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. Overly simplistic venn diagram with data scientists and data engineers.

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.

Why a data scientist is not a data engineer

O'Reilly Media - Ideas

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. Let’s call this data scientist Bob.

Data Engineering with Cloudera Altus

Cloudera Engineering

With modern businesses dealing with an ever-increasing volume of data, and an expanding set of data sources, the data engineering process that enables analysis, visualization, and reporting only becomes more important.

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. Continue reading The evolution of data science, data engineering, and AI

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.

Jupyter notebooks and the intersection of data science and data engineering

O'Reilly Media - 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

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. Image 1: Data Engineering Skillsets.

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.

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 [.].

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

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

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 technology provides an innovative way to deploy, provision, and optimize data center capacity anywhere in the world based on the needs of businesses, applications, and users.

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).

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). Next, let’s look at capacity: how big is our “big data”?

Announcing Support for Spot Instances in Cloudera Altus

Cloudera Engineering

A month ago, we publicly announced Cloudera Altus , our new platform–as–a–service offering, and today, we are expanding the Altus data engineering service to support AWS EC2 Spot instances. Cloud Altus aws data engineering ETL Preemptible Spot TCO

Altus SDK for Java

Cloudera Engineering

Altus empowers customers and partners alike, to run data engineering workloads in the cloud, leveraging cloud infrastructures such as AWS. Cloudera Altus also provides the ability to create data engineering pipelines using both a web console and CLI.

Cloudera Altus is Now Available on Azure


It was exactly one year ago at Strata London that we introduced the world to Cloudera Altus Data Engineering. That is what we introduced to AWS users last May – Altus Data Engineering (on AWS). But Altus is more than a multi-cloud Data Engineering cloud service.

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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.

Informatica Big Data Management on Cloudera Altus

Cloudera Engineering

Companies are increasingly moving their data operations into the cloud. With both companies focusing on helping customers derive business insights out of vast amounts of data, our new joint offering will dramatically simplify leveraging cloud-native infrastructures for big data analytics.

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).

Cloud-Scale Modeling with Cloudera Altus

Cloudera Engineering

Tools like Apache Spark bring scale to machine learning, and Cloudera Data Science Workbench brings Spark to data scientists. What happens when a data scientist wants to burst into the cloud to forge models at scale?

Cloud 64

Cloudera SDX: Under the Hood

Cloudera Engineering

Shared Data Experience — SDX — is Cloudera’s secret ingredient that makes it possible to deploy Cloudera’s four core functions (Data Engineering, Data Science, Analytic DB, Operational DB) on a single platform. What is SDX?

Cloudera Altus on Microsoft Azure

Cloudera Engineering

Cloudera Altus ( launched in May 2017 ) is a platform-as-a-service (PaaS) offering that enables users to analyze and process data at scale in public cloud infrastructures. The post Cloudera Altus on Microsoft Azure appeared first on Cloudera Engineering Blog.

Azure 60

Deploy Cloudera EDH Clusters Like a Boss Revamped – Part 2

Cloudera Engineering

The post Deploy Cloudera EDH Clusters Like a Boss Revamped – Part 2 appeared first on Cloudera Engineering Blog.

A new era of SQL-development, fueled by a modern data warehouse


However, as the data warehousing world shifts into a fast-paced, digital, and agile era, the demands to quickly generate reports and help guide data-driven decisions are constantly increasing. New data types need to be quickly joined with existing data sets.

Data 53

Nominations Now Open for the Sixth Annual Cloudera Data Impact Awards


Cloudera 2017 Data Impact Award Winners. We are excited to kick off the 2018 Data Impact Awards ! Since 2012, the Data Impact Awards have showcased how organizations are using Cloudera and the power of data to transform themselves and achieve dramatic results.

Data 54

The Data Science Iron Triangle – Modern BI and Machine Learning


Most organizations struggle to unlock data science in the enterprise. To that end, Cloudera offers the Data Science Workbench, a collaborative, scalable, and highly extensible platform for data exploration, analysis, modeling, and visualization. The New Iron Triangle.

Now Available: Cloudera Data Science Workbench Release 1.4


Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. Data scientists often develop models using a variety of Python/R open source packages.

Data 56

Building the Modern Platform with Cloudera Enterprise 6.x


Times are changing, and the traditional models of analytics and data management don’t serve the needs of the modern enterprise, so the way to address these topics is changing too. They want the freedom to explore their business data and understand what it means.

Turning petabytes of pharmaceutical data into actionable insights


That’s the equivalent of 1 petabyte ( ComputerWeekly ) – the amount of unstructured data available within our large pharmaceutical client’s business. Then imagine the insights that are locked in that massive amount of data. Authors: Mai N.

Altus SDX: Shared services for cloud-based analytics


The real power in machine learning and analytics is when multiple analytics disciplines are able to work together in concert, sharing data in service of solving more complex and more valuable questions. This clearly defines the data context even for transient compute applications.

The Top 10 Most Popular VISION Blogs of 2017


We (Mike Olson, Amr Awadallah, Christophe Bisciglia, and Jeff Hammerbacher) started Cloudera because we believe that data makes things that are impossible today, possible tomorrow. There’s more data coming, and there are plenty of impossible things to work on.

Breaking down data silos: when SAP alone is not enough


But when companies are looking towards new technologies such as data lakes, machine learning or predictive analytics, SAP alone is just not enough. It’s the de facto choice for all major corporations on the planet to manage their business data. Breaking down data silos.

Data 54

Events and Commands: Two Faces of the Same Coin?


Uncategorized application architecture command sourcing Data Engineering event driven event sourcingEvents are obviously the fundamental building block of event-sourced systems.

Introducing Cloudera Altus Analytic DB (beta) for Cloud-based Data Warehousing


As the first data warehouse cloud service that brings the warehouse to the data, it delivers instant self-service BI and SQL analytics to anyone – easily, reliably, and securely. Many business users are faced with limitations on what data they can access, how quickly they can do so, and what they can do with it. To help support additional users and workloads, different data silos have proliferated throughout the business.

Handling real-time data operations in the enterprise

O'Reilly Media - Data

Getting DataOps right is crucial to your late-stage big data projects. Data science is the sexy thing companies want. The data engineering and operations teams don't get much love. Let's call these operational teams that focus on big data: DataOps teams.

IoT Analytics: The New Frontier in Business Intelligence


The data engineering that precedes analytics was covered in our previous post, Data Engineering: The Heavy Lifting Behind IoT. Among the many sobriquets that the Internet of Things has acquired, none is more expressive than the term “Internet of Insights.”

IoT 68

Google Rolls Out Additional Cloud Certifications

Google is moving to address a chronic shortage of cloud skills necessary to advance DevOps.

Cloud 108

Simplifying machine learning lifecycle management

O'Reilly Media - Data

The O’Reilly Data Show Podcast: Harish Doddi on accelerating the path from prototype to production. In this episode of the Data Show , I spoke with Harish Doddi , co-founder and CEO of Datatron , a startup focused on helping companies deploy and manage machine learning models.