Astera

APRIL 27, 2023

Data Lake Vs Data Warehouse Every business needs to store, analyze, and make decisions based on data. To do this, they must choose between two popular data storage technologies: data lakes and data warehouses. What is a Data Lake? What is a Data Warehouse?

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Data Warehouse Cloud Computing Data Management Data Governance 52

InfoQ Articles

APRIL 22, 2021

In this article, we will define a new reference architecture for cloud-native companies that are looking for a simplified access management solution for their cloud resources, from SSH hosts, databases, data warehouses, to message pipelines and cloud storage endpoints. By Manav Mital.

Data Warehouse 76

Data Warehouse 76

Astera

DECEMBER 21, 2022

Data and analytics are indispensable for businesses to stay competitive in the market. Hence, it’s critical for you to look into how cloud data warehouse tools can help you improve your system. According to Mordor Intelligence , the demand for data warehouse solutions will reach $13.32 billion by 2026. Ease of Use.

Data Warehouse 52

Data Warehouse Cloud Computing Cybersecurity Data Modelling 52

Smart Data Collective

APRIL 8, 2020

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

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Data Warehouse Big Data Business Intelligence Business Intelligence 336

Smart Data Collective

OCTOBER 17, 2022

A point of data entry in a given pipeline. Examples of an origin include storage systems like data lakes, data warehouses and data sources that include IoT devices, transaction processing applications, APIs or social media. The final point to which the data has to be eventually transferred is a destination.

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Data Warehouse Big Data Procurement Data Visualization 327

Smart Data Collective

JUNE 6, 2021

Before diving into whether or not Google BigQuery is the future of big data analytics, it’s vital to firstly understand what “big data analytics” actually means. Google BigQuery is a service (within the Google Cloud platform (GCP)) implemented to collect and analyze big data (also known as a data warehouse).

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ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business.

Data Management 130

Data Management Data Warehouse Data Governance Government 130

ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business. Data Volume, Transformation and Location.

Data Management 130

Data Management Data Warehouse Data Governance Government 130

ElegantJ BI

SEPTEMBER 30, 2021

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In this article, we will present the factors and considerations involved in choosing the right data management solution for your business. Data Volume, Transformation and Location.

Data Management 130

Data Management Data Warehouse Data Governance Government 130

Sisense

SEPTEMBER 3, 2019

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. OLTP databases are best at queries where we are doing point scans or short scans of the data, think “return the number of deposits by X user this week.”.

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Data Warehouse Data Architecture Planning Big Data 78

BizAcuity

APRIL 1, 2023

Teradata is based on a parallel Data Warehouse with shared-nothing architecture. Data is stored in a row-based format. It supports a hybrid storage model in which frequently accessed data is stored in SSD whereas rarely accessed data is stored on HDD. The BYNET interconnect supports up to 512 nodes.

Data Warehouse 130

Data Warehouse Monitoring Planning Data Modelling 130

BizAcuity

SEPTEMBER 22, 2022

Teradata is based on a parallel Data Warehouse with shared-nothing architecture. Data is stored in a row-based format. It supports a hybrid storage model in which frequently accessed data is stored in SSD whereas rarely accessed data is stored on HDD. Not being an agile cloud data warehouse.

Data Warehouse 130

Data Warehouse Monitoring Planning Data Modelling 130

Smart Data Collective

APRIL 29, 2020

What is data management? Data management can be defined in many ways. Usually the term refers to the practices, techniques and tools that allow access and delivery through different fields and data structures in an organisation. Data transformation. Data analytics and visualisation.

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Data Management Data Warehouse Master Data Management Digital Transformation 208

Smart Data Collective

JUNE 16, 2022

Online Analytical Processing (OLAP) is a term that refers to the process of analyzing data online. Data processing and analysis are usually done with a simple spreadsheet, which has data values organized in a row and column structure. The data is processed and modified after it has been extracted.

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Data Warehouse Data Modelling Monitoring Business Intelligence 325

Smart Data Collective

DECEMBER 13, 2022

Five Best Practices for Data Analytics. Extracted data must be saved someplace. There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Select a Storage Platform.

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Visualization Data Analytics Data Analytics Artificial Intelligence 340

BizAcuity

APRIL 1, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Mining 130

Business Analysis Knowledge Share

OCTOBER 24, 2023

Data Warehousing is the process of collecting, storing, and managing data from various sources into a central repository. This repository, often referred to as a data warehouse , is specifically designed for query and analysis. Data Sources Data Warehouses collect data from diverse sources within an organization.

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IBM Maintenance Data Warehouse IBM cost Data Quality 73

BizAcuity

JANUARY 31, 2023

Thanks to the recent technological innovations and circumstances to their rapid adoption, having a data warehouse has become quite common in various enterprises across sectors. Data governance and security measures are critical components of data strategy.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Mining 130

Analysts Corner

AUGUST 13, 2023

As organizations increasingly rely on data to drive their operations, strategy, and innovation, ensuring data integrity and usability has never been more important. This is where data profiling comes into play. It’s akin to conducting a “health check” on your data to assess its quality, integrity, consistency, and usability.

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Astera

JANUARY 11, 2024

Understanding the Shift from Siloed Data to Centralized Data Many organizations still operate with siloed financial data, limiting their ability to harness analytics’ power fully. In such cases, data isn’t easily accessible or shared across the organization.

Data Warehouse 52

Data Warehouse Finance Visualization Planning 52

ElegantJ BI

NOVEMBER 19, 2019

ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Security Data Quality Monitoring 130

ElegantJ BI

NOVEMBER 19, 2019

ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Security Data Quality Monitoring 130

ElegantJ BI

NOVEMBER 19, 2019

ETL, as it is called, refers to the process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Security Data Quality Monitoring 130

Astera

JANUARY 25, 2024

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

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E-Commerce Data Warehouse Retail Customer Experience 52

Sisense

APRIL 17, 2020

We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. Why use a materialized view?

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Astera

JANUARY 11, 2023

The primary purpose of your data warehouse is to serve as a centralized repository for historical data that can be quickly queried for BI reporting and analysis. Data modeling — which defines the database schema — is the heart of your data warehouse . Develop and deploy high-volume data warehouses.

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Data Warehouse Data Modelling 52

Astera

JANUARY 11, 2023

The primary purpose of your data warehouse is to serve as a centralized repository for historical data that can be quickly queried for BI reporting and analysis. Data modeling — which defines the database schema — is the heart of your data warehouse . Automated Forward Engineering — The Astera Way .

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Data Warehouse Data Modelling 52

Insight Software

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

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IBM Maintenance Data Warehouse IBM Support Real-time Data 52

Astera

MAY 9, 2023

This process involves the following six stages: Data Collection Data is gathered from reliable sources, including databases such as data lakes and data warehouses. Data Preparation The data collected in the first stage is then prepared and cleaned.

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Astera

MARCH 20, 2023

For instance, organizations often encounter the challenge of synchronizing disparate databases to create a standardized model or warehouse. Astera DW Builder provides a uniform way for designing data warehouses that is compatible with all databases. But what is reverse engineering?

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Data Modelling Data Warehouse Visualization Business Intelligence 52

Astera

DECEMBER 14, 2022

Kimball-style dimensional modeling has been the go-to architecture for most data warehouse developers over the past couple of decades. The questions that arise, however, are the following: How easy is it to load and maintain data in fact and dimension tables? And Is it worth the effort? Do you realize what that would require?

Data Warehouse 52

Data Warehouse Data Modelling Business Intelligence Business Intelligence 52

Astera

MARCH 11, 2024

It eliminates the need for complex infrastructure management, resulting in streamlined operations. According to a recent Gartner survey, 85% of enterprises now use cloud-based data warehouses like Snowflake for their analytics needs. Stitch also offers solutions for non-technical teams to quickly set up data pipelines.

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Data Warehouse Documentation Monitoring Data Quality 52

Astera

SEPTEMBER 20, 2023

Enforces data quality standards through transformations and cleansing as part of the integration process. Use Cases Use cases include data lakes and data warehouses for storage and initial processing. Use cases include creating data warehouses, data marts, and consolidated data views for analytics and reporting.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

Astera

SEPTEMBER 20, 2023

Enforces data quality standards through transformations and cleansing as part of the integration process. Use Cases Use cases include data lakes and data warehouses for storage and initial processing. Use cases include creating data warehouses, data marts, and consolidated data views for analytics and reporting.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data E-Commerce 52

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Data Quality 130

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Data Quality 130

ElegantJ BI

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 130

Data Warehouse Data Governance Government Data Quality 130