tell application "Finder"
display dialog "Hello World"
end tell
Here's a streamlined guide with highlighted key points for each major attraction in Mathura, focusing on the essential aspects and must-do activities, particularly from a Hindu cultural perspective:
- Essentials: Birthplace of Lord Krishna, spiritual ceremonies.
- Must-Do: Participate in the evening aarti; it's a profound devotional experience.
- Essentials: Rich architecture, vibrant festivities.
- Must-Do: Attend the festival celebrations during Janmashtami for a lively and colorful experience.
Certainly! Below is a README file you can use to guide yourself or others on setting up a Seafile Docker instance with data storage on an external drive (in this case, a Seagate external drive, which we'll refer to as ExtDrive
). This document will include all necessary steps to ensure that Seafile uses this external drive for storing files.
This README provides detailed instructions on how to set up Seafile using Docker, specifically configured to store data on an external Seagate drive named ExtDrive
. Follow these steps to ensure your data is stored securely and externally from your main system.
Watermarks and Allowed Lateness are both vital techniques in managing late data in stream processing systems, but they serve slightly different purposes and are often used in conjunction to maximize data integrity and processing efficiency. Here’s an in-depth look at when and why you might choose to use each technique, or both together, along with real-world industry examples.
Purpose: Watermarks are primarily used to handle out-of-order data. They provide a way to estimate the "completeness" of data up to a certain point in time, based on event timestamps.
When to Use: Use watermarks when:
- You expect data to arrive out of order.
- You need a mechanism to know when to close a window and process its data.
Here's the table sorted chronologically based on the release date of each Google Cloud service:
Google Cloud Service | Release Date | Based on/Open-source Inspiration | Open-source Start Date | Notes |
---|---|---|---|---|
Google BigQuery | 2010 | Dremel (Internal Google Tech) | N/A | BigQuery is inspired by Dremel but is not directly based on open-source technology. |
Google Cloud Dataflow | 2014 | Apache Beam | 2016 (as Apache Beam) | Initially developed by Google as Google Dataflow, then donated to the Apache Software Foundation as Apache Beam. |
Google Cloud Composer | 2018 | Apache Airflow | 2015 | Developed by Airbnb and later open-sourced as Apache Airflow, which Google adopted for Cloud Composer. |
Google Data Fusion | 2019 | CDAP (Cask Data Application Platform) | 2011 |
Framework Step | Details |
---|---|
Situation |
|
Task | The urgent task was to stabilize and scale the bank’s data processing capabilities to not only retain the e-commerce client but also to set a foundation for scalable, compliant growth suitable for high-volume transaction environments. |
- Data processing frameworks
- Batch and real-time streaming analytics
- SQL versus NoSQL use cases and use case patterns
- Enterprise data governance and metadata management
Category | Tools |
---|