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 281

Artificial Inteligence Strategy Data Machine Learning 281

CIO

AUGUST 2, 2023

A data and analytics capability cannot emerge from an IT or business strategy alone. With both technology and business organization deeply involved in the what, why, and how of data, companies need to create cross-functional data teams to get the most out of it. What are some examples of data solutions in each of those buckets?

Data 219

Data Architecture Government Strategy 219

TechCrunch

MAY 18, 2022

Heartex, a startup that bills itself as an “open source” platform for data labeling, today announced that it landed $25 million in a Series A funding round led by Redpoint Ventures. We agreed that the only viable solution was to have internal teams with domain expertise be responsible for annotating and curating training data.

Open Source 203

Open Source Weak Development Team Artificial Inteligence Data 203

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.

Data Engineering 56

Data Engineering Big Data Engineering Data 56

CIO

FEBRUARY 13, 2023

This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%). CIOs anticipate an increased focus on cybersecurity (70%), data analysis (55%), data privacy (55%), AI/machine learning (55%), and customer experience (53%).

Budget 363

Budget Artificial Inteligence Technical Review VR 363

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

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

Cloudera

DECEMBER 10, 2020

Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If not, before adopting a cloud data warehouse, consider the true costs of a cloud-native data warehouse. These costs impede the adoption of cloud-native data warehouses.

Data 98

Data Technical Review Storage Systems Review 98

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

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

Cloudera

NOVEMBER 16, 2022

Overall, it struck me that while data science is not new, most firms are still defining the mission of the data office and data officer. It’s clear firms seek to leverage data and embrace its potential insights, but most are forging ahead in largely uncharted territory.

Data 82

Data Insurance Metrics eBook 82

O'Reilly Media - Data

JULY 18, 2018

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. Economic value of data.

Machine Learning 137

Machine Learning Artificial Inteligence Data Marketing 137

Cloudera

FEBRUARY 12, 2024

Get it wrong and your deployment may become wholly unusable with users unable to access and use the Cloudera data services. DNS servers are usually deployed in the hub virtual network or an on-prem data center instead of in the Cloudera VNET. In Cloudera deployments on public cloud, one of the key configuration elements is the DNS.

Azure 52

Azure Firewall Data Engineering Storage 52

TechCrunch

APRIL 12, 2022

While companies find AI’s predictive power alluring, particularly on the data analytics side of the organization, achieving meaningful results with AI often proves to be a challenge. It’s true that AI can help to project revenue, for example, by identifying trends in buying and selling. ” Taking Flyte.

Artificial Inteligence 202

Artificial Inteligence Machine Learning Open Source Biotech 202

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

Cloudera

JANUARY 6, 2021

Python is used extensively among Data Engineers and Data Scientists to solve all sorts of problems from ETL/ELT pipelines to building machine learning models. Apache HBase is an effective data storage system for many workflows but accessing this data specifically through Python can be a struggle.

Machine Learning 133

Machine Learning Artificial Inteligence Data Applications 133

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

O'Reilly Media - Ideas

JANUARY 8, 2019

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. Cloud for data infrastructure.

Trends 89

Trends Data Machine Learning Artificial Inteligence 89

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Overview of Cloudera Data Warehouse.

Performance 97

Performance Cloud Data Storage 97

Cloudera

DECEMBER 20, 2022

Efficient use of data will therefore be critical to improving the competitiveness and productivity of assets, both traditional and renewable generation. Data efficiency in renewables. Effective use of data can have a direct impact on the cash flow of wind and solar generation companies in areas such as real-time decision making.

Energy 84

Energy Analytics Data Machine Learning 84

Cloudera

SEPTEMBER 1, 2020

We wanted to do something about this search-engine deployment-related pain point and created a pre-configured service template to expedite the ditch-rich path of getting to a reliable Solr service, deployed for application developers to start using in just minutes. What is ‘Data Discovery and Exploration’ in CDP Data Hub?

Data 69

Data Backup Disaster Recovery Storage 69

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. Performance.

Big Data 63

Big Data Data Storage Microservices 63

Data Virtualization

OCTOBER 14, 2020

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. Prescriptive and descriptive models can help improve business and decision making processes.

Data 59

Data Virtualization Analytics Examples 59

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. User data collection is data about a user who is collected for market research purposes.

Big Data 52

Big Data Data Development Business Intelligence 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

Mentormate

OCTOBER 31, 2023

In our example, obvious stakeholders include healthcare providers, patients, and insurers. These are parties indirectly affected by the project, such as local communities, adjacent industries, regulatory bodies, or, in the example of healthcare, even medical researchers. Could more data enhance performance?

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

TIBCO - Connected Intelligence

APRIL 22, 2021

Opportunity 1: Stop copying so much data. In its annual Worldwide Global Datasphere Forecast, 2019-2023, IDC projected that only 15% of annual data growth is actually net new data. That means 85% of data growth results from copying data you already have. Some analytic tools query data efficiently.

Sustainability 46

Sustainability Virtualization Data Energy 46

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

Kentik

APRIL 12, 2023

With modern networks’ increasing complexity and scale, it has become essential to collect and analyze data from various sources to gain insights into network performance, security, and availability. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Cloudera

FEBRUARY 1, 2024

Delivering transformational innovation and accurate business decisions requires harnessing the full potential of your organization’s entire data ecosystem. Ultimately, this boils down to how reliable and trustworthy the underlying data that feeds your insights and applications is. How does Cloudera support Day 2 operations?

Backup 85

Backup Cloud Architecture Resources 85

Cloudera

FEBRUARY 16, 2022

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. classification problem. Figure 01: Is this a chihuahua or a muffin?

Machine Learning 86

Machine Learning Artificial Inteligence Weak Development Team Construction 86

TIBCO - Connected Intelligence

DECEMBER 11, 2020

Like the rest of the world, data is transforming the landscape of how Nordics citizens live and work, how commerce occurs, how its governments engage, and so much more. To better understand data-driven innovation in the Nordics, I reached out to Enfo Group’s Björn Arkenfall , Executive Vice President. “We But data can be troublesome.

Business Transformation 52

Business Transformation Data Analytics Hotels 52

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

Cloudera

SEPTEMBER 21, 2021

Many customers looking at modernizing their pipeline orchestration have turned to Apache Airflow, a flexible and scalable workflow manager for data engineers. A provider could be used to make HTTP requests, connect to a RDBMS, check file systems (such as S3 object storage), invoke cloud provider services, and much more.

Off-The-Shelf 100

Off-The-Shelf Data Engineering Virtualization Cloud 100

Altexsoft

OCTOBER 30, 2021

Explaining the difference, especially when they both work with something intangible such as data , is difficult. If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. Data science vs data engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

O'Reilly Media - Data

APRIL 3, 2018

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

Machine Learning 155

Machine Learning Artificial Inteligence Company Case Study 155

Altexsoft

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. Deloitte calculated that companies with data-driven CEOs are 77 percent more likely to succeed) and in today’s business world it’s absolutely self-evident that data is the key to success.

Weak Development Team 52

Weak Development Team Data Azure Analytics 52