INDUSTRY INSIGHTS YOUR PEERS ARE READING
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 MORE
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. MORE
Google is moving to address a chronic shortage of cloud skills necessary to advance DevOps. MORE
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? MORE
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. MORE
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. MORE
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. MORE
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. MORE
The O’Reilly Data Show Podcast: Avner Braverman on what’s missing from serverless today and what users should expect in the near future. Continue reading What data scientists and data engineers can do with current generation serverless technologies MORE
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. MORE
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). MORE
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. MORE
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. MORE
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 MORE
The post Deploy Cloudera EDH Clusters Like a Boss Revamped – Part 2 appeared first on Cloudera Engineering Blog. MORE
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. MORE
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 MORE
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. MORE
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 MORE
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. MORE
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 [.]. MORE
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. MORE
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). MORE
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”? MORE
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? MORE
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. MORE
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. MORE
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.” MORE
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 MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
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. MORE
Data Engineering Related Topics O'Reilly Media - Data
APRIL 11, 2018
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.
O'Reilly Media - Data
JULY 16, 2018
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.
O'Reilly Media - Ideas
APRIL 9, 2019
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.
Cloudera Engineering
MAY 24, 2017
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.
O'Reilly Media - Data
MAY 24, 2018
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
Erik Bernhardsson
JANUARY 27, 2018
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.
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O'Reilly Media - Data
AUGUST 27, 2018
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
Cloudera
JANUARY 12, 2018
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.
QBurst
FEBRUARY 12, 2019
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.
CTOvision
JUNE 7, 2018
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 [.].
CTOvision
APRIL 27, 2018
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
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Toptal
DECEMBER 6, 2016
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
CTOvision
MAY 1, 2014
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.
Kentik
MAY 2, 2016
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).
Kentik
APRIL 25, 2016
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”?
Cloudera Engineering
JUNE 23, 2017
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
Cloudera Engineering
FEBRUARY 22, 2018
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
MAY 22, 2018
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.
Cloudera
JANUARY 3, 2018
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.
Cloudera Engineering
OCTOBER 6, 2017
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.
Cloudera
FEBRUARY 11, 2019
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).
Cloudera Engineering
NOVEMBER 15, 2017
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?
Cloudera Engineering
SEPTEMBER 26, 2017
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 Engineering
SEPTEMBER 27, 2017
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.
Cloudera Engineering
JANUARY 23, 2018
The post Deploy Cloudera EDH Clusters Like a Boss Revamped – Part 2 appeared first on Cloudera Engineering Blog.
Cloudera
SEPTEMBER 17, 2018
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.
Cloudera
MAY 10, 2018
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.
Cloudera
JULY 9, 2018
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.
Cloudera
MAY 22, 2018
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.
Cloudera
MAY 22, 2018
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.
Cloudera
JUNE 4, 2018
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.
Cloudera
MARCH 6, 2018
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.
Cloudera
JANUARY 19, 2018
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.
Cloudera
FEBRUARY 19, 2018
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.
OpenCredo
MARCH 14, 2018
Uncategorized application architecture command sourcing Data Engineering event driven event sourcingEvents are obviously the fundamental building block of event-sourced systems.
Cloudera
NOVEMBER 28, 2017
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.
O'Reilly Media - Data
SEPTEMBER 24, 2018
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.
QBurst
MARCH 13, 2019
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.”
DevOps.com
JANUARY 24, 2019
Google is moving to address a chronic shortage of cloud skills necessary to advance DevOps.
O'Reilly Media - Data
AUGUST 16, 2018
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.
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