Sisense

NOVEMBER 12, 2020

Data warehouse vs. databases Traditional vs. Cloud Explained Cloud data warehouses in your data stack A data-driven future powered by the cloud. We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. And where does all this data live?

Data Warehouse 52

Data Warehouse Data Lake OLAP Data-driven 52

Jet Global

DECEMBER 3, 2021

The term “ business intelligence ” (BI) has been in common use for several decades now, referring initially to the OLAP systems that drew largely upon pre-processed information stored in data warehouses. Discover Meaning Amid All That Data. Sales and customer service interactions are tracked in CRM.

Business Intelligence 52

Business Intelligence Sales OLAP Data Warehouse 52

AWS Big Data

MARCH 20, 2023

We discuss how to create such a solution using Amazon Kinesis Data Streams , Amazon Managed Streaming for Kafka (Amazon MSK), Amazon Kinesis Data Analytics for Apache Flink ; the design decisions that went into the architecture; and the observed business benefits by Poshmark.

Analytics 52

Analytics Slice and Dice Data Processing Data Lake 52

AWS Big Data

OCTOBER 18, 2023

Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that provides the flexibility to use provisioned or serverless compute for your analytical workloads. Modern analytics is much wider than SQL-based data warehousing. You can isolate workloads using data sharing, while using the same underlying datasets.

Analytics 98

Analytics Data Warehouse Testing Dashboards 98

erwin

AUGUST 15, 2022

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. A sample data warehousing project.

Data Lake 52

Data Lake Modeling Unstructured Data Data Warehouse 52

erwin

JUNE 7, 2022

Designing databases for data warehouses or data marts is intrinsically much different than designing for traditional OLTP systems. Accordingly, data modelers must embrace some new tricks when designing data warehouses and data marts. Data modeling for the cloud: good database design means “right size” and savings.

Data Warehouse 52

Data Warehouse Modeling Sales Data Lake 52

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Jet Global

JULY 29, 2020

For users of Oracle E-Business Suite (EBS), data access is about to get a bit more difficult now that the company has phased out the Oracle Discoverer product. While it has many advantages, it’s not built to be a transactional reporting tool for day-to-day ad hoc analysis or easy drilling into data details. But does OBIEE stack up?

Reporting 98

Reporting Cost-Benefit Consulting Finance 98

Jet Global

MAY 1, 2023

By leveraging data analysis to solve high-value business problems, they will become more efficient. This is in contrast to traditional BI, which extracts insight from data outside of the app. that gathers data from many sources. These tools prep that data for analysis and then provide reporting on it from a central viewpoint.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52