datapine
SEPTEMBER 29, 2022
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.
Cloudera
JULY 21, 2022
Co-author: Mike Godwin, Head of Marketing, Rill Data. Cloudera has partnered with Rill Data, an expert in metrics at any scale, as Cloudera’s preferred ISV partner to provide technical expertise and support services for Apache Druid customers. Deploying metrics shouldn’t be so hard. Cloudera Data Warehouse).
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AWS Big Data
NOVEMBER 9, 2023
In this post, we delve into a case study for a retail use case, exploring how the Data Build Tool (dbt) was used effectively within an AWS environment to build a high-performing, efficient, and modern data platform. It does this by helping teams handle the T in ETL (extract, transform, and load) processes.
AWS Big Data
MAY 25, 2023
Data transformation plays a pivotal role in providing the necessary data insights for businesses in any organization, small and large. To gain these insights, customers often perform ETL (extract, transform, and load) jobs from their source systems and output an enriched dataset.
DataKitchen
OCTOBER 5, 2021
Data operations (or data production) is a series of pipeline procedures that take raw data, progress through a series of processing and transformation steps, and output finished products in the form of dashboards, predictions, data warehouses or whatever the business requires. Their product is the data.
DataKitchen
JULY 27, 2023
Azure Synapse Analytics Pipelines: Azure Synapse Analytics (formerly SQL Data Warehouse) provides data exploration, data preparation, data management, and data warehousing capabilities. It provides data prep, management, and enterprise data warehousing tools. It does the job. So go ahead.
AWS Big Data
NOVEMBER 15, 2023
The difference lies in when and where data transformation takes place. In ETL, data is transformed before it’s loaded into the data warehouse. In ELT, raw data is loaded into the data warehouse first, then it’s transformed directly within the warehouse.
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