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? Reverse ETL tools.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

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 Host , enter the Redshift Serverless endpoint’s host URL. For Port , enter 5349. This is optional.

Data Analytics 52

Data Analytics Data Warehouse Analytics Data Processing 52

AWS Big Data

JULY 20, 2023

The system ingests data from various sources such as cloud resources, cloud activity logs, and API access logs, and processes billions of messages, resulting in terabytes of data daily. This data is sent to Apache Kafka, which is hosted on Amazon Managed Streaming for Apache Kafka (Amazon MSK).

Data Lake 88

Data Lake Analytics Snapshot Optimization 88

Octopai

NOVEMBER 13, 2022

The modern data stack is a data management system built out of cloud-based data systems. A given modern data stack will usually include components for data ingestion from your data sources, data transformation, data storage, data analysis and reporting.

Enterprise 77

Enterprise Data Warehouse Reporting Metadata 77

FineReport

MARCH 31, 2024

In 2024, business intelligence (BI) software has undergone significant advancements, revolutionizing data management and decision-making processes. These tools empower organizations to glean valuable insights from their data, enhancing decision-making processes and bolstering competitiveness in data-driven markets.

Dashboards 52

Dashboards Visualization Data mining Data-driven 52

Jet Global

FEBRUARY 23, 2024

Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields. Data mapping is important for several reasons.

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