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Category: cloud

  • Detailed Airflow Task Types

    Detailed Airflow Task Types Detailed Airflow Task Types for Orchestration Airflow’s strength lies in its ability to orchestrate a wide variety of tasks through its rich set of operators. Operators represent a single task in a workflow. Here are some key categories and examples: Core Task Concepts At its heart, an Airflow task is an Read more

  • How Flink and Airflow Work Together

    Detailed Integration of Flink and Airflow Detailed Integration of Apache Flink and Apache Airflow The synergy between Apache Flink and Apache Airflow creates robust and scalable data processing pipelines. Airflow orchestrates the overall workflow, while Flink handles the computationally intensive data transformations. Let’s explore the integration patterns and considerations in more detail. The Complementary Roles Read more

  • Top Must-Know Apache Airflow Internals

    Top Must-Know Apache Airflow Internals Top Must-Know Apache Airflow Internals Understanding the core components and how they interact is crucial for effectively using and troubleshooting Apache Airflow. Here are the top must-know internals: 1. DAG (Directed Acyclic Graph) Parsing Concept: Airflow continuously (by default, every `min_file_process_interval` seconds) parses Python files in the `dags_folder` to identify Read more

  • Top 50 Design Patterns for Enterprise-Scale Applications

    Top 50 Design Patterns for Enterprise-Scale Applications Building robust, scalable, and maintainable enterprise-scale applications requires careful architectural considerations and the strategic application of design patterns. Here are 30 important design patterns categorized for better understanding, along with details and relevant links: 1. Microservices Details: An architectural style that structures an application as a collection of Read more

  • Processing Data Lakehouse Data for Machine Learning

    Processing Data Lakehouse Data for Machine Learning Processing Data Lakehouse Data for Machine Learning Leveraging the vast amounts of data stored in a data lakehouse for Machine Learning (ML) requires a structured approach to ensure data quality, relevance, and efficient processing. Here are the key steps involved: 1. Data Discovery and Selection Details: The initial Read more

  • Processing Data Lakehouse Data for Agentic AI

    Processing Data Lakehouse Data for Agentic AI Processing Data Lakehouse Data for Agentic AI Agentic AI, characterized by its autonomy, goal-directed behavior, and ability to interact with its environment, relies heavily on data for learning, reasoning, and decision-making. Processing data from a data lakehouse for such AI agents requires careful consideration of data quality, relevance, Read more

  • Building an Azure Data Lakehouse from Ground Zero

    Building an Azure Data Lakehouse from Ground Zero Building an Azure Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on Azure involves leveraging Azure Data Lake Storage Gen2 (ADLS Gen2) as the storage foundation, along with services like Azure Synapse Analytics, Azure Databricks, and Azure Data Factory for data processing and querying. Read more

  • Building a GCP Data Lakehouse from Ground Zero

    Building a GCP Data Lakehouse from Ground Zero Building a GCP Data Lakehouse from Ground Zero: Detailed Steps Building a data lakehouse on Google Cloud Platform (GCP) involves leveraging services like Google Cloud Storage (GCS), BigQuery, Dataproc, and potentially Looker. Here are the detailed steps to build one from the ground up: Step 1: Set Read more

  • Integrating with Azure Data Lakehouse: Real-Time and Batch

    Integrating with Azure Data Lakehouse: Real-Time and Batch Integrating with Azure Data Lakehouse: Real-Time and Batch Azure provides a comprehensive set of services to build a data lakehouse, primarily leveraging Azure Data Lake Storage Gen2 (ADLS Gen2) as the foundation, along with services for real-time and batch data integration and processing. Real-Time (Streaming) Integration Real-time Read more

  • Integrating with Google BigQuery: Real-Time and Batch mode

    Integrating with Google BigQuery: Real-Time and Batch Integrating with Google BigQuery: Real-Time and Batch Google BigQuery offers various methods for integrating data in both real-time (streaming) and batch modes, catering to different data ingestion needs. Real-Time (Streaming) Integration Real-time integration focuses on ingesting data as it is generated, making it available for near immediate analysis. Read more