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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Cloudera Data Engineering – Integration steps to leverage spark on Kubernetes


What is Cloudera Data Engineering (CDE) ? Cloudera Data Engineering is a serverless service for Cloudera Data Platform (CDP) that allows you to submit jobs to auto-scaling virtual clusters. The Cloudera Data Engineering service API is documented in Swagger.

What Role Do Data Engineers Play in Data Security?


While we know that data engineers are very different than data architects — as the latter conceptualize data frameworks and the former build and maintain them — the data engineer function has evolved quite a bit in recent years. Data Basics Featured

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.

Thank Your Data Engineers With A Streaming Data Warehouse


Read Andrew Wooler explain how Kinetica can provide a cost-effective streaming data warehouse on Forbes : I recently watched the movie Ford v Ferrari, based on the true story of […].

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 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.

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


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.

Big Data Engineer: Role, Responsibilities, and Job Description


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.

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.

Data Engineering is Critical to Big Data Success


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.

Introducing CDP Data Engineering: Purpose Built Tooling For Accelerating Data Pipelines


For enterprise organizations, managing and operationalizing increasingly complex data across the business has presented a significant challenge for staying competitive in analytic and data science driven markets. CDP data lifecycle integration and SDX security and governance.

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


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. So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms.

Using Cloudera Data Engineering to Analyze the Paycheck Protection Program Data


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).

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


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

Data Engineering: The Heavy Lifting Behind IoT


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 […].

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

Forward Thinking Tech Leaders at IO Seeking Big Data Engineer


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.

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


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

Inside the Kentik Data Engine, Part 2


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?

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Inside the Kentik Data Engine, Part 1


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”?

Article: Agile Development Applied to Machine Learning Projects

InfoQ Articles

Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. Machine learning is a powerful new tool, but how does it fit in your agile development?

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.

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.

INFOGRAPHIC: Data Scientist vs. Data Engineer by Cognilytica


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 article

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.

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 article

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: 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 article

Course 100

Article: InfoQ 2020 Recap, Editor Recommendations, and Best Content of the Year

InfoQ Articles

Microservices Chaos Engineering Edge Computing Distributed Systems Azure Continuous Delivery AWS Google Cloud Data Mesh Kubernetes Modular Monolith Jakarta EE Culture & Methods Development Architecture & Design AI, ML & Data Engineering DevOps article

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: 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.

Staffing your big data team


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.

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.

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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

Hadoop Security Basics (In Under 5 Minutes)


Between growing data and analytics teams, more collaborative projects, and GDPR , security and governance are becoming bigger issues. As organizations' analytical maturity improves, it's increasingly critical that business and technical users alike understand data best practices. Hadoop Technology Data Engineering data architecture

Governing for digital transformation and growth


The former sees growing investment in data analytics to become data-driven (45% of organizations expect to increase their spending in this area) while the latter is fueled by disruptive technology and the adoption of AI (41% of organizations name it as their game changer). Both rely virtually entirely on the enterprise leveraging of data. They need a range of analytical capabilities from data engineering to data warehousing to operational databases and data science.