Remove Data Lakes Remove ETL Remove Tableau
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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

It supports a holistic data model, allowing for rapid prototyping of various models. It also supports a wide range of data warehouses, analytical databases, data lakes, frontends, and pipelines/ETL. Data Lakes : It supports MS Azure Blob Storage. pipelines, Azure Data Bricks.

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Unlock the value of your Azure data with Tableau

Tableau

In Tableau 2021.1, we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere.

Azure 102
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Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. It allows data engineers to define and manage complex workflows as directed acyclic graphs (DAGs).

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CI/CD für Datenpipelines – Ein Game-Changer mit AnalyticsCreator

Data Science Blog

Data Lakes: Unterstützt MS Azure Blob Storage. Frontends : Kompatibel mit Tools wie Power BI, Qlik Sense und Tableau. Pipelines/ETL : Unterstützt Technologien wie SQL Server Integration Services und Azure Data Factory.

Azure 130
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Unlock the value of your Azure data with Tableau

Tableau

In Tableau 2021.1, we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere.

Azure 52
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Navigating the Big Data Frontier: A Guide to Efficient Handling

Women in Big Data

A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake. Once ingested, the data is prepared through filtering, error correction, and restructuring for ease of use.

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Understanding Business Intelligence Architecture: Key Components

Pickl AI

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. This can involve: Data Warehouses: These are optimized for query performance and reporting.