021

And planning, in turn, relies on understanding of current performance, past trends, existing risks, and possible future scenarios. There are two main approaches to demand planning: Traditional statistical methods make forecasts based on historical data and assume the continuation of existing trends. Stock management.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87