azure-synapse-analytics 
Analytics Vidhya
JULY 29, 2022
Introduction We are all pretty much familiar with the common modern cloud data warehouse model, which essentially provides a platform comprising a data lake (based on a cloud storage account such as Azure Data Lake Storage Gen2) AND a data warehouse compute engine […].
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. Fault tolerance is built in. Any hardware failures are automatically replaced.
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DataKitchen
JULY 27, 2023
The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. You can use it for big data analytics and machine learning workloads.
CIO Business Intelligence
JUNE 29, 2022
Insights hidden in your data are essential for optimizing business operations, finetuning your customer experience, and developing new products — or new lines of business, like predictive maintenance. And as businesses contend with increasingly large amounts of data, the cloud is fast becoming the logical place where analytics work gets done.
Data Virtualization
JANUARY 19, 2023
Reading Time: 2 minutes Today, many businesses are modernizing their on-premises data warehouses or cloud-based data lakes using Microsoft Azure Synapse Analytics. Unfortunately, with data spread.
Cloudera
JANUARY 15, 2021
Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. The results demonstrate superior price performance of Cloudera Data Warehouse on the full set of 99 queries from the TPC-DS benchmark. Introduction.
AWS Big Data
OCTOBER 17, 2023
With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex. You have to migrate terabytes or petabytes of data from your legacy system while not disrupting your production workload.
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