Astera

JANUARY 23, 2024

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. The combination of data vault and information marts solves this problem.

Agile 52

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Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

Data Warehouse 52

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Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

Data Warehouse 52

Data Warehouse Data Management Data Modelling Data Quality 52

Astera

OCTOBER 27, 2023

What is a Cloud Data Warehouse? Simply put, a cloud data warehouse is a data warehouse that exists in the cloud environment, capable of combining exabytes of data from multiple sources. A cloud data warehouse is critical to make quick, data-driven decisions.

Data Warehouse 52

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Astera

FEBRUARY 27, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

Data Warehouse 52

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Astera

FEBRUARY 27, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

APRIL 7, 2023

Finally, the stored data is retrieved at optimal speeds to support efficient analysis and decision-making. Essentially, a data warehouse also acts as a centralized database for storing structured, analysis-ready data and giving a holistic view of this data to decision-makers.

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Data Warehouse Healthcare Data Management Data Quality 52

Analysts Corner

JUNE 18, 2023

ETL Developer: Defining the Role An ETL developer is a professional responsible for designing, implementing, and managing ETL processes that extract, transform, and load data from various sources into a target data store, such as a data warehouse. Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

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Data Warehouse Data Quality Documentation Data Governance 130

Actian

JUNE 4, 2019

Implementing a data warehouse is a big investment for most companies and the decisions you make now will impact both your IT costs and the business value you are able to create for many years. Data Warehouse Cost. Your data warehouse is the centralized repository for your company’s data assets.

Data Warehouse 40

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Astera

JANUARY 24, 2024

Modern data management relies heavily on ETL (extract, transform, load) procedures to help collect, process, and deliver data into an organization’s data warehouse. However, ETL is not the only technology that helps an enterprise leverage its data. It provides multiple security measures for data protection.

Data Warehouse 52

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Astera

SEPTEMBER 1, 2023

The significance of data warehousing for insurance cannot be overstated. It forms the bedrock of modern insurance operations, facilitating data-driven insights and streamlined processes to better serve policyholders. The data warehouse has the highest adoption of data solutions, used by 54% of organizations.

Data Warehouse 40

Data Warehouse Data Modelling Data Security Data Quality 40

Tableau

APRIL 18, 2022

Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Data quality and lineage. Data modeling. Metadata management.

Data Quality 99

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Tableau

APRIL 18, 2022

Tableau helps strike the necessary balance to access, improve data quality, and prepare and model data for analytics use cases, while writing-back data to data management sources. Analytics data catalog. Data quality and lineage. Data modeling. Metadata management.

Data Quality 97

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Astera

JANUARY 2, 2024

Free Download Here’s what the data management process generally looks like: Gathering Data: The process begins with the collection of raw data from various sources. Once collected, the data needs a home, so it’s stored in databases, data warehouses , or other storage systems, ensuring it’s easily accessible when needed.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

Astera

JANUARY 31, 2024

Its extensibility is evident through components, such as SQL Server Integration Services (SSIS) for ETL , SQL Server Reporting Services (SSRS) for creating and managing reports, SQL Server Analysis Service (SSAS) for analytical data models, and so on. Azure SQL Database).

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Astera

MARCH 28, 2024

Additionally, data catalogs include features such as data lineage tracking and governance capabilities to ensure data quality and compliance. On the other hand, a data dictionary typically provides technical metadata and is commonly used as a reference for data modeling and database design.

Data Governance 52

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Astera

JUNE 14, 2023

Transformation: Converting data into a consistent format for easy use. Aligning external and internal data formats. Handling inaccurate and abnormal data. Ensuring data quality and consistency. Loading/Integration: Establishing a robust data storage system to store all the transformed data.

Procurement 52

Procurement Data Security Retail Monitoring 52

Astera

JULY 13, 2023

This process includes moving data from its original locations, transforming and cleaning it as needed, and storing it in a central repository. Data integration can be challenging because data can come from a variety of sources, such as different databases, spreadsheets, and data warehouses.

Data Warehouse 40

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Astera

AUGUST 22, 2023

Grid View: The Grid View presents a dynamic and interactive grid that updates in real time, displaying the transformed data after each operation. It offers an instant preview and feedback on data quality, helping you ensure the accuracy and integrity of your data.

Innovation 52

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Insight Software

SEPTEMBER 1, 2023

These include data privacy and security concerns, model accuracy and bias challenges, user perception and trust issues, and the dependency on data quality and availability. Data Privacy and Security Concerns: Embedded predictive analytics often require access to sensitive user data for accurate predictions.

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