CIO Business Intelligence

DECEMBER 6, 2024

Another offering that AWS announced to support the integration is the SageMaker Data Lakehouse , aimed at helping enterprises unify data across Amazon S3 data lakes and Amazon Redshift data warehouses.

Data Analytics 122

Data Analytics Data Lake Analytics Data Warehouse 122

AWS Big Data

DECEMBER 4, 2024

Amazon SageMaker Lakehouse , now generally available, unifies all your data across Amazon Simple Storage Service (Amazon S3) data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data. The tools to transform your business are here.

Data Analytics 103

Data Analytics Analytics Data Lake Data Quality 103

DataKitchen

AUGUST 19, 2021

Figure 3 shows an example processing architecture with data flowing in from internal and external sources. Each data source is updated on its own schedule, for example, daily, weekly or monthly. The data scientists and analysts have what they need to build analytics for the user. The new Recipes run, and BOOM! Conclusion.

Data Warehouse 246

Data Warehouse Data Lake Manufacturing Testing 246

AWS Big Data

NOVEMBER 7, 2024

BladeBridge offers a comprehensive suite of tools that automate much of the complex conversion work, allowing organizations to quickly and reliably transition their data analytics capabilities to the scalable Amazon Redshift data warehouse. times better price performance than other cloud data warehouses.

Data Warehouse 105

Data Warehouse Reporting Big Data Data Lake 105

AWS Big Data

JANUARY 15, 2025

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Two use cases illustrate how this can be applied for business intelligence (BI) and data science applications, using AWS services such as Amazon Redshift and Amazon SageMaker.

IoT 111

IoT Machine Learning Metadata Data-driven 111

AWS Big Data

MARCH 10, 2023

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 122

Data Lake Sales Data Warehouse Snapshot 122

Data Science 101

NOVEMBER 7, 2019

Microsoft just held one of its largest conferences of the year, and a few major announcements were made which pertain to the cloud data science world. Azure Synapse Analytics can be seen as a merge of Azure SQL Data Warehouse and Azure Data Lake. Those are the big data science announcements of the week.

Data Science 106

Data Science Machine Learning Data Lake Visualization 106

AWS Big Data

JUNE 10, 2024

Various data pipelines process these logs, storing petabytes (PBs) of data per month, which after processing data stored on Amazon S3, are then stored in Snowflake Data Cloud. Until recently, this data was mostly prepared by automated processes and aggregated into results tables, used by only a few internal teams.

Data Lake 126

Data Lake Metadata Snapshot Analytics 126

Data Virtualization

APRIL 21, 2022

Reading Time: 3 minutes First we had data warehouses, then came data lakes, and now the new kid on the block is the data lakehouse. But what is a data lakehouse and why should we develop one? In a way, the name describes what.

Data Lake 59

Data Lake Data Warehouse Data Integration Management 59

DataKitchen

DECEMBER 7, 2021

This book is not available until January 2022, but considering all the hype around the data mesh, we expect it to be a best seller. In the book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today’s organizations.

Data-driven 246

Data-driven Data Governance Big Data Data Science 246

O'Reilly on Data

OCTOBER 19, 2021

Similarly, it would be pointless to pretend that a data-intensive application resembles a run-off-the-mill microservice which can be built with the usual software toolchain consisting of, say, GitHub, Docker, and Kubernetes. Adapted from the book Effective Data Science Infrastructure. Data Science Layers.

IT 364

IT Testing Experimentation Software 364

Sisense

DECEMBER 23, 2019

AI and ML are the only ways to derive value from massive data lakes, cloud-native data warehouses, and other huge stores of information. There just aren’t enough AI and data science practitioners to go around to tackle this lofty goal.

Data Lake 86

Data Lake Machine Learning Data Warehouse Digital Transformation 86

KDnuggets

DECEMBER 14, 2021

We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

Data Science 159

Data Science Machine Learning Analytics Data Lake 159

CIO Business Intelligence

DECEMBER 20, 2024

Beyond breaking down silos, modern data architectures need to provide interfaces that make it easy for users to consume data using tools fit for their jobs. Data must be able to freely move to and from data warehouses, data lakes, and data marts, and interfaces must make it easy for users to consume that data.

Data Architecture 122

Data Architecture Management Consulting Internet of Things 122

AWS Big Data

OCTOBER 14, 2024

Enterprise data is brought into data lakes and data warehouses to carry out analytical, reporting, and data science use cases using AWS analytical services like Amazon Athena , Amazon Redshift , Amazon EMR , and so on.

Metadata 105

Metadata Data Lake Modeling Data Warehouse 105

David Menninger's Analyst Perspectives

OCTOBER 31, 2024

I previously wrote about the importance of open table formats to the evolution of data lakes into data lakehouses. The concept of the data lake was initially proposed as a single environment where data could be combined from multiple sources to be stored and processed to enable analysis by multiple users for multiple purposes.

Data Lake 130

Data Lake Unstructured Data Data Warehouse Software 130

CIO Business Intelligence

MARCH 9, 2022

But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for big data analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure. Meet the data lakehouse.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Big Data 119

Jen Stirrup

MAY 5, 2020

Recently, I gave a Make Your Data Work Monday webinar on the complexities of the data sources for data science in Azure, and I thought it important enough to turn into an actual post. How can you differentiate the different opportunities to store your data in Azure?

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

AWS Big Data

MARCH 28, 2023

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. As organizations across the globe are modernizing their data platforms with data lakes on Amazon Simple Storage Service (Amazon S3), handling SCDs in data lakes can be challenging.

Data Lake 101

Data Lake Testing Snapshot Sales 101

CIO Business Intelligence

NOVEMBER 25, 2022

Today, more than 90% of its applications run in the cloud, with most of its data is housed and analyzed in a homegrown enterprise data warehouse. Like many CIOs, Carhartt’s top digital leader is aware that data is the key to making advanced technologies work. Today, we backflush our data lake through our data warehouse.

Data Lake 128

Data Lake Data Warehouse Unstructured Data Data Architecture 128

CIO Business Intelligence

JUNE 29, 2022

The recent announcement of the Microsoft Intelligent Data Platform makes that more obvious, though analytics is only one part of that new brand. Azure Data Factory. Azure Data Lake Analytics. Data warehouses are designed for questions you already know you want to ask about your data, again and again.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

CIO Business Intelligence

JUNE 23, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 98

Data Lake Data Warehouse Machine Learning Data-driven 98

AWS Big Data

NOVEMBER 13, 2023

Amazon Redshift is a fully managed data warehousing service that offers both provisioned and serverless options, making it more efficient to run and scale analytics without having to manage your data warehouse. These upstream data sources constitute the data producer components.

Data Warehouse 111

Data Warehouse Analytics Data Lake Data Science 111

CDW Research Hub

JULY 18, 2022

While many organizations understand the business need for a data and analytics cloud platform , few can quickly modernize their legacy data warehouse due to a lack of skills, resources, and data literacy. Security Data Lake. Learn more about our Security Data Lake Solution.

Optimization 87

Optimization Data Lake Data Warehouse Manufacturing 87

Cloudera

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. On data warehouses and data lakes.

Data Lake 94

Data Lake Data Warehouse Machine Learning Data-driven 94

AWS Big Data

MAY 3, 2023

In today’s data-driven business environment, organizations face the challenge of efficiently preparing and transforming large amounts of data for analytics and data science purposes. Businesses need to build data warehouses and data lakes based on operational data.

Data Lake 113

Data Lake Data Warehouse Data-driven Optimization 113

AWS Big Data

NOVEMBER 8, 2023

This post was co-written with Rajiv Arora, Director of Data Science Platform at Gilead Life Sciences. Gilead Sciences, Inc. Amazon Redshift Serverless is a fully managed cloud data warehouse that allows you to seamlessly create your data warehouse with no infrastructure management required.

Data Lake 104

Data Lake Data Warehouse Cost-Benefit Optimization 104

CIO Business Intelligence

JUNE 9, 2022

All this data arrives by the terabyte, and a data management platform can help marketers make sense of it all. Marketing-focused or not, DMPs excel at negotiating with a wide array of databases, data lakes, or data warehouses, ingesting their streams of data and then cleaning, sorting, and unifying the information therein.

Management 144

Management Advertising Data Lake Sales 144

IBM Big Data Hub

JULY 5, 2023

Data Lakes have been around for well over a decade now, supporting the analytic operations of some of the largest world corporations. Such data volumes are not easy to move, migrate or modernize. The challenges of a monolithic data lake architecture Data lakes are, at a high level, single repositories of data at scale.

Data Lake 60

Data Lake Metadata Cost-Benefit Data Warehouse 60

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 76

Data Science Data Analytics Prescriptive Analytics Analytics 76

CIO Business Intelligence

OCTOBER 13, 2023

The data architect also “provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture,” according to DAMA International’s Data Management Body of Knowledge.

Data Architecture 132

Data Architecture Data Warehouse Statistics Visualization 132

CIO Business Intelligence

APRIL 25, 2022

“You can think that the general-purpose version of the Databricks Lakehouse as giving the organization 80% of what it needs to get to the productive use of its data to drive business insights and data science specific to the business. Features focus on media and entertainment firms.

Recreation/Entertainment 119

Recreation/Entertainment Data Lake Data Warehouse Unstructured Data 119

CIO Business Intelligence

MAY 24, 2022

The company said that IDMC for Financial Services has built-in metadata scanners that can help extract lineage, technical, business, operational, and usage metadata from over 50,000 systems (including data warehouses and data lakes) and applications including business intelligence, data science, CRM, and ERP software.

Finance 140

Finance Management Metadata Machine Learning 140

AWS Big Data

DECEMBER 4, 2024

In today’s data-driven world , organizations are constantly seeking efficient ways to process and analyze vast amounts of information across data lakes and warehouses. This post will showcase how this data can also be queried by other data teams using Amazon Athena. Verify that you have Python version 3.7

Data Lake 116

Data Lake Metadata Insurance Data-driven 116

Cloudera

JANUARY 21, 2021

While cloud-native, point-solution data warehouse services may serve your immediate business needs, there are dangers to the corporation as a whole when you do your own IT this way. Cloudera Data Warehouse (CDW) is here to save the day! CDW is an integrated data warehouse service within Cloudera Data Platform (CDP).

Data Warehouse 94

Data Warehouse Data Lake IT Analytics 94

Boris Evelson

JUNE 14, 2023

No matter what technology foundation you’re using – a data lake, a data warehouse, data fabric, data mesh, etc. – BI applications are where business users consume data and turn it into actionable insights and decisions. The BI market has […]

Business Intelligence 98

Business Intelligence Data Lake Data Warehouse Data-driven 98

DataKitchen

JANUARY 3, 2022

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.

Business Analytics 245

Business Analytics Analytics Testing Dashboards 245