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

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 100

Analytics Data Warehouse Testing Dashboards 100

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

Sisense

FEBRUARY 16, 2021

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. Designers, engineers, and analysts see data in different ways.

Modeling 52

Modeling OLAP Data Warehouse Cost-Benefit 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

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