FineReport

APRIL 21, 2024

From addressing implementation challenges to conducting a comparative analysis of leading options, we delve into how embedded BI tools empower organizations to make informed decisions and drive business intelligence initiatives with unprecedented efficiency and precision. What Are Embedded BI Tools?

Dashboards 52

Dashboards Visualization Interactive Business Intelligence 52

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Extract, load, Transform (ELT) tools. Data ingestion/integration services. Data orchestration tools. Business intelligence (BI) platforms. How Did the Modern Data Stack Get Started? How Can I Build a Modern Data Stack?

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

CIO Business Intelligence

JUNE 29, 2022

If you’re used to using SQL Server Analysis Services for business intelligence, Analysis Services offers that enterprise-grade analytics engine as a cloud service that you can also connect to Power BI. Azure Data Factory. The reason Azure has so many analytics services is so you can build your entire stack there. Microsoft.

Data Lake 116

Data Lake Analytics Data Warehouse Machine Learning 116

CIO Business Intelligence

DECEMBER 6, 2023

Airbus was conceiving an ambitious plan to develop an open aviation data platform, Skywise, as a single platform of reference for all major aviation players that would enable them to improve their operational performance and business results and support Airbus’ own digital transformation.

Digital Transformation 116

Digital Transformation Software Cost-Benefit Manufacturing 116

AWS Big Data

NOVEMBER 13, 2023

Gameskraft used Amazon Redshift workload management (WLM) to manage priorities within workloads, with higher priority being assigned to the extract, transform, and load (ETL) queue that runs critical jobs for data producers. The same AWS Glue database is referenced in the external schema on the consumer side.

Data Warehouse 94

Data Warehouse Data Lake Analytics Data Science 94

AWS Big Data

JULY 25, 2023

Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver fast performance for even the most demanding and unpredictable workloads, and you pay only for what you use. For more information on how to connect to a database, refer to tDBConnection.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

AWS Big Data

APRIL 27, 2023

With these features, you can now build data pipelines completely in standard SQL that are serverless, more simple to build, and able to operate at scale. Typically, data transformation processes are used to perform this operation, and a final consistent view is stored in an S3 bucket or folder.

Data Lake 75

Data Lake Snapshot Optimization Data Transformation 75

AWS Big Data

FEBRUARY 8, 2023

Amazon Redshift enables you to run complex SQL analytics at scale and performance on terabytes to petabytes of structured and unstructured data, and make the insights widely available through popular business intelligence (BI) and analytics tools.

Data Quality 78

Data Quality Testing Data Warehouse Unstructured Data 78

Sisense

MAY 11, 2021

The right data model + artificial intelligence = augmented analytics. However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts. Dig into AI. One solution with immense potential is ”edge computing.”

Modeling 84

Modeling Big Data IoT Data Warehouse 84

AWS Big Data

NOVEMBER 29, 2023

dbt is an open source, SQL-first templating engine that allows you to write repeatable and extensible data transforms in Python and SQL. dbt is predominantly used by data warehouses (such as Amazon Redshift ) customers who are looking to keep their data transform logic separate from storage and engine.

Data Lake 106

Data Lake Management Metrics Data Warehouse 106

AWS Big Data

AUGUST 1, 2023

Although Jira Cloud provides reporting capability, loading this data into a data lake will facilitate enrichment with other business data, as well as support the use of business intelligence (BI) tools and artificial intelligence (AI) and machine learning (ML) applications. Choose Update.

Data Lake 87

Data Lake Data Transformation Cost-Benefit Data-driven 87

Smarten

JANUARY 4, 2018

Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting.

Data Warehouse 40

Data Warehouse OLAP Data Governance Optimization 40

CIO Business Intelligence

APRIL 30, 2024

Einstein Copilot for Tableau remains in beta, but Tableau announced two new features for the AI assistant as well: AI-assisted data transformation. This feature can automate a data transformation pipeline with step-by-step suggestions for preparing data for analysis.

Analytics 96

Analytics Metrics Visualization Dashboards 96

Jet Global

MAY 28, 2019

Unlike a database, a data warehouse’s architecture is built for getting the data out, and not just through technical expertise, but for common users like management, executives, finance professionals, and other staff. A data warehouse is typically used by companies with a high level of data diversity or analytical requirements.

Data Warehouse 85

Data Warehouse Reporting Business Intelligence Sales 85

CIO Business Intelligence

JANUARY 24, 2024

Gather/Insert data on market trends, customer behavior, inventory levels, or operational efficiency. IoT, Web Scraping, API, IDP, RPA Data Processing Data Pipelines and Analysis Layer Employ data pipelines with algorithms to filter, sort, and interpret data, transforming raw information into actionable insights.

IoT 118

IoT Machine Learning Optimization Internet of Things 118

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. 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

Jet Global

FEBRUARY 23, 2024

This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making. Why Data Mapping is Important Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Jet Global

MAY 1, 2023

We hope this guide will transform how you build value for your products with embedded analytics. Learn how embedded analytics are different from traditional business intelligence and what analytics users expect. that gathers data from many sources. Data Transformation and Enrichment Data can be enriched for analysis.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52