Astera

FEBRUARY 28, 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

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

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

Agile Data Warehouse Data Quality Government 52

The BAWorld

NOVEMBER 22, 2021

Data Modeling. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Conceptual Data Model. Logical Data Model : It is an abstraction of CDM. Data Profiling.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly.

Big Data 52

Big Data Supply Chain Data Warehouse Data Analytics 52

Astera

DECEMBER 22, 2022

It’s one of the three core data types, along with structured and semi-structured formats. Examples of unstructured data include call logs, chat transcripts, contracts, and sensor data, as these datasets are not arranged according to a preset data model. This makes managing unstructured data difficult.

Data Quality 52

Data Quality Data Mining Data Warehouse Data Governance 52

Data Pine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis.

Business Intelligence 95

Business Intelligence Business Intelligence Data Visualization Visualization 95