Datavail

AUGUST 2, 2022

However, the list below covers the expenses that will make up the cloud analytics budget for most businesses: Storage (data warehousing, data lakes, data archiving, etc.). Data migration and integration. TCO for Analytics: Cloud vs. On-Premises. How should you integrate multiple on-premises data sources?

Analytics 52

Analytics Cloud Artificial Inteligence How To 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

Datavail

JULY 7, 2022

Unstructured data. Legacy analytics infrastructure was often designed for an era where the majority of enterprise data was structured (i.e. data that belongs in a row-column tabular database). Multiple studies estimate that unstructured data could make up as much as 80 to 90 percent of all information. High data volumes.

Analytics 52

Analytics Artificial Inteligence Cloud Artificial Intelligence 52

Cloudera

JUNE 7, 2021

The team’s focus turned to bringing Flink Data Definition Language ( DDL) and the batch interface into SSB with that completed. For customers, this opens up massive new opportunities within the Cloudera stack to incorporate existing data footprints with streaming sources. makes building these data products a snap.

Analytics 61

Analytics Systems Review Architecture Technical Review 61

Samanage

APRIL 11, 2019

The ETL Process vs The ELT Process. As a holdover from an era before Big Data, automation, machine learning, employee-focused service management, the Internet of Things, and AI, this process maintains an inherent focus on the retention of important data via a reliance on business logic, decisions, and aggregation.

Machine Learning 40

Machine Learning Artificial Inteligence Travel Big Data 40

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

Cloudera

AUGUST 21, 2020

Live data-streaming offers businesses exciting new opportunities to transform the way they operate, leveraging real-time insights to drive better decision making and enhance operational efficiency. To start off, what are the advantages of a forward-looking data-in-motion strategy?

Data 59

Data Analytics Banking Government 59

Perficient

MARCH 14, 2023

Blog Series Recap We have put together a blog series building off the webinar and going more in-depth about Perficient’s Healthy Lakehouse solution. Blog Series Recap We have put together a blog series building off the webinar and going more in-depth about Perficient’s Healthy Lakehouse solution.

Data 64

Data Healthcare AWS Analytics 64

Datavail

MAY 21, 2020

ETL (extract, transform, load) is the backbone of data processing for modern enterprises, helping you get the valuable insights you rely on day in and day out. OLTP vs. OLAP: What’s the Difference? OLTP databases emphasize accuracy and integrity, which means that redundant and duplicate data should be kept to a minimum.

Analytics 98

Analytics Analysis Business Intelligence Data 98

Altexsoft

JANUARY 26, 2022

As you may guess from the name, this role sits somewhere in the middle of a data analyst and data engineer, but it’s really neither one nor the other. Quoting a comment from the Reddit discussion , “Their [analytics engineers] job is to marry the technical requirements of the data stack with the business objectives”.

Analytics 59

Analytics Engineering Data Engineering Software Engineering 59

Altexsoft

FEBRUARY 21, 2022

There has been a lot of buzz around data science, machine learning (ML), and artificial intelligence (AI) lately. As you may already know, to train a machine learning model, you need data. Lots of data, to be more precise. Lots of quality data, to be even more precise. Source: Google AI blog.

Training 52

Training Artificial Inteligence Machine Learning Systems Review 52

Cloudera

AUGUST 21, 2020

And next to those legacy ERP, HCM, SCM and CRM systems, that mysterious elephant in the room – that “Big Data” platform running in the data center that is driving much of the company’s analytics and BI – looks like a great potential candidate. . How do we maintain visibility to all data and systems for security/compliance?

Big Data 71

Big Data Data Center Cloud Open Source 71

Actian

SEPTEMBER 3, 2019

In order to delve into each of the four key trends identified in the survey and highlighted in the Datacast Infographic , we are releasing a series of in-depth blog posts, which you can find and follow along with on Data at Work , the official Actian blog.

Enterprise 40

Enterprise Data Trends Survey 40

Actian

SEPTEMBER 3, 2019

In order to delve into each of the four key trends identified in the survey and highlighted in the Datacast Infographic , we are releasing a series of in-depth blog posts, which you can find and follow along with on Data at Work , the official Actian blog.

Enterprise 40

Enterprise Data Trends Survey 40