databricks

JUNE 4, 2025

Amazon Web Services (AWS) returns as a Legend Sponsor at Data + AI Summit 2025 , the premier global event for data, analytics, and AI. Taking place in San Francisco and virtually from June 9-12 , this year’s summit will bring together 20,000+ data leaders and practitioners to explore the impact and future of data and AI.

AWS 130

AWS AI AI Data Science 130

Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. Google BigQuery: Google BigQuery is a serverless, cloud-based data warehouse designed for big data analytics.

Data Engineering 195

Data Engineering Data Engineering Data Engineering Data Engineer 195

Dataconomy

MARCH 20, 2025

Methods of creating data marts Let’s explain those methods. ETL processes ETL, or Extract, Transform, Load, plays a pivotal role in the creation of data marts. This process extracts data from various sources, transforms it into a desired format, and loads it into the data mart.

Data Warehouse 91

Data Warehouse ETL Data Quality Database 91

Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

ETL 52

ETL Data Warehouse Data Quality Data Governance 52

Pickl AI

OCTOBER 15, 2024

Summary: Selecting the right ETL platform is vital for efficient data integration. Consider your business needs, compare features, and evaluate costs to enhance data accuracy and operational efficiency. Introduction In today’s data-driven world, businesses rely heavily on ETL platforms to streamline data integration processes.

ETL 52

ETL Azure AWS Data Governance 52

Dataconomy

FEBRUARY 25, 2025

The evolution of artificial intelligence (AI) has highlighted the critical need for AI-ready data systems within modern enterprises. As organizations strive to keep pace with increasing data volumes, balancing effective data acquisition, optimal storage, and real-time analytics becomes essential.

Data Pipeline 103

Data Pipeline AI AI ETL 103

Smart Data Collective

MAY 20, 2019

We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason. The Rise of Regulation.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Pickl AI

OCTOBER 17, 2024

Summary: The ETL process, which consists of data extraction, transformation, and loading, is vital for effective data management. Following best practices and using suitable tools enhances data integrity and quality, supporting informed decision-making. Introduction The ETL process is crucial in modern data management.

ETL 52

ETL Data Warehouse SQL Data Quality 52

Alation

APRIL 25, 2023

That means if you haven’t already incorporated a plan for data governance into your long-term vision for your business, the time is now. Let’s take a closer look at what data governance is — and the top five mistakes to avoid when implementing it. 5 common data governance mistakes 1.

Data Governance 52

Data Governance ETL Machine Learning Machine Learning 52

Dataconomy

MAY 26, 2025

Big data management refers to the strategies and processes involved in handling extensive volumes of structured and unstructured data to ensure high data quality and accessibility for analytics and business intelligence applications. Ensure compliance: Proper management helps meet regulatory data governance requirements.

Big Data 91

Big Data Big Data Apache Hadoop Data Quality 91

Dataconomy

DECEMBER 17, 2024

Example: For a project to optimize supply chain operations, the scope might include creating dashboards for inventory tracking but exclude advanced predictive analytics in the first phase. Define data needs : Specify datasets, attributes, granularity, and update frequency. Key questions to ask: What data sources are required?

Data Quality 91

Data Quality Power BI Data Engineering Data Engineering 91

Alation

APRIL 25, 2023

That means if you haven’t already incorporated a plan for data governance into your long-term vision for your business, the time is now. Let’s take a closer look at what data governance is — and the top five mistakes to avoid when implementing it. 5 common data governance mistakes 1.

Data Governance 40

Data Governance ETL Machine Learning Machine Learning 40

IBM Data Science in Practice

NOVEMBER 28, 2022

As organizations steer their business strategies to become data-driven decision-making organizations, data and analytics are more crucial than ever before. The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.

Data Pipeline 130

Data Pipeline Data Quality Data Preparation Data Governance 130

Flipboard

NOVEMBER 27, 2024

His mission is to enable customers achieve their business goals and create value with data and AI. He helps architect solutions across AI/ML applications, enterprise data platforms, data governance, and unified search in enterprises.

AWS 148

AWS Database ML ML 148

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 […].

Analytics 69

Analytics Analytics Data Silos Data Lakes 69

Pickl AI

DECEMBER 10, 2024

Summary : Data Analytics trends like generative AI, edge computing, and Explainable AI redefine insights and decision-making. Businesses harness these innovations for real-time analytics, operational efficiency, and data democratisation, ensuring competitiveness in 2025. billion by 2030, with an impressive CAGR of 27.3%

Analytics 52

Analytics Analytics Augmented Analytics Machine Learning 52

IBM Journey to AI blog

MARCH 14, 2024

The solution: IBM databases on AWS To solve for these challenges, IBM’s portfolio of SaaS database solutions on Amazon Web Services (AWS), enables enterprises to scale applications, analytics and AI across the hybrid cloud landscape. It enables secure data sharing for analytics and AI across your ecosystem.

AWS 93

AWS Database ETL AI 93

IBM Journey to AI blog

JUNE 29, 2023

Learn more about IBM Planning Analytics Integrated business planning framework Integrated Business Planning (IBP) is a holistic approach that integrates strategic planning, operational planning, and financial planning within an organization. These tools enable the extraction, transformation, and loading (ETL) of data from various sources.

Analytics 94

Analytics Analytics Business Intelligence Business Intelligence 94

Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

Big Data 119

Big Data Big Data Data Engineering Data Engineering 119

ODSC - Open Data Science

JUNE 19, 2023

In particular, its progress depends on the availability of related technologies that make the handling of huge volumes of data possible. These technologies include the following: Data governance and management  — It is crucial to have a solid data management system and governance practices to ensure data accuracy, consistency, and security.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

AWS Machine Learning Blog

JUNE 9, 2025

We use multiple data sources, including Amazon S3 for our storage needs, Amazon QuickSight for our business intelligence requirements, and Google Drive for team collaboration. Noah Kershaw leads the product team at Kepler Group, a global digital marketing agency that helps brands connect with their audiences through data-driven strategies.

AI 88

AI AI AWS Business Intelligence 88

IBM Journey to AI blog

APRIL 20, 2023

This trust depends on an understanding of the data that inform risk models: where does it come from, where is it being used, and what are the ripple effects of a change? Moreover, banks must stay in compliance with industry regulations like BCBS 239, which focus on improving banks’ risk data aggregation and risk reporting capabilities.

Database 86

Database Data Engineering Data Engineering Data Engineering 86

IBM Journey to AI blog

AUGUST 4, 2023

Data democratization instead refers to the simplification of all processes related to data, from storage architecture to data management to data security. It also requires an organization-wide data governance approach, from adopting new types of employee training to creating new policies for data storage.

Data Lakes 73

Data Lakes AI AI Data Governance 73

phData

NOVEMBER 9, 2023

The main goal of a data mesh structure is to drive: Domain-driven ownership Data as a product Self-service infrastructure Federated governance One of the primary challenges that organizations face is data governance. As this happens, spending shifts from ELT to analytics.

Data Warehouse 98

Data Warehouse Data Lakes Clustering Cloud Data 98

Dataversity

DECEMBER 14, 2021

Watching closely the evolution of metadata platforms (later rechristened as Data Governance platforms due to their focus), as somebody who has implemented and built Data Governance solutions on top of these platforms, I see a significant evolution in their architecture as well as the use cases they support.

Data Governance 52

Data Governance Data Observability ETL Data Quality 52

Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. BI tools rely on high-quality, consistent data to generate accurate insights.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 15, 2023

Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources. With all data in one place, businesses can break down data silos and gain holistic insights.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

JUNE 11, 2024

Understanding Data Warehouse Functionality A data warehouse acts as a central repository for historical data extracted from various operational systems within an organization. This allows businesses to analyze trends, identify patterns, and make informed decisions based on historical data.

Data Warehouse 52

Data Warehouse ETL Data Mining Data Mining 52

Dataconomy

FEBRUARY 23, 2023

Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

Pickl AI

NOVEMBER 4, 2024

Key Takeaways Data Engineering is vital for transforming raw data into actionable insights. Key components include data modelling, warehousing, pipelines, and integration. Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Alation

MAY 16, 2023

Data is a valuable resource, especially in the world of business. A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

phData

AUGUST 9, 2024

Imagine you are building out a routine sales report in Snowflake AI Data Cloud when you come across a requirement for a field called “Is Platinum Customer.” This scenario is all too common to analytics engineers. The data we get from the source systems is often incomplete and needs to be augmented with external data.

ETL 52

ETL Data Warehouse Data Governance Tableau 52

ODSC - Open Data Science

JUNE 12, 2023

The Datamarts capability opens endless possibilities for organizations to achieve their data analytics goals on the Power BI platform. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

Power BI 52

Power BI Data Warehouse ETL Data Preparation 52

Pickl AI

OCTOBER 19, 2023

Regular Data Audits Conduct regular data audits to identify issues and discrepancies. This proactive approach allows you to detect and address problems before they compromise data quality. Data Governance Framework Implement a robust data governance framework. How Do You Fix Poor Data Quality?

Data Quality 52

Data Quality Data Governance Data Warehouse Machine Learning 52

ODSC - Open Data Science

SEPTEMBER 27, 2023

Data Warehouses and Relational Databases It is essential to distinguish data lakes from data warehouses and relational databases, as each serves different purposes and has distinct characteristics. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. A Note on the Shift from ETL to ELT.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Read Many of the preferred platforms for analytics fall into one of these two categories.

Data Warehouse 59

Data Warehouse Data Lakes Hadoop Big Data 59