FineReport

JUNE 24, 2021

Technicals such as data warehouse, online analytical processing (OLAP) tools, and data mining are often binding. For example, the following dashboard globally presents core indicators that DAS Corporation cares about in daily management. Business intelligence solutions examples (by FineReport).

Business Intelligence 84

Business Intelligence OLAP Data mining Visualization 84

Jet Global

APRIL 20, 2020

This practice, together with powerful OLAP (online analytical processing) tools, grew into a body of practice that we call “business intelligence.” A few decades ago, technology professionals developed methods for collecting, aggregating, and staging their most important information into data warehouses.

Business Intelligence 78

Business Intelligence Finance Data Warehouse OLAP 78

AWS Big Data

AUGUST 1, 2024

This includes the expected response time limits for dashboard queries or analytical queries, elapsed runtime for daily ETL jobs, desired elapsed time for data sharing with consumers, total number of tenants with concurrency of loads and reports, and mission-critical reports for executives or factory operations.

Data Warehouse 113

Data Warehouse KPI Optimization Cost-Benefit 113

Jet Global

AUGUST 14, 2019

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions.

Enterprise 70

Enterprise OLAP Business Intelligence Cost-Benefit 70

AWS Big Data

MARCH 7, 2023

Consumption This pillar consists of various consumption channels for enterprise analytical needs. It includes business intelligence (BI) users, canned and interactive reports, dashboards, data science workloads, Internet of Things (IoT), web apps, and third-party data consumers.

Analytics 94

Analytics Data Warehouse Data Lake Metadata 94

Jet Global

SEPTEMBER 4, 2020

Data warehouses gained momentum back in the early 1990s as companies dealing with growing volumes of data were seeking ways to make analytics faster and more accessible. Online analytical processing (OLAP), which enabled users to quickly and easily view data along different dimensions, was coming of age.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

Jet Global

OCTOBER 22, 2020

Power BI provides users with some very nice dashboarding and reporting capabilities. Unfortunately, it also introduces a mountain of complexity into the reporting process. It updates a dedicated database against which you can perform reporting and analytics. Within the data warehouse paradigm, there are two divergent approaches.

Modeling 52

Modeling OLAP Reporting Sales 52

Jet Global

OCTOBER 2, 2019

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Sisense

FEBRUARY 16, 2021

Data warehouses provide a consolidated, multidimensional view of data along with online analytical processing ( OLAP ) tools. OLAP tools help in the interactive and effective processing of data in a multidimensional space. Live models run queries directly against the data source.

Modeling 52

Modeling OLAP Data Warehouse Cost-Benefit 52

Sisense

SEPTEMBER 3, 2019

First, we’ll dive into the two types of databases: OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing). For example, if you are using Redshift solely for analytics purposes, you can scale the cluster up with more nodes when this happens and resume work once it is complete.

Data Warehouse 78

Data Warehouse OLAP Statistics Cost-Benefit 78

IBM Big Data Hub

SEPTEMBER 25, 2023

As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries. It can ingest data from offline batch data sources (such as Hadoop and flat files) as well as online data sources (such as Kafka).

OLAP 86

OLAP Data Lake Data-driven Snapshot 86

Jet Global

MAY 4, 2021

As the first in-memory database for SAP, HANA was revolutionary, bringing together the best characteristics of both traditional online transaction processing and online analytical processing.

Reporting 52

Reporting IT Finance Data Warehouse 52