Timo Elliott

DECEMBER 14, 2022

Most innovation platforms make you rip the data out of your existing applications and move it to some another environment—a data warehouse, or data lake, or data lake house or data cloud—before you can do any innovation.

Data Lake 105

Data Lake Recreation/Entertainment Data Warehouse Metadata 105

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.

Analytics 94

Analytics Data Warehouse Data Lake Metadata 94

Domino Data Lab

JANUARY 11, 2021

Data Science works best with a high degree of data granularity when the data offers the closest possible representation of what happened during actual events – as in financial transactions, medical consultations or marketing campaign results. To drop and recreate the table, we specify ‘replace’ as the argument to if_exists.

Data Science 103

Data Science Recreation/Entertainment Data Warehouse Publishing 103

CIO Business Intelligence

APRIL 26, 2022

Despite nearly $1 billion in online revenue in 2020, the web-based outdoor recreational retailer was running its entire business on an outdated and unsupported e-commerce platform called ADT. Backcountry also lacked many core services critical for an online retailer — no CMS, no analytics, no data platform, and no data lake.

Data Lake 98

Data Lake Dashboards Recreation/Entertainment Sales 98

Jen Stirrup

MAY 5, 2020

You can also use Azure Data Lake storage as well, which is optimized for high-performance analytics. It has native integration with other data sources, such as SQL Data Warehouse, Azure Cosmos, database storage, and even Azure Blob Storage as well. The data is also distributed. Azure Data Lake Store.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

Timo Elliott

JUNE 19, 2023

The next area is data. There’s a huge disruption around data. For a long time, we’ve always ripped data out of our core systems and put it into a data warehouse or a data lake or a data lake house or a data cloud. And then you have to recreate it all in this new area.

Data Lake 105

Data Lake Digital Transformation Recreation/Entertainment Technology 105

Cloudera

NOVEMBER 12, 2020

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

Data Warehouse 104

Data Warehouse Reporting Risk Cost-Benefit 104

Timo Elliott

NOVEMBER 30, 2022

Because of technology limitations, we have always had to start by ripping information from the business systems and moving it to a different platform—a data warehouse, data lake, data lakehouse, data cloud. It’s possible to do, but it takes huge amounts of time and effort to recreate all that from scratch.

Recreation/Entertainment 98

Recreation/Entertainment Metadata Data Warehouse Cost-Benefit 98

Jet Global

MAY 27, 2022

How Synapse works with Data Lakes and Warehouses. Synapse services, data lakes, and data warehouses are often discussed together. Here’s how they correlate: Data lake: An information repository that can be stored in a variety of different ways, typically in a raw format like SQL.

Data Lake 52

Data Lake IT Recreation/Entertainment Data Warehouse 52

AWS Big Data

JUNE 27, 2024

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more. This enables you to use your data to acquire new insights for your business and customers. Document the entire disaster recovery process.

Snapshot 92

Snapshot Data Warehouse Data Processing Strategy 92

Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

BizAcuity

APRIL 14, 2022

The best way to avoid poor data quality is having a strict data governance system in place. The majority of the data a business has stored is generally unstructured. Most of these are accumulated in data silos or data lakes. Which means queries for large data sets might take days or eventually fail.

Big Data 52

Big Data Data Analytics Management Analytics 52

AWS Big Data

OCTOBER 10, 2024

Amazon Redshift has established itself as a highly scalable, fully managed cloud data warehouse trusted by tens of thousands of customers for its superior price-performance and advanced data analytics capabilities. This allows you to maintain a comprehensive view of your data while optimizing for cost-efficiency.

Data Lake 118

Data Lake Data Warehouse Recreation/Entertainment Data-driven 118

AWS Big Data

NOVEMBER 8, 2023

With real-time streaming data, organizations can reimagine what’s possible. From enabling predictive maintenance in manufacturing to delivering hyper-personalized content in the media and entertainment industry, and from real-time fraud detection in finance to precision agriculture in farming, the potential applications are vast.

Data-driven 105

Data-driven Machine Learning Data Lake Cost-Benefit 105

Birst BI

OCTOBER 24, 2019

When we look at tools like Microsoft’s Power BI and Tableau, you must recreate complex data objects repeatedly across different teams and use cases. This is not conducive to ongoing and repeatable insights and value generation out of your data assets. This includes ETL processes and subsequent augmented and extended data sets.

Data Warehouse 51

Data Warehouse Recreation/Entertainment Data Lake Data Architecture 51