Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers. Overly simplistic venn diagram with data scientists and data engineers. Yes, both positions work on big data.

Data engineering: A quick and simple definition

O'Reilly Media - Data

Get a basic overview of data engineering and then go deeper with recommended resources. As the the data space has matured, data engineering has emerged as a separate and related role that works in concert with data scientists. Continue reading Data engineering: A quick and simple definition

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I'm looking for data engineers

Erik Bernhardsson

I’m interrupting the regular programming for a quick announcement: we’re looking for data engineers at Better. Migrate our data warehouse to Redshift. Write and productionize a web scraper to ingest a bunch of financial third party data. Fit Gamma distributions to conversion data to understand the time lag and conversion rates. This position is very engineering-heavy at its core, and the main qualification is solid programming skills.

I'm looking for data engineers

Erik Bernhardsson

I’m interrupting the regular programming for a quick announcement: we’re looking for data engineers at Better. Migrate our data warehouse to Redshift. Write and productionize a web scraper to ingest a bunch of financial third party data. Fit Gamma distributions to conversion data to understand the time lag and conversion rates. This position is very engineering-heavy at its core, and the main qualification is solid programming skills.

The Evolution of the Data Team: Lessons Learned From Growing a Team From 3 to 20

Speaker: Mindy Chen, Director of Decision Science, Hudl

In this webinar, we will unpack how data team structures have evolved by drawing on examples from our customers at Snowplow and discussing the pros and cons of the different structures that we have seen. We will be joined by Mindy Chen, Director of Decision Science at Hudl, who will take us on a journey through the challenges and opportunities during her experience of growing her data team from 3 to 20.

Big Data Engineer: Role, Responsibilities, and Job Description

Altexsoft

Big data can be quite a confusing concept to grasp. What to consider big data and what is not so big data? Big data is still data, of course. But it requires a different engineering approach and not just because of its amount. Regular data processing.

The evolution of data science, data engineering, and AI

O'Reilly Media - Data

The O’Reilly Data Show Podcast: A special episode to mark the 100th episode. This episode of the Data Show marks our 100th episode. We had a collection of friends who were key members of the data science and big data communities on hand and we decided to record short conversations with them. The logistics of studio interviews proved too complicated, but those Foo Camp conversations got us thinking about starting a podcast, and the Data Show was born.

Why a data scientist is not a data engineer

O'Reilly on Data

Or, why science and engineering are still different disciplines. "A He would have to ask an engineer to do it for him.". A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Otherwise, this leads to failure with big data projects.

What is Data Engineering: Explaining Data Pipeline, Data Warehouse, and Data Engineer Role

Altexsoft

Being at the top of data science capabilities, machine learning and artificial intelligence are buzzing technologies many organizations are eager to adopt. However, they often forget about the fundamental work – data literacy, collection, and infrastructure – that must be done prior to building intelligent data products. Data science layers towards AI, Source: Monica Rogati. Explaining Data Engineering and Data Warehouse.

Data Engineering is Critical to Big Data Success

Cloudera

I mentioned in an earlier blog titled, “Staffing your big data team, ” that data engineers are critical to a successful data journey. That said, most companies that are early in their journey lack a dedicated engineering group. And the longer it takes to put a team in place, the likelier it is that your big data project will stall. However, it’s imperative to find people who have an intense interest in the data that they are working with.

Using Cloudera Data Engineering to Analyze the Paycheck Protection Program Data

Cloudera

Data from the US Treasury website show which companies received PPP loans and how many jobs were retained. Analysis of this data presents three challenges. First, the size of the data is significant. Cloudera Data Engineering (CDE).

What is Data Engineer: Role Description, Responsibilities, Skills, and Background

Altexsoft

quintillion bytes of data generated daily, data scientists get busier than ever. And data science provides us with methods to make use of this data. But, understanding and interpreting data is just a final stage in a long way, as the information goes from its raw format to the fancy analytical boards. So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. Data engineer skills.

Data Scientists Get all the Glamour But, Wow, Is There a Need for Data Engineers

The New Stack

Ori Rafael is CEO of Upsolver, which delivers a self-service data lake ETL platform that bridges the gap between data lakes and data consumers and enables organizations to unlock the value of their cloud data lakes. The headlines about an acute shortage of data scientists have been featuring prominently in the last few years. Data Scientists and Data Engineers Are Parts of the Same Data Science Value Chain. What Does the Data Say?

Data Engineering: The Heavy Lifting Behind IoT

QBurst

The post Data Engineering: The Heavy Lifting Behind IoT appeared first on QBurst - Blog. This post is part of our continuing blog series on the Internet of Things. In our previous posts, we discussed sensors, wireless technologies in IoT, and Connected Operations: 3 IoT Scenarios. Smart cities, self-driving cars, intelligent machines—the IoT market is exploding with “Things.” The ease with which they cross over from sci-fi to real life […].

Why It’s Important For Your Organization to Know The Difference Between a Data Scientist and Data Engineer

CTOvision

In particular, there has been a significant increase in demand for data scientists. Companies are searching and competing for increasingly scarce data scientists as the […]. Artificial Intelligence Big Data and Analytics Cloud Computing CTO artificial intelligence big data data data engineer data scientist Enterprise

What data scientists and data engineers can do with current generation serverless technologies

O'Reilly on Data

The O’Reilly Data Show Podcast: Avner Braverman on what’s missing from serverless today and what users should expect in the near future. In this episode of the Data Show , I spoke with Avner Braverman , co-founder and CEO of Binaris , a startup that aims to bring serverless to web-scale and enterprise applications. Continue reading What data scientists and data engineers can do with current generation serverless technologies

A Data Engineer's Guide To Non-Traditional Data Storages

Toptal

With the rise of big data and data science, storage and retrieval have become a critical pipeline component for data use and analysis. Recently, new data storage technologies have emerged. Which one is best suited for data engineering? In this article, Toptal Data Scientist Ken Hu compares three prominent storage technologies within the context of data engineering

Jupyter notebooks and the intersection of data science and data engineering

O'Reilly on Data

David Schaaf explains how data science and data engineering can work together to deliver results to decision makers. Continue reading Jupyter notebooks and the intersection of data science and data engineering

Forward Thinking Tech Leaders at IO Seeking Big Data Engineer

CTOvision

Senior Software Engineer – Big Data. IO is the global leader in software-defined data centers. IO has pioneered the next-generation of data center infrastructure technology and Intelligent Control, which lowers the total cost of data center ownership for enterprises, governments, and service providers. We are looking for a talented Big Data Software Engineer to join the Applied Intelligence group in San Francisco. By Bob Gourley.

From Continuous Delivery To Continuous Data Delivery: Laying the Foundations

Dzone - DevOps

However, data engineering can become a major constraint within that process. data science continuous delivery data analytics data analysis data engineering data engineer continuous engineering continuous delivery foundationModern DevOps practices of continuous testing, integration, deployment/delivery, and monitoring form the backbone of a smooth deployment pipeline that continuously feeds back into itself for improvement.

Inside the Kentik Data Engine, Part 2

Kentik

In part 1 of this series we introduced Kentik Data Engine™, the backend to Kentik Detect™, which is a large-scale distributed datastore that is optimized for querying IP flow records (NetFlow v5/9, sFlow, IPFIX) and related network data (GeoIP, BGP, SNMP). In this query we’ve grouped the data by source ? Time-series data Summary tables are great, but often we want time-series data to build visualizations. Want to try KDE with your own network data?

.Net 40

Inside the Kentik Data Engine, Part 1

Kentik

Here at Kentik, we’ve applied many of the same concepts to Kentik Data Engine™ (KDE), a datastore optimized for querying IP flow records (NetFlow v5/9, sFlow, IPFIX) and related network data (GeoIP, BGP, SNMP). KDE is the backend of Kentik Detect™ and as such enables users to query network data and view visualizations via the Kentik portal, a fast, intuitive UI. Next, let’s look at capacity: how big is our “big data”?

Benefits of Data Virtualization to Data Scientists

Data Virtualization

The business value of applying data science in organizations is incontestable. Data science work can be divided into analytical and data preparation work. Examples of data preparation activities.

Article: How to Get Hired as a Machine Learning Engineer

InfoQ Culture Methods

To become a machine learning engineer, you have to interview. interviewing An introduction to Machine Learning Machine Learning Culture & Methods AI, ML & Data Engineering articleYou have to gain relevant skills from books, courses, conferences, and projects.

Article: What Does AI and Test Automation Have in Common?

InfoQ Culture Methods

Automated testing Artificial Intelligence Programming Automation Development Culture & Methods AI, ML & Data Engineering articleThese days AI is a big buzzword. While it rises in popularity, the controversy surrounding it flourishes as well. We will demystify AI, and see how it is already embedded in our everyday life, and then you are going to learn about how we (The folks at Testim.io) utilised this kind of groundbreaking technology to bring test automation to the next level.

INFOGRAPHIC: Data Scientist vs. Data Engineer by Cognilytica

CTOvision

As AI increasingly gains popularity among enterprises, companies are actively seeking data scientists who possess data science skills. Many enterprises confuse the roles of data scientists and data engineers. Artificial Intelligence Big Data and Analytics CTOEven though some traits, skills, programming languages and tools are shared by both roles, the overall roles and core skill sets are different and are not [.].

Article: Innovation Startups Modeling Agile Culture

InfoQ Culture Methods

To mix the power of the data and the importance of people to offer business intelligence is a key point nowadays. Machine Learning Data Analytics Agile Culture Startup Innovation Culture Culture & Methods AI, ML & Data Engineering articleInnovation is not only about the most advanced technology, management and processes are the new era of startups' innovation.

Article: Key Takeaway Points and Lessons Learned from QCon London 2020

InfoQ Culture Methods

QCon returned to London this past March for its fourteenth year in the city, attracting over 1,600 senior developers, architects, data engineers, team leads, and CTOs. QCon London 2020 DevOps Development Architecture & Design Culture & Methods AI, ML & Data Engineering articleThis article provides a summary of the key takeaways. By Abel Avram.

A Day in the Life of a Content Analytics Engineer

The Netflix TechBlog

I’m a Senior Analytics Engineer on the Content and Marketing Analytics Research team. Being an Analytics Engineer is like being a hybrid of a librarian ?? One of my favorite things about being an Analytics Engineer is the variety.

Analytics at Netflix: Who we are and what we do

The Netflix TechBlog

But there is far less agreement on what that term “data analytics” actually means?—?or Even within Netflix, we have many groups that do some form of data analysis, including business strategy and consumer insights. When you think about data at Netflix, what comes to mind?

Article: Q&A on the Book AI Crash Course

InfoQ Culture Methods

Use Cases Project Management Book Review Artificial Intelligence Machine Learning Programming InfoQ Training / Certification Agile Development Culture & Methods AI, ML & Data Engineering articleThe book AI Crash Course by Hadelin de Ponteves contains a toolkit of four different AI models: Thompson Sampling, Q-Learning, Deep Q-Learning and Deep Convolutional Q-learning.

How Our Paths Brought Us to Data and Netflix

The Netflix TechBlog

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.

Data 68

Staffing your big data team

Cloudera

A traditional BI and analytics organization consists of three main groups: Analysts that develop reports often using sample data. The data management team – modelers that take requests, find data, and develop models to answer the questions. In a big data world, we often see three new roles emerge and work more closely together: data engineers, data scientists and architects. You can think of them as the data workhorse.

Article: Results from the InfoQ Reader Survey 2019

InfoQ Culture Methods

Programming Languages System Programming Programming DevOps Culture & Methods Development Architecture & Design AI, ML & Data Engineering articleAt the end of 2019, InfoQ ran a survey of our readers to find out what tools, techniques, and languages they were using. This is a summary of the results. By Charles Humble.

Article: Data Analytics in the World of Agility

InfoQ Culture Methods

Is it all about customer-centric business, or is there any data left? Can we integrate data analytics and customer empathy? Customers & Requirements Big Data Data Analytics Project Management Database Data Analysis Infrastructure Surveys Agile Culture & Methods AI, ML & Data Engineering article