The MLOps Blog

MAY 17, 2023

However, efficient use of ETL pipelines in ML can help make their life much easier. This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines.

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Precisely

FEBRUARY 20, 2025

Thats where data integration comes in. Data integration breaks down data silos by giving users self-service access to enterprise data, which ensures your AI initiatives are fueled by complete, relevant, and timely information. Assessing potential challenges , like resource constraints or existing data silos.

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Data Silos AI AI Data Quality 69

IBM Journey to AI blog

OCTOBER 22, 2024

The data universe is expected to grow exponentially with data rapidly propagating on-premises and across clouds, applications and locations with compromised quality. This situation will exacerbate data silos, increase pressure to manage cloud costs efficiently and complicate governance of AI and data workloads.

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Data Silos Data Pipeline DataOps Business Intelligence 102

Smart Data Collective

OCTOBER 20, 2021

In this new reality, leveraging processes like ETL (Extract, Transform, Load) or API (Application Programming Interface) alone to handle the data deluge is not enough. As per the TDWI survey, more than a third (nearly 37%) of people has shown dissatisfaction with their ability to access and integrate complex data streams.

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Dataconomy

MAY 26, 2025

These platforms facilitate heavy data lifting, making it easier to manage large datasets. Real-time integration: Techniques that allow for timely data processing to support immediate decision-making. Building robust architecture Creating a flexible data architecture ensures that the management system is able to adapt to evolving needs.

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Big Data Big Data Apache Hadoop Data Quality 91

IBM Journey to AI blog

MAY 9, 2024

Challenges in data governance for healthcare and how data lineage can help Data governance can help healthcare organizations maximize the accuracy and security of their data assets. Data quality issues Positive business decisions and outcomes rely on trustworthy, high-quality data. ” Michael L.,

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Data Governance Data Silos Data Quality Predictive Analytics 85

IBM Data Science in Practice

NOVEMBER 28, 2022

Insights from data gathered across business units improve business outcomes, but having heterogeneous data from disparate applications and storages makes it difficult for organizations to paint a big picture. How can organizations get a holistic view of data when it’s distributed across data silos?

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Data Pipeline Data Quality Data Preparation Data Governance 130

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.

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Women in Big Data

MARCH 5, 2025

I worked extensively with ETL processes, PostgreSQL, and later, enterprise-scale data systems. Ive always had a logical, data-driven mindset, constantly digging deeper into metrics and questioning assumptions. In simple terms, we help businesses modernize, democratize and transform their data.

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ML ML Big Data Big Data 52

Pickl AI

JUNE 28, 2023

Understanding Data Integration in Data Mining Data integration is the process of combining data from different sources. Thus creating a consolidated view of the data while eliminating data silos. Limited Scalability : The process is not workable for handling large volumes of data.

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phData

APRIL 18, 2023

This is due to a fragmented ecosystem of data silos, a lack of real-time fraud detection capabilities, and manual or delayed customer analytics, which results in many false positives. Snowflake Marketplace offers data from leading industry providers such as Axiom, S&P Global, and FactSet.

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Precisely

JUNE 2, 2023

As companies strive to leverage AI/ML, location intelligence, and cloud analytics into their portfolio of tools, siloed mainframe data often stands in the way of forward momentum. Data Integrity Is a Business Imperative As the number of data tools and platforms continues to grow, the amount of data silos within organizations grow too.

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Data Silos ETL Data Quality Data Pipeline 52

phData

NOVEMBER 9, 2023

The primary objective of this idea is to democratize data and make it transparent by breaking down data silos that cause friction when solving business problems. What Components Make up the Snowflake Data Cloud?

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Data Warehouse Data Lakes Clustering Cloud Data 98

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? What is meant by Data Lake?

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Data Lakes Data Warehouse Database ETL 52

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.

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Data Quality Data Lakes Data Warehouse Big Data 52

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.

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Data Lakes Clustering Big Data Big Data 52

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.

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AI Data Quality AI Database 45

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. ”.

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Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

DECEMBER 10, 2024

Unified Data Fabric Unified data fabric solutions enable seamless access to data across diverse environments, including multi-cloud and on-premise systems. These solutions break down data silos, making it easier to integrate and analyse data from various sources in real-time.

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Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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Analytics Analytics Data Silos Data Lakes 69

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.

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SQL Data Analyst Data Warehouse AWS 141

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.

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Data Lakes Data Warehouse AWS Database 139

phData

SEPTEMBER 27, 2024

If the event log is your customer’s diary, think of persistent staging as their scrapbook – a place where raw customer data is collected, organized, and kept for future reference. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data.

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Data Modeling Data Models Apache Kafka Data Lakes 52