CIO

FEBRUARY 14, 2023

By George Trujillo, Principal Data Strategist, DataStax Increased operational efficiencies at airports. To succeed with real-time AI, data ecosystems need to excel at handling fast-moving streams of events, operational data, and machine learning models to leverage insights and automate decision-making.

Artificial Inteligence 290

Artificial Inteligence Strategy Data Machine Learning 290

KitelyTech

FEBRUARY 3, 2021

Big data and data science are important parts of a business opportunity. How companies handle big data and data science is changing so they are beginning to rely on the services of specialized companies. User Data Collection. Therefore, the software must have an easily extendable architecture.

Big Data 52

Big Data Data Development Business Intelligence 52

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

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. What is a data architect?

Data 87

Data Data Engineering Big Data Architecture 87

TechCrunch

OCTOBER 7, 2021

Increasingly, conversations about big data, machine learning and artificial intelligence are going hand-in-hand with conversations about privacy and data protection. They could see that the longer-term issue would be a growing need and priority for data privacy. The germination for Gretel.ai military and over the years.

Artificial Inteligence 246

Artificial Inteligence Technical Review Engineering Data 246

Cloudera

NOVEMBER 27, 2017

In an earlier VISION post, The Five Markers on Your Big Data Journey , Amy O’Connor shared some common traits of many of the most successful data-driven companies. In this blog, I’d like to explore what I believe is the most important of those traits, building and fostering a culture of data. .

Big Data 41

Big Data Culture Data Telecommunications 41

O'Reilly Media - Ideas

FEBRUARY 11, 2019

In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Many companies are just beginning to address the interplay between their suite of AI, big data, and cloud technologies. Temporal data and time-series analytics.

Big Data 87

Big Data Technology Tools Cloud 87

Xebia

MAY 16, 2023

Data Engineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the Data Engineers themselves. Overly restrictive governance.

Testing 130

Testing How To Data Engineering Engineering 130

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. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

Data Engineering 280

Data Engineering Engineering Data Artificial Inteligence 280

Xebia

MARCH 10, 2021

Many organisations have a new ambition to become a data-driven organisation. In essence, this means the organisation wants to make better business decisions based on insights provided by data [4]. Data itself is not able to advise a business for better decision-making. Data & Analytics as a separate business domain.

DevOps 130

DevOps Data Analytics Policies 130

TechCrunch

FEBRUARY 7, 2022

Location data is absolutely critical to such strategies, enabling leading enterprises to not only mitigate challenges, but unlock previously unseen opportunities. Throughout the COVID-19 recovery era, location data is set to be a core ingredient for driving business intelligence and building sustainable consumer loyalty.

Business Intelligence 230

Business Intelligence AWS Sustainability Data Engineering 230

Cloudera

JUNE 7, 2022

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

Data 113

Data Architecture Analytics Government 113

Mobilunity

MAY 8, 2023

An Enterprise Data Lake is a large, centralized repository that stores structured and unstructured data at scale. It is designed to store vast amounts of data from a variety of sources. Moreover an Enterprise Data Lake makes it accessible to data scientists, analysts, and other users across the enterprise.

Engineering 52

Engineering Data Storage AWS 52

Cloudera

MARCH 10, 2022

We surveyed some of the most inspiring female leaders in data from across our global customers to find out how bias has affected their careers and how they believe we can break the cycle. . Gisele Ferreira, Data Superintendent at CNP Seguros Brasil recounts, “I started my career in Technology in 2000 in the Air Force. “At ’”.

Data 117

Data Weak Development Team Big Data Energy 117

Netflix Tech

MARCH 4, 2024

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. Therefore, the operational cost increases linearly with the number of failed jobs.

Machine Learning 84

Machine Learning Artificial Inteligence Data Systems Review 84

Hacker Earth Developers Blog

JUNE 26, 2019

Data science is one of the most sought after jobs of the 21st century. But how do you hire a data scientist who fits the bill? According to Firstround.com , in a competitive field like data science, strong candidates often receive 3 or more offers, so success rates of hiring are commonly below 50%. Data Science.

Data 200

Data How To Recruiting Machine Learning 200

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes. But with vastly different architectural worldviews.

Data 94

Data Analytics Open Source Architecture 94

Mobilunity

MAY 3, 2024

When it comes to financial technology, data engineers are the most important architects. As fintech continues to change the way standard financial services are done, the data engineer’s job becomes more and more important in shaping the future of the industry. Knowledge of Scala or R can also be advantageous.

Data Engineering 52

Data Engineering Fintech Engineering Data 52

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Data 110

Data Artificial Inteligence Analytics Machine Learning 110

Altexsoft

AUGUST 25, 2020

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. Data engineering vs big data engineering.

Big Data 75

Big Data Data Engineering Engineering Data 75

Apiumhub

APRIL 1, 2021

Data Science is one of the highest-paid and most popular fields nowadays. And there is nothing better than reading data science books to get the ball rolling. Top Data science books you should definitely read. Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce.

Artificial Inteligence 52

Artificial Inteligence Data Handbook Machine Learning 52

Cloudera

JANUARY 20, 2021

to make a classification model based off of training data stored in both Cloudera’s Operational Database (powered by Apache HBase) and Apache HDFS. One big use case is with sensor data. Corporations now use this type of data to notify consumers and employees in real-time. Training Data in HBase and HDFS.

Machine Learning 113

Machine Learning Artificial Inteligence Applications Data 113

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. What is a data lake?

Architecture 52

Architecture Data Storage Machine Learning 52

Mobilunity

DECEMBER 21, 2022

One of the essential goals of sourcing and using data is to help businesses make better decisions, so they can be at the top of their industries. To do this effectively, many businesses employ data scientists. These experts help to collect, visualize, and make sense of all kinds of data correctly. Agriculture.

Data 52

Data How To Machine Learning Artificial Inteligence 52

Hacker Earth Developers Blog

JUNE 26, 2019

Data science is one of the most sought after jobs of the 21st century. But how do you hire a data scientist who fits the bill? According to Firstround.com , in a competitive field like data science, strong candidates often receive 3 or more offers, so success rates of hiring are commonly below 50%. Data Science.

Data 100

Data How To Recruiting Machine Learning 100

Altexsoft

JUNE 29, 2021

For example, Netflix takes advantage of ML algorithms to personalize and recommend movies for clients, saving the tech giant billions. This article will focus on the role of a machine learning engineer, their skills and responsibilities, and how they contribute to an AI project’s success. Who does what in a data science team.

Artificial Inteligence 105

Artificial Inteligence Machine Learning Engineering Data Engineering 105

Openxcell

MAY 8, 2023

In today’s data-driven world, barely a day goes by without a confidential data leakage hitting the headlines. Businesses collect and store vast data or information, ranging from financial to personal details. Although this data is a valuable asset, it also carries the risk of “Data Leakage.” What is Data Leakage?

Data 52

Data Software Review Technical Review ChatGPT 52

O'Reilly Media - Ideas

APRIL 9, 2019

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. Let’s call this data scientist Bob.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

Altexsoft

MARCH 16, 2021

These devices are used to collect tons of various health and fitness-related data, such as daily activity, pulse, temperature, sleep patterns, and so on, all that in real time. But what happens to all the massive amounts of data from all these wearables and other medical and non-medical devices? Let’s see where it can come from.

Big Data 116

Big Data Healthcare Applications Data 116

Cloudera

JUNE 10, 2021

Apache Spark is a very popular analytics engine used for large-scale data processing. It is widely used for many big data applications and use cases. In this blog post, let us look at how you can read and write data to COD from Apache Spark. Cloudera Data Engineering. Prerequisites .

How To 93

How To Data Engineering Virtualization Resources 93

Cloudera

DECEMBER 17, 2021

As a Principal Solutions Engineer, he says his core responsibility is “to take Cloudera’s latest and greatest technology and meet a customer’s complex business requirements, across the data lifecycle, on any cloud or the datacenter.”. As he puts it, “Cloudera puts the customers first”.

Engineering 96

Engineering Telecommunications Culture Machine Learning 96

TIBCO - Connected Intelligence

SEPTEMBER 7, 2021

But with major growth, comes growing pains: more specifically, disparate data silos and disconnected, legacy systems. To revolutionize its relationship with data, Volkswagen Pon Financial Services turned to TIBCO solutions—and its success story is a great example of how you can too. Accelerated Growth Fuels Change.

Virtualization 32

Virtualization Data Insurance Software Review 32

TIBCO - Connected Intelligence

SEPTEMBER 14, 2020

Everyone yearns for consistent, well-governed, data that is easy to access and use. It enables organizations to more easily and successfully explore their data for insights, provide data as a service, report in real-time, and monitor digital operations. Data virtualization is rising to meet this challenge.

Virtualization 49

Virtualization Data Analytics Innovation 49

CIO

JULY 14, 2023

CIOs want to take advantage of this but on their terms—and their own data. In the shaper model, you’re leveraging existing foundational models, off the shelf, but retraining them with your own data.” A general LLM won’t be calibrated for that, but you can recalibrate it—a process known as fine-tuning—to your own data.

Generative AI 245

Generative AI Artificial Inteligence Open Source ChatGPT 245

CTOvision

JUNE 15, 2015

You know Spark, the free and open source complement to Apache Hadoop that gives enterprises better ability to field fast, unified applications that combine multiple workloads, including streaming over all your data. They also launched a plan to train over a million data scientists and data engineers on Spark.

Open Source 117

Open Source Big Data Machine Learning Artificial Inteligence 117

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. Who are my power users, and which are my famous pools?

Systems Review 76

Systems Review Metrics Trends Performance 76

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Data scientists love Python, period.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107