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. “We [founded Union] because we believe that machine learning and data workflows are fundamentally different from software deployments.

Artificial Inteligence 202

Artificial Inteligence Machine Learning Open Source Biotech 202

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. Does the data live in one or many clouds?

Data 198

Data Architecture Government Strategy 198

Cloudera

OCTOBER 13, 2021

Enterprises need to find a way of getting insights from this vast treasure trove of data into the hands of the people that need it. For relatively low amounts of data, public cloud is a possible path for some organizations. These companies have instead opted to leverage their existing data centre investment.

Data Center 96

Data Center Cloud Data How To 96

Cloudera

APRIL 26, 2023

Data is core to decision making today and organizations often turn to the cloud to build modern data apps for faster access to valuable insights. Can you achieve similar outcomes with your on-premises data platform? Today’s end users demand speed, agility, and cutting-edge features. You absolutely can.

Cloud 64

Cloud Data Load Balancer Storage 64

Tenable

NOVEMBER 3, 2022

When our data engineering team was enlisted to work on Tenable One, we knew we needed a strong partner. 4, represents a paradigm shift in how organizations can improve their preventive cybersecurity strategies to reduce risk. In that time, our data engineering team also scaled from five to 11 engineers.

Data 98

Data AWS Storage Data Engineering 98

Kentik

APRIL 12, 2023

Network telemetry is the process of collecting and analyzing data from network devices, including switches, routers, firewalls, and servers, to gain visibility into network traffic and performance. At the heart of DataOps is the agile development methodology, which emphasizes collaboration, iteration, and continuous delivery.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Hu's Place - HitachiVantara

APRIL 11, 2019

According to the Harvard Business Review , " Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all. REAN Cloud also provides ongoing managed services.

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

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.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. CRM platforms). public, private, hybrid cloud)?

Scalability 92

Scalability Technical Review Data Analytics 92

Altexsoft

FEBRUARY 10, 2021

DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible. It covers the entire data analytics lifecycle, from data extraction to visualization and reporting, using Agile practices to speed up business results.

Analytics 75

Analytics DevOps Development Software Review 75

Altexsoft

JULY 19, 2022

That’s exactly what every data-driven organization has been trying to find for years,” someone would come up with a new, better solution. Data mesh is another hot trend in the data industry claiming to be able to solve many issues of its predecessors. How a data mesh may look like.

Architecture 52

Architecture Data Analytics Data Engineering 52

Cloudera

DECEMBER 10, 2020

Appealing directly to end-users in the Lines of Business (LOBs), these solutions can dramatically shorten time to value, lower administrative burdens, and promise continuous agility in response to changing business demands. 150 for storage use = $15 / TB / month x 10 TB. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

Cloudera

JANUARY 11, 2021

Requests to Central IT for data warehousing services can take weeks or months to deliver. Central IT teams at large organizations face a proliferation of IT projects arising from the complexities of markets and from the needs of internal lines of business (LoBs). Cloudera customers have described the data challenges they face.

Data 62

Data Pharmaceuticals Open Source Machine Learning 62

Cloudera

DECEMBER 20, 2022

In addition, using data well can allow better decisions to be made, such as the possibility of bypassing the day-ahead market and going directly to the intraday market and having a better return per watt generated. . Organizations working in traditional energy generation will have to adjust costs by improving the efficiency of these plants.

Energy 81

Energy Analytics Data Machine Learning 81

Cloudera

SEPTEMBER 9, 2018

Businesses need to be agile and quick in using data to drive insights, lest they lose the opportunity they are trying to capitalize on. It’s no surprise that there is a strong correlation between successful businesses and those that exceed in extracting value from data. Using Cloudera Altus for your cloud data warehouse.

Data 40

Data Data Engineering Analytics Cloud 40

Cloudera

OCTOBER 26, 2017

Become more agile with business intelligence and data analytics. Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. Published originally on O’Reilly.com.

Cloud 43

Cloud Data Storage Analytics 43

Cloudera

OCTOBER 25, 2022

The data lakehouse is gaining in popularity because it enables a single platform for all your enterprise data with the flexibility to run any analytic and machine learning (ML) use case. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

Altexsoft

SEPTEMBER 10, 2021

Consisting of the same steps as in ETL, ELT changes the sequence — it first extracts raw data from sources and loads it into a target source, where transformation happens as and when required. The target system for ELT is usually a data lake or cloud data warehouse. Key types of data integration.

Tools 52

Tools Data Software Review Technical Review 52

RapidValue

JULY 21, 2019

In a world, which demands consumable data immediately but plagued by constant change and data drift, the traditional approach of designing, deploying and operating data pipelines as siloed activities is expensive and risky. You have to make sure that they are powerful and easy to use for both data operators and consumers.

Business Transformation 65

Business Transformation Enterprise DevOps Analytics 65

Altexsoft

DECEMBER 9, 2020

Some famous ones are: Gartner ’s Maturity Model for Data and Analytics, DELTA Plus by Tom Davenport, DAMM – Data Analytics Maturity Model for Associations, SAS Analytic Maturity Scorecard, and many more. At this stage, data is siloed, not accessible for most employees, and decisions are mostly not data-driven.

Analytics 102

Analytics Technical Review Technology Applications 102

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Building an AI or machine learning model is not a one-time effort.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Altexsoft

NOVEMBER 23, 2020

Otherwise, let’s start from the most basic question: What is data migration? What is data migration? In general terms, data migration is the transfer of the existing historical data to new storage, system, or file format. What makes companies migrate their data assets. Main types of data migration.

Data 104

Data Transportation Backup Storage 104

TIBCO - Connected Intelligence

APRIL 22, 2021

Adopting more sustainable business practices is a near-universal objective at organizations today. 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.

Sustainability 46

Sustainability Virtualization Data Energy 46

TIBCO - Connected Intelligence

DECEMBER 14, 2020

On-premises, cloud, hybrid, and multi-cloud are just some of the locations where your data is now stored. Each provides value in an increasing data driven business climate. Today’s best compute and storage options will be surpassed tomorrow. On the consumption side, your demand for data has never been higher.

Virtualization 26

Virtualization Data Architecture Agile 26

O'Reilly Media - Ideas

JANUARY 25, 2024

Before that, cloud computing itself took off in roughly 2010 (AWS was founded in 2006); and Agile goes back to 2000 (the Agile Manifesto dates back to 2001, Extreme Programming to 1999). Data analysis and databases Data engineering was by far the most heavily used topic in this category; it showed a 3.6%

Trends 119

Trends Technical Review Technology Artificial Inteligence 119

Cloudera

SEPTEMBER 10, 2018

The other day, it struck me how similar this is to how organizations digitally transform and become data-driven. Your raw material is data instead of wood, and you use multidisciplinary analytics rather than woodworking tools like a hammer, saw, and sander.

Analytics 40

Analytics Machine Learning Artificial Inteligence Data Engineering 40

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. When you execute a query against the data lake, data transformation are performed at run-time or in-memory. Classic ETL.

Storage 40

Storage Big Data Google Cloud Analysis 40

Altexsoft

MARCH 2, 2023

As the amount of enterprise data continues to surge, businesses are increasingly recognizing the importance of data governance — the framework for managing an organization’s data assets for accuracy, consistency, security, and effective use. Projections show that the data governance market will expand from $1.81

Government 52

Government Tools Data Weak Development Team 52

Altexsoft

APRIL 13, 2023

Due to extensive usage of connected IoT devices and advanced processing technologies, SCCTs not only gather data and build operational reports but also create predictions, define the impact of various macro- and microeconomic factors on the supply chain, and run “what-if” scenarios to find the best course of action. into the digital twin.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

AUGUST 4, 2021

A data mart is a smaller subsection of a data warehouse built specifically for a particular subject area, business function, or group of users. The main idea is to provide a particular part of an organization with data that is the most relevant for their analytical needs. Moreover, not all organizations use data lakes.

Data 64

Data Analytics Construction Cloud 64

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

O'Reilly Media - Ideas

MARCH 31, 2020

There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.” Particularly at a company that’s new to AI, part of an AI product manager’s job is helping the organization build the culture it needs to succeed with AI.

Product Management 139

Product Management Artificial Inteligence Machine Learning Software Review 139

Cloudera

DECEMBER 12, 2018

For example, Impala uses the Hive metastore catalog as its data dictionary and it operates directly on data existing in HDFS, which is found through the Namenode API. So, they have the same components: a catalog, query compilation, query execution, and file management and storage, yet they’re independent of each other.

Data 42

Data Marketing Storage Big Data 42