Artificial Intelligence, Data Architecture and Data Warehouse

A Powerful Pair: Modern Data Warehouses and Machine Learning

Dataversity

MARCH 10, 2023

Artificial intelligence (AI) technologies like machine learning (ML) have changed how we handle and process data. Most companies utilize AI only for the tiniest fraction of their data because scaling AI is challenging. However, AI adoption isn’t simple.

Data Warehouse

Data Warehouse Artificial Intelligence Predictive Analytics Data Architecture

A Bridge Between Data Lakes and Data Warehouses

Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

Data Warehouse

Data Warehouse Data Quality Data Governance Artificial Intelligence

The 8 Best Hevo Data Alternatives to Build ELT/ETL Data Pipelines in 2025

Astera

DECEMBER 16, 2024

What is one thing all artificial intelligence (AI), business intelligence (BI), analytics, and data science initiatives have in common? They all need data pipelines for a seamless flow of high-quality data. Wide Source Integration: The platform supports connections to over 150 data sources.

Data Warehouse

Data Warehouse Real-time Data Monitoring Data Quality

Webinars

From Failure to Forecasting: Smarter M&A Strategies

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Transforming Analytics with a Modern Data Stack

Dataversity

NOVEMBER 29, 2021

There’s been a lot of talk about the modern data stack recently. Much of this focus is placed on the innovations around the movement, transformation, and governance of data as it relates to the shift from on-premise to cloud data warehouse-centric architectures.

Data Warehouse

Data Warehouse Government Innovation Artificial Intelligence

How Data Management and Big Data Analytics Speed Up Business Growth

BizAcuity

APRIL 14, 2022

There are a wide range of problems that are presented to organizations when working with big data. Challenges associated with Data Management and Optimizing Big Data. Unscalable data architecture. Scalable data architecture is not restricted to high storage space. Enterprise Big Data Strategy.

Big Data

Big Data Data Analytics Data Analytics Data Management

Guide to Digital Transformation: Data-first Architecture

Dataversity

APRIL 30, 2021

The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].

Digital Transformation

Digital Transformation Digital Data Warehouse Data Governance

Data Vault 2.0: What You Need to Know

Astera

NOVEMBER 30, 2023

With its foundation rooted in scalable hub-and-spoke architecture, Data Vault 1.0 provided a framework for traceable, auditable, and flexible data management in complex business environments. Building upon the strengths of its predecessor, Data Vault 2.0 Here are some key reasons why Data Vault 2.0 Data Vault 2.0

Data Warehouse

Data Warehouse Big Data Healthcare Data Governance

Should You Have Separate Document, Time-Series, NoSQL and SQL Databases or Can a Single Database Support All of These Data Types and Requirements?

Actian

JULY 31, 2019

At one time, data was largely transactional and Online Transactional Processing (OLTP) and Enterprise resource planning (ERP) systems handled it inline, and it was heavily structured. They are generating the entire range of structured and unstructured data, but with two-thirds of it in a time-series format.

IBM Support

IBM Support Documentation IBM Maintenance Data Warehouse

What is Data Modernization? Benefits, Importance & Components

Astera

AUGUST 13, 2024

Improve Data Access and Usability Modernizing data infrastructure involves transitioning to systems that enable real-time data access and analysis. The transition includes adopting in-memory databases, data streaming platforms, and cloud-based data warehouses, which facilitate data ingestion , processing, and retrieval.

Data Governance

Data Governance Data Architecture Data Quality Real-time Data

Data Science vs Data Analytics: Key Differences

Astera

MAY 31, 2024

On the other hand, Data Science is a broader field that includes data analytics and other techniques like machine learning, artificial intelligence (AI), and deep learning. Data integration combines data from many sources into a unified view. Data warehouses and data lakes play a key role here.

Data Analytics

Data Analytics Data Analytics Data Warehouse Healthcare

5 Takeaways from the Gartner Catalyst Conference

Domo

SEPTEMBER 11, 2019

2 – Customers find it easy and inexpensive to get data in and out of Domo Other data management solutions might make it easy to get your data in, but they make it difficult and/or expensive to get it out. Any customer who wants to get their data out of Domo can do so in a number of ways.

Data Warehouse

Data Warehouse Pharmaceutical Healthcare Real-time Data

10 Best Informatica Alternatives & Competitors in 2024

Astera

DECEMBER 28, 2023

Top Informatica Alternatives to Consider in 2024 Astera Astera is an end-to-end, automated data management and integration platform powered by artificial intelligence (AI). The tool enables users of all backgrounds to build their own data pipelines within minutes.

IBM cost

IBM cost IBM Support IBM Maintenance Data Warehouse

10 Best Informatica Alternatives & Competitors in 2024

Astera

DECEMBER 28, 2023

Top Informatica Alternatives to Consider in 2024 Astera Astera is an end-to-end, automated data management and integration platform powered by artificial intelligence (AI). The tool enables users of all backgrounds to build their own data pipelines within minutes.

IBM cost

IBM cost IBM Support IBM Maintenance Data Warehouse

Best Data Mining Tools in 2024

Astera

MAY 6, 2024

It utilizes artificial intelligence to analyze and understand textual data. Best For: Businesses that require a wide range of data mining algorithms and techniques and are working directly with data inside Oracle databases. Cons: There’s a high learning curve for using Apache Mahout.

Data Mining

Data Mining Supply Chain Data Quality Visualization

What Is Embedded Analytics?

Insight Software

MAY 1, 2023

These sit on top of data warehouses that are strictly governed by IT departments. The role of traditional BI platforms is to collect data from various business systems. Data Environment First off, the solutions you consider should be compatible with your current data architecture.

Embedded Analytics

Embedded Analytics IBM Support IBM Maintenance IBM cost

Business Analysis Digest

A Powerful Pair: Modern Data Warehouses and Machine Learning

A Bridge Between Data Lakes and Data Warehouses

Webinars

Trending Sources

The 8 Best Hevo Data Alternatives to Build ELT/ETL Data Pipelines in 2025

Webinars

Transforming Analytics with a Modern Data Stack

How Data Management and Big Data Analytics Speed Up Business Growth

Guide to Digital Transformation: Data-first Architecture

Data Vault 2.0: What You Need to Know

Should You Have Separate Document, Time-Series, NoSQL and SQL Databases or Can a Single Database Support All of These Data Types and Requirements?

What is Data Modernization? Benefits, Importance & Components

Data Science vs Data Analytics: Key Differences

5 Takeaways from the Gartner Catalyst Conference

10 Best Informatica Alternatives & Competitors in 2024

10 Best Informatica Alternatives & Competitors in 2024

Best Data Mining Tools in 2024

What Is Embedded Analytics?

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