Analytics Vidhya

OCTOBER 10, 2022

This article was published as a part of the Data Science Blogathon. Introduction Most of you would know the different approaches for building a data and analytics platform. You would have already worked on systems that used traditional warehouses or Hadoop-based data lakes. Selecting one among […].

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Data Lake Data Science Publishing Analytics 284

O'Reilly on Data

FEBRUARY 19, 2021

The Right Solution for Your Data: Cloud Data Lakes and Data Lakehouses. Data lakes have experienced a fairly robust resurgence over the last few years, specifically cloud data lakes. A Wave of Cloud-Native, Distributed Data Frameworks.

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Data Lake Data Warehouse Machine Learning Modeling 289

Analytics Vidhya

OCTOBER 10, 2022

This article was published as a part of the Data Science Blogathon. Introduction In the modern data world, Lakehouse has become one of the most discussed topics for building a data platform.

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Data Lake Data Science Publishing Enterprise 340

Data Virtualization

NOVEMBER 24, 2022

Reading Time: 4 minutes My previous post explained that, in my mind, the data lakehouse differs hardly at all from the traditional data warehouse architectural design pattern (ADP). It consists largely of the application of new cloud-based technology to the same requirements and constraints.

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Data Warehouse Data Integration Technology Management 52

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.

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Data Science Data Analytics Prescriptive Analytics Analytics 84

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.

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Data Lake Machine Learning Data Warehouse Digital Transformation 86

Data Virtualization

MARCH 16, 2023

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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Data Virtualization

MARCH 16, 2023

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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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.

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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.

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Optimization Data Lake Data Warehouse Manufacturing 87

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.

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Data Warehouse Data Lake Manufacturing Testing 246

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.

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Data Lake Data Warehouse Machine Learning Cost-Benefit 90

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?

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Data Virtualization

OCTOBER 5, 2022

For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.

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Data Lake Data Architecture Data Integration Management 52

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.

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Data Lake Data Warehouse Machine Learning Cost-Benefit 81

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 […]

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Business Intelligence Data Lake Data Warehouse Data-driven 98

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.

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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.

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Data Virtualization

FEBRUARY 22, 2023

Reading Time: 2 minutes The data lakehouse attempts to combine the best parts of the data warehouse with the best parts of data lakes while avoiding all of the problems inherent in both. However, the data lakehouse is not the last word in data.

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Data Virtualization

FEBRUARY 22, 2023

Reading Time: 2 minutes The data lakehouse attempts to combine the best parts of the data warehouse with the best parts of data lakes while avoiding all of the problems inherent in both. However, the data lakehouse is not the last word in data.

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Data Lake Data Warehouse Data Integration Management 52

Data Virtualization

JANUARY 19, 2023

Reading Time: 2 minutes Today, many businesses are modernizing their on-premises data warehouses or cloud-based data lakes using Microsoft Azure Synapse Analytics. Unfortunately, with data spread.

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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.

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AWS Big Data

MARCH 7, 2023

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

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Analytics Data Warehouse Data Lake Metadata 69

Data Virtualization

JULY 1, 2021

If we look at a typical , many of its stages have more to do with data than science. Before data scientists can begin their work regarding data science, they often must begin by: Finding the right data Gaining access.

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CDW Research Hub

JULY 27, 2021

Data volumes are growing exponentially, and traditional, on-premises data warehouses are constrained, overly complex, and costly to scale. In this way, the Cloud Data Warehouse Accelerator enables a seamless transition to Snowflake. Reduce the total cost of ownership of the data infrastructure.

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CIO Business Intelligence

JULY 24, 2022

He announced his departure on LinkedIn and reflected on some of the achievements during the five years with the department which included building an advanced data analytics platforms utilising data warehouse, a data lake, data science containers and supporting visualisation tools. IT Leadership

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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.

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CIO Business Intelligence

NOVEMBER 28, 2023

Data, of course, has been all the rage the past decade, having been declared the “new oil” of the digital economy. And yes, data has enormous potential to create value for your business, making its accrual and the analysis of it, aka data science, very exciting.

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Cloudera

JUNE 2, 2022

This blog aims to answer two questions: What is a universal data distribution service? Why does every organization need it when using a modern data stack? Every organization on the hybrid cloud journey needs the ability to take control of their data flows from origination through all points of consumption.

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Enterprise Data Lake Data Collection Data-driven 99

Peter James Thomas

AUGUST 18, 2018

That was the Science, here comes the Technology… A Brief Hydrology of Data Lakes. Overlapping with the above, from around 2012, I began to get involved in also designing and implementing Big Data Architectures; initially for narrow purposes and later Data Lakes spanning entire enterprises. In Closing.

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Data Lake Data Warehouse Data mining Statistics 63

Domino Data Lab

JANUARY 11, 2021

Arming data science teams with the access and capabilities needed to establish a two-way flow of information is one critical challenge many organizations face when it comes to unlocking value from their modeling efforts. Domino Data Lab and Snowflake: Better Together. Introduction. About Snowflake.

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Recreation/Entertainment Data Science Data Warehouse Modeling 103

IBM Big Data Hub

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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CIO Business Intelligence

JULY 13, 2022

This blog aims to answer two questions: What is a universal data distribution service? Why does every organization need it when using a modern data stack? Every organization on the hybrid cloud journey needs the ability to take control of their data flows from origination through all points of consumption.

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Enterprise Data Lake Data Collection Data-driven 60

Andrew White

DECEMBER 9, 2019

Records (files, or what you might all unstructured data). Analytical stewardship is a missing link in analytics, BI and data science. While firms can now be effective with operational data governance in business application (see webinar Effective Data and Analytics Governance – Finally! Images (i.e.

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Analytics Data Lake Data Governance Metadata 66

Alation

FEBRUARY 13, 2020

Data curation is important in today’s world of data sharing and self-service analytics, but I think it is a frequently misused term. When speaking and consulting, I often hear people refer to data in their data lakes and data warehouses as curated data, believing that it is curated because it is stored as shareable data.

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Metadata Data Warehouse Data Lake Data Governance 52

IBM Big Data Hub

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

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Risk Modeling Management Metadata 77

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.

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