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ETL Pipeline with Google DataFlow and Apache Beam

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Processing large amounts of raw data from various sources requires appropriate tools and solutions for effective data integration. Building an ETL pipeline using Apache […].

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Good ETL Practices with Apache Airflow

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction to ETL ETL is a type of three-step data integration: Extraction, Transformation, Load are processing, used to combine data from multiple sources. It is commonly used to build Big Data.

Big Data 382
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Getting Started with Azure Synapse Analytics

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Azure Synapse Analytics is a cloud-based service that combines the capabilities of enterprise data warehousing, big data, data integration, data visualization and dashboarding.

Analytics 373
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From Blob Storage to SQL Database Using Azure Data Factory

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Azure data factory (ADF) is a cloud-based ETL (Extract, Transform, Load) tool and data integration service which allows you to create a data-driven workflow. In this article, I’ll show […].

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Artificial intelligence and machine learning adoption in European enterprise

O'Reilly on Data

Our survey showed that companies are beginning to build some of the foundational pieces needed to sustain ML and AI within their organizations: Solutions, including those for data governance, data lineage management, data integration and ETL, need to integrate with existing big data technologies used within companies.

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The quest for high-quality data

O'Reilly on Data

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI AI starts with ‘good’ data” is a statement that receives wide agreement from data scientists, analysts, and business owners. These data sets are often siloed, incomplete, and extremely sparse.

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Core technologies and tools for AI, big data, and cloud computing

O'Reilly on Data

Foundational data technologies. Machine learning and AI require data—specifically, labeled data for training models. Moving forward, tracking data provenance is going to be important for security, compliance, and for auditing and debugging ML systems. Data Platforms. Data Integration and Data Pipelines.

Big Data 213