Jet Global

JUNE 5, 2019

So how does a leading-edge business find a way to marry their wealth of data with the opportunity to utilize it effectively via BI software? Let’s introduce the concept of data mining. Toiling Away in the Data Mines. Clustering helps to group data and recognize differences and similarities.

Data mining 75

Data mining Business Intelligence OLAP Key Performance Indicator 75

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. To best understand how to do this, let’s dig into the challenges of big data and look at a wave of emerging issues. displaying BI insights for human users).

Big Data 52

Big Data IoT Data Warehouse Data-driven 52

Cloudera

MAY 15, 2019

He is a successful architect of healthcare data warehouses, clinical and business intelligence tools, big data ecosystems, and a health information exchange. The Enterprise Data Cloud – A Healthcare Perspective. The analytics and data platform is powering different data needs, use cases, and growth.

Enterprise 47

Enterprise Data Lake Data mining IoT 47

In(tegrate) the Clouds

OCTOBER 7, 2016

As the data visualization, big data, Hadoop, Spark and self-service hype gives way to IoT, AI and Machine Learning, I dug up an old parody post on the business intelligence market circa 2007-2009 when cloud analytics was just a disruptive idea. Ad hoc query, data mining, information I’m still not finding.

Business Intelligence 40

Business Intelligence OLAP Marketing Scorecard 40

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.

Data Warehouse 130

Data Warehouse Data Lake Visualization Big Data 130

Sisense

MAY 11, 2021

Here at Sisense, we think about this flow in five linear layers: Raw This is our data in its raw form within a data warehouse. We follow an ELT ( E xtract, L oad, T ransform) practice, as opposed to ETL, in which we opt to transform the data in the warehouse in the stages that follow.

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