Data Silos, ETL and SQL - Data Science Current

Data Silos

ETL

SQL

Data integration

Dataconomy

JUNE 18, 2025

Accessibility for analytics Centralized data repositories enhance access for analysts and data scientists, streamlining robust data analysis and allowing for comprehensive insights that drive strategic decisions. Data integration fosters a more interconnected environment, enabling seamless information flow.

Data Warehouse

Data Warehouse Data Silos ETL Big Data

Data Activation for Beginners: Everything You Need to Know

Smart Data Collective

MAY 31, 2022

It’s more than just data that provides the information necessary to make wise, data-driven decisions. It’s more than just allowing access to data warehouses that were becoming dangerously close to data silos. Data activation is about giving businesses the power to make data serve them.

ETL

ETL Data Silos Data Warehouse Big Data

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Trending Sources

The power of remote engine execution for ETL/ELT data pipelines

IBM Journey to AI blog

MAY 15, 2024

According to International Data Corporation (IDC), stored data is set to increase by 250% by 2025 , with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase costs and complicate the governance of AI and data workloads.

Data Pipeline

Data Pipeline ETL SQL Database

Learn the Differences Between ETL and ELT

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

ETL

ETL Data Warehouse Data Quality Data Lakes

Tackling AI’s data challenges with IBM databases on AWS

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

AWS

AWS Database ETL AI

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Pickl AI

NOVEMBER 15, 2023

This centralization streamlines data access, facilitating more efficient analysis and reducing the challenges associated with siloed information. With all data in one place, businesses can break down data silos and gain holistic insights. What Is a Data Warehouse?

Data Lakes

Data Lakes Data Warehouse Database ETL

Data architecture strategy for data quality

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Learn more about the benefits of data fabric and IBM Cloud Pak for Data.

Data Quality

Data Quality Data Lakes Data Warehouse Big Data

Drowning in Data? A Data Lake May Be Your Lifesaver

ODSC - Open Data Science

SEPTEMBER 29, 2023

A 2019 survey by McKinsey on global data transformation revealed that 30 percent of total time spent by enterprise IT teams was spent on non-value-added tasks related to poor data quality and availability. The data lake can then refine, enrich, index, and analyze that data. It truly is an all-in-one data lake solution.

Data Lakes

Data Lakes Clustering Big Data Big Data

AI that’s ready for business starts with data that’s ready for AI

IBM Journey to AI blog

JULY 3, 2024

Open is creating a foundation for storing, managing, integrating and accessing data built on open and interoperable capabilities that span hybrid cloud deployments, data storage, data formats, query engines, governance and metadata. With an open data lakehouse, you can access a single copy of data wherever your data resides.

AI AI Data Quality Database

Data platform trinity: Competitive or complementary?

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”.

Data Lakes

Data Lakes Data Warehouse Azure Apache Hadoop

Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

Flipboard

MARCH 21, 2025

Traditionally, answering this question would involve multiple data exports, complex extract, transform, and load (ETL) processes, and careful data synchronization across systems. SageMaker Unified Studio provides a unified experience for using data, analytics, and AI capabilities.

SQL

SQL Data Analyst Data Warehouse AWS

Simplify data access for your enterprise using Amazon SageMaker Lakehouse

Flipboard

DECEMBER 4, 2024

Currently, organizations often create custom solutions to connect these systems, but they want a more unified approach that them to choose the best tools while providing a streamlined experience for their data teams. You can use Amazon SageMaker Lakehouse to achieve unified access to data in both data warehouses and data lakes.

Data Lakes

Data Lakes Data Warehouse AWS Database

The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

phData

SEPTEMBER 27, 2024

Some modern CDPs are starting to incorporate these concepts, allowing for more flexible and evolving customer data models. It also requires a shift in how we query our customer data. Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying.

Data Modeling

Data Modeling Data Models Apache Kafka Data Lakes

Data integration

Data Activation for Beginners: Everything You Need to Know

Trending Sources

The power of remote engine execution for ETL/ELT data pipelines

Learn the Differences Between ETL and ELT

Tackling AI’s data challenges with IBM databases on AWS

Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world

Data architecture strategy for data quality

Drowning in Data? A Data Lake May Be Your Lifesaver

AI that’s ready for business starts with data that’s ready for AI

Data platform trinity: Competitive or complementary?

Connect, share, and query where your data sits using Amazon SageMaker Unified Studio

Simplify data access for your enterprise using Amazon SageMaker Lakehouse

The Evolution of Customer Data Modeling: From Static Profiles to Dynamic Customer 360

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