CIO Business Intelligence

NOVEMBER 14, 2022

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

Data mining 133

Data mining Data-driven Statistics OLAP 133

Sisense

APRIL 26, 2020

Read on to explore more about structured vs unstructured data, why the difference between structured and unstructured data matters, and how cloud data warehouses deal with them both. Structured vs unstructured data. However, both types of data play an important role in data analysis.

Unstructured Data 52

Unstructured Data Data Warehouse Structured Data Data mining 52

Sisense

MARCH 14, 2021

As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data. IoT sensors on factory floors are constantly streaming data into cloud warehouses and other storage locations.

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

FineReport

JUNE 24, 2021

Business intelligence can assist decision-making and operation optimization, either at the operational or tactical, or strategic levels. Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. Data preparation and data processing.

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

AWS Big Data

FEBRUARY 22, 2023

An AWS Glue ETL job, using the Apache Hudi connector, updates the S3 data lake hourly with incremental data. The AWS Glue job can transform the raw data in Amazon S3 to Parquet format, which is optimized for analytic queries. Data had to be manually processed by data analysts, and data mining took a long time.

Data Lake 52

Data Lake Dashboards Cost-Benefit Metadata 52

Sisense

MAY 11, 2021

We’re going to nerd out for a minute and dig into the evolving architecture of Sisense to illustrate some elements of the data modeling process: Historically, the data modeling process that Sisense recommended was to structure data mainly to support the BI and analytics capabilities/users.

Modeling 84

Modeling Big Data IoT Data Warehouse 84

Jet Global

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.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52