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how to create a worksheet in snowflake and rename sheet

    Creating a worksheet in the Snowflake GUI (Graphical User Interface) is a straightforward process that allows you to run SQL queries and interact with your data. 

Here's how you can create a worksheet and rename it:

Creating a Worksheet: YouTube Link

Login to Snowflake:
Access the Snowflake web interface by logging into your Snowflake account.

Navigate to Worksheets:
    In the left sidebar, you'll find a menu. Click on "Worksheet" under the "SQL" section.

Open a New Worksheet:
        Once you're in the "Worksheet" section, you can see a list of existing worksheets if any. To create a new worksheet, click the "New Worksheet" button.

Write and Execute SQL:

    In the newly opened worksheet, you'll find an SQL editor where you can write your SQL queries. Write your query and click the "Run" button (usually represented by a play icon) to execute the query.

Renaming a Worksheet:

Worksheet Tabs:

    In the top area of the worksheet interface, you'll see tabs representing each open worksheet. The default name for a new worksheet is often "Worksheet 1" or similar.

Right-Click the Tab:

    To rename the worksheet, right-click on the tab of the worksheet you want to rename. This action will typically open a context menu.

Enter New Name:
    
        After selecting "Rename," the tab name will become editable. Enter the desired name for the worksheet.

Confirm the Name:

        Press "Enter" or click outside the renamed area to confirm the new name for the worksheet. The tab should now display the new name you entered.

Note: Keep in mind that specific features and layout might vary slightly depending on updates and the version of the Snowflake user interface you are using. The steps provided should be generally applicable to most versions of Snowflake's web interface.


Remember that worksheets are useful for running queries and interacting with your data, but they are typically not meant for long-term storage of queries or results. For more organized storage and sharing of SQL scripts and results, consider using Snowflake's scripts, notebooks, or other relevant features based on your use case.


SQL Essentials: Mastering Data Querying in Snowflake

Introduction:

        Structured Query Language (SQL) is the foundation of data querying and manipulation. In Snowflake, the cloud data platform, SQL serves as a powerful tool to extract valuable insights from your data. In this blog post, we'll delve into the SQL basics you need to know for data querying in Snowflake. Whether you're a beginner or an experienced data professional, understanding these essentials will empower you to unlock the true potential of your data and make informed decisions for your organization.


1. SELECT Statement:

        The SELECT statement is the core of SQL querying and allows you to retrieve data from one or more tables. To use it effectively:

- Specify the columns you want to retrieve data from using the SELECT keyword.
- Define the source table using the FROM keyword.
- Apply optional filtering conditions using the WHERE clause to narrow down your results.

2. Filtering Data with WHERE Clause:
    
        The WHERE clause filters data based on specified conditions. Use comparison operators (e.g., =, <>, >, <, >=, <=) and logical operators (e.g., AND, OR, NOT) to create powerful filters. For example:

Mastering Data Mobility: Best Practices for Efficient Data Loading and Unloading in Snowflake

Introduction:


        Efficient data loading and unloading are crucial aspects of any successful data management strategy. Snowflake, the cloud-native data platform, provides powerful capabilities for handling data with ease. In this blog post, we'll delve into the best practices for maximizing efficiency when loading data into Snowflake and unloading it for analysis and sharing. By following these guidelines, you can streamline your data workflows and drive better insights for your organization.


1. Data Loading Best Practices:


a. Optimize Staging Area:

  • Before loading data into Snowflake, consider using a staging area in cloud storage (e.g., Amazon S3) for your data files. This allows you to preprocess, validate, or compress data before loading it into Snowflake, reducing the load time and minimizing data transfer costs.


b. Choose the Right File Format:

  • Select the appropriate file format for your data to ensure optimal loading performance and efficient storage utilization. For structured data, CSV is a popular choice, while Parquet and Avro are more suitable for semi-structured and columnar data, respectively.


c. Use Bulk Loading:

  • For large datasets, leverage Snowflake's bulk loading options, such as the COPY command, which efficiently loads data from the staging area into Snowflake. Bulk loading significantly reduces loading times and resource consumption.


d. Data Skew Management:

  •  Be mindful of data skew when designing your data loading process. Data skew occurs when a single file or partition contains significantly more data than others, leading to imbalanced workloads. Distributing data evenly can enhance loading performance and query execution.


e. Utilize Load Cloning:

  • When dealing with concurrent data loading, take advantage of Snowflake's load cloning feature. It allows you to clone the structure and metadata of an existing table and load new data into the clone without affecting the original table. This ensures uninterrupted access to data during loading processes.


2. Data Unloading Best Practices:


a. Choose Appropriate Data Format:

  • Select the data format that best aligns with your data sharing and analysis needs. For collaboration with external parties, consider using widely supported formats like CSV. For big data processing, Parquet might be a more efficient choice.


b. Use Compression Techniques:

  • Apply data compression to reduce file sizes during data unloading. This not only optimizes storage costs but also improves data transfer speeds when sharing or archiving data.


c. Avoid Unnecessary Data:

  • Only unload the data that is required for analysis or sharing. Removing irrelevant data reduces the size of data exports, leading to faster data transfer and improved data clarity for stakeholders.


d. Keep Data Secure:

  • Ensure that sensitive data is adequately masked or encrypted before unloading, especially when sharing data externally. Implementing proper data security measures safeguards sensitive information from unauthorized access.


Conclusion:


        Efficient data loading and unloading are vital elements in the success of any data-driven organization. By following these best practices in Snowflake, you can optimize your data workflows, accelerate data processing, and enable seamless data sharing and collaboration with stakeholders.

        As you master the art of data mobility in Snowflake, continue exploring its extensive features to unlock the full potential of your data and drive actionable insights for your business.


[Closing Call-to-Action]


        Ready to enhance your data mobility and drive data insights like never before? Start your data journey with Snowflake today and experience the transformative power of efficient data loading and unloading. Stay tuned to our blog for more tips, tutorials, and best practices to master Snowflake's potential.


Happy Snowflaking!

Empowering Data Mobility: Unleashing the Power of Unloading Data from Snowflake

Introduction:

        In the world of data-driven decision-making, having the ability to extract and share data efficiently is critical. Snowflake, the cloud data platform, excels not only in data loading but also in unloading data for analysis and sharing with external stakeholders. In this blog post, we'll explore the powerful capabilities of unloading data from Snowflake and how it empowers organizations to drive insights and collaborate seamlessly.


1. Understanding Data Unloading in Snowflake:

        Unloading data from Snowflake refers to the process of extracting data from Snowflake tables and saving it into external storage systems, such as cloud storage (e.g., Amazon S3) or on-premises storage. This data mobility allows you to export data in various formats, making it accessible for analysis by data scientists, sharing with partners, or archiving for compliance purposes.


2. Choosing the Right Format:

    Snowflake offers a range of formats for unloading data, allowing you to choose the one that best suits your requirements:


  • CSV (Comma-Separated Values):
    •   CSV is a widely used format for simple tabular data. It's human-readable and compatible with various data analysis tools.


  • Parquet:
    • Parquet is a columnar storage format that offers highly efficient data compression and is optimized for big data processing.


  • JSON (JavaScript Object Notation):
    • JSON is ideal for semi-structured data and provides flexibility in representing nested and complex data structures.


  • Avro:
    • Avro is a compact data format suitable for data serialization, making it efficient for data exchange between systems.


3. Unloading Data with Snowflake:

To unload data from Snowflake, follow these steps:


  • Specify the Unload Location:
    • Choose the external storage location where you want to unload the data. It can be cloud-based storage like Amazon S3 or a network share on-premises.

  • Specify the File Format and Options:
    • Define the file format and options for the unloaded data, such as compression type, field delimiter, and character encoding.

  • Run the UNLOAD Command:
    • Use the UNLOAD command in Snowflake, specifying the source table, target storage location, and other required parameters.


4. Efficient Data Sharing:

        Unloading data from Snowflake facilitates seamless data sharing with external stakeholders:


  • Sharing with Partners and Customers:
    • Unloading data allows you to share specific datasets securely with partners, customers, or clients without giving them direct access to your Snowflake environment.


  • Collaboration with Data Scientists:
    • Data scientists often prefer working with data in their own analysis environments. Unloading data into formats like CSV or Parquet enables them to analyze the data using their preferred tools and methodologies.


  • Archiving and Compliance:
    • Unloading data to external storage facilitates data archiving for compliance and regulatory requirements, ensuring historical data is readily accessible.


Conclusion:


        Unloading data from Snowflake for analysis and sharing is a fundamental capability that empowers organizations to unleash the true potential of their data. By choosing the right format and following the simple steps to unload data, you can effortlessly make your data available for analysis, collaborate with external stakeholders, and ensure compliance with data retention policies.

        As you continue your data journey with Snowflake, keep exploring its diverse features to optimize data mobility and drive informed decision-making for your organization.


[Closing Call-to-Action]

        Ready to experience the data mobility power of Snowflake? Start your data journey with Snowflake today and discover the true potential of your data. Stay tuned to our blog for more tips, tutorials, and best practices to master Snowflake's potential.


Happy Snowflaking!

Seamless Data Loading: A Step-by-Step Guide to Effortlessly Load Data into Snowflake

Introduction:

        Loading data into a data platform is a crucial first step in the journey of data analytics. With Snowflake, the cloud-native data platform, data loading becomes a breeze, enabling organizations to efficiently ingest, process, and analyze massive volumes of data. In this blog post, we'll walk you through a step-by-step guide on how to load your data into Snowflake effortlessly, so you can unlock valuable insights and make data-driven decisions with ease.


Step 1: Prepare Your Data:

        Before loading data into Snowflake, it's essential to prepare your data in the required format. Snowflake supports various data formats, such as CSV, JSON, Parquet, and Avro. Ensure that your data is structured appropriately and that any source-specific data transformations are applied before proceeding.


Step 2: Create a Snowflake Table:

        To load data, you need to create a target table in Snowflake that will hold your data. This involves defining the table's schema, specifying column names, data types, and any relevant constraints. You can create tables using SQL queries in the Snowflake worksheet or through the Snowflake user interface.


Step 3: Choose the Data Loading Method:

        Snowflake offers multiple data loading methods to suit your specific needs:


  • Snowflake Data Loading Services (Snowpipe):
    • Snowpipe is a continuous data ingestion service that automatically loads data from external storage (e.g., Amazon S3) into Snowflake as soon as new data arrives. It eliminates the need for manual data loading and enables real-time data analytics.


  • Bulk Data Loading:
    • For larger datasets, Snowflake provides bulk data loading options, such as the COPY command, which efficiently loads data from staging areas in cloud storage into your Snowflake table.


  • INSERT and MERGE Statements:
    • For smaller datasets or incremental updates, you can use standard SQL INSERT and MERGE statements to load or update data in your Snowflake table.


Step 4: Load Data into Snowflake:

Depending on your chosen data loading method, follow these steps:


  • Using Snowpipe:
    • Set up Snowpipe by creating a stage that points to your external storage location. Snowpipe automatically detects new data and loads it into your target table.


  • Bulk Data Loading with COPY Command:
    • Prepare your data in a staging area in cloud storage (e.g., Amazon S3 or Azure Blob Storage). Then, use the COPY command to load data from the staging area into your Snowflake table.


  • Using INSERT and MERGE Statements:
    • Write SQL INSERT or MERGE statements to load or update data directly into your Snowflake table from your source data.


  • Step 5: Monitor Data Loading Progress:
    • After initiating the data loading process, monitor the progress to ensure data is being loaded correctly and efficiently. Snowflake provides query history and execution details to help you track the status of your data loading operations.


Conclusion:

        With Snowflake's efficient data loading capabilities, ingesting and processing data has never been easier. By following this step-by-step guide, you can seamlessly load your data into Snowflake and set the stage for powerful data analytics and business insights.

        As you embark on your data journey with Snowflake, keep exploring its features and functionalities to unleash the full potential of your data and drive informed decision-making for your organization.


[Closing Call-to-Action]

        Ready to experience the seamless data loading power of Snowflake? Start your data journey with Snowflake today and discover the true potential of your data. Stay tuned to our blog for more tips, tutorials, and best practices to master Snowflake's potential.


Happy Snowflaking!

Getting Started with Snowflake: Setting Up and Navigating the User Interface

Introduction:


    Snowflake, the cloud-native data platform, has become a go-to choice for organizations looking to harness the power of data analytics. To embark on your Snowflake journey, it's essential to understand how to set up your Snowflake account and navigate its user interface effectively. In this blog post, we'll guide you through the process of getting started with Snowflake, so you can make the most of this powerful platform from day one.


1. Creating Your Snowflake Account:


The first step towards using Snowflake is setting up your account. Here's how to do it:


  • Visit the Snowflake website and sign up for a trial account or reach out to a Snowflake sales representative to get started.
  • Provide the necessary information, such as your email address, company name, and contact details.
  • Once your account is created, you'll receive login credentials and a unique URL to access the Snowflake web interface.

Watch video how to create Snowflake trial account: Link

2. Navigating the Snowflake User Interface:


        Upon logging in to your Snowflake account, you'll be greeted with a user-friendly interface designed for ease of use and efficiency. Here's an overview of the key elements of the Snowflake user interface:


  • Home Page:
    •   The home page is your central dashboard, providing an overview of your account's usage, storage, and recent activity. It offers quick access to essential features and resources.


  • Database Explorer:
    • The database explorer is where you manage your databases, schemas, and tables. You can create databases, define schemas, and upload or create tables to store your data.


  • Worksheet:
    • The worksheet is where you write and execute SQL queries to interact with your data. It's a powerful SQL editor that supports syntax highlighting, code suggestions, and query history.


  • Virtual Warehouses:
    • Virtual warehouses are computing resources used to execute queries in Snowflake. In the user interface, you can start, stop, and configure warehouses based on your performance needs.


  • Query History:
    • The query history section displays the history of executed queries, allowing you to review past queries and their results.


  • Account Usage:
    • The account usage section provides insights into your Snowflake account's resource consumption, enabling you to monitor usage and manage costs efficiently.

Watch video how to create Snowflake trial account: Link

3. Key Features and Navigation Tips:


  • Search Functionality:
    • The Snowflake user interface includes a search bar that allows you to quickly find databases, tables, or other objects in your account. This feature is especially helpful as your data grows.


  • User-Friendly Menus:
    • Snowflake organizes its features and functions through user-friendly drop-down menus and icons. This design ensures easy navigation and accessibility for all users.


  • Help and Documentation:
    • Snowflake offers extensive documentation and guides within the user interface. If you're unsure about any feature or need assistance, the built-in help resources are readily available.

Watch video how to create Snowflake trial account: Link

Conclusion:


        Setting up your Snowflake account and navigating the user interface is the first step on your journey to data-driven insights. Snowflake's intuitive design and powerful features make it a pleasure to work with, whether you're managing databases, writing SQL queries, or monitoring account usage.


        Now that you've learned how to get started with Snowflake, you're ready to dive into the exciting world of data analytics. Continue exploring Snowflake's capabilities and uncover the full potential of your data to drive informed decision-making and business growth.


[Closing Call-to-Action]

        Ready to get started with Snowflake? Sign up for a trial account and begin your data journey with Snowflake today. Stay tuned to our blog for more tips, tutorials, and best practices to master Snowflake's potential.


Happy Snowflaking!

Unleashing the Power of Data: The Unmatched Benefits of Embracing Snowflake

Introduction:


        In today's data-centric world, having a reliable and efficient data platform is paramount to unlocking the full potential of your data. Snowflake, the leading cloud data platform, has been garnering immense popularity for its unique set of benefits that cater to businesses of all sizes and industries. In this blog post, we'll explore the unparalleled advantages of using Snowflake for your data needs and how it can revolutionize your data management and analytics workflows.


1. Infinite Scalability and Flexibility:


        Snowflake's cloud-native architecture offers infinite scalability, allowing you to effortlessly handle data of any size and complexity. Whether you're dealing with gigabytes or petabytes of data, Snowflake's elastic architecture scales computing resources on-demand, ensuring that your data platform grows seamlessly with your business needs. Say goodbye to hardware limitations and resource constraints; Snowflake's flexibility empowers you to focus on driving insights instead of worrying about infrastructure.


2. Elastic Computing Power:


        Traditional data warehouses often struggle to handle fluctuating workloads efficiently, leading to performance bottlenecks and increased costs. Snowflake's elastic compute power solves this problem by dynamically allocating resources based on demand. This means you pay for what you use, optimizing cost-effectiveness while ensuring high-performance data processing during peak times. The ability to auto-scale resources allows your data teams to focus on analysis, knowing that Snowflake will handle the heavy lifting.


3. Instant Data Cloning and DevOps Efficiency:


        Snowflake's zero-copy cloning feature is a game-changer for data management and development workflows. Creating clones of your data is instantaneous and doesn't consume additional storage, enabling data scientists and developers to work in parallel without affecting production data. DevOps teams can iterate faster, create multiple environments for testing and development, and accelerate innovation without the complexities of data replication.


4. Seamless Data Sharing and Collaboration:


        Data collaboration is essential for modern businesses, and Snowflake excels in this domain. Snowflake's secure data sharing capabilities make it incredibly easy to share data with internal teams, partners, suppliers, and customers. Data sharing is done without the need for data movement, ensuring data integrity and compliance. With Snowflake, you can break down data silos and foster a data-driven culture of collaboration and insights sharing.


5. Advanced Query Performance:


        Snowflake's architecture and query optimization techniques deliver exceptional query performance, even with complex analytical workloads. Parallel processing capabilities ensure that queries are executed in the most efficient manner, providing real-time insights for data-driven decision-making. Whether it's ad-hoc queries or large-scale data analytics, Snowflake's superior performance empowers your teams to be more productive and agile.


6. Zero Maintenance Overhead:


        Managing and maintaining traditional data warehouses can be a time-consuming and resource-intensive task. With Snowflake, all the complexities of infrastructure management, software upgrades, and tuning are handled by the platform itself. Snowflake's fully managed service allows your data teams to focus on extracting value from data, enabling them to make strategic business decisions based on insights.


7. Unparalleled Data Security:


        Data security is a top priority for businesses, especially in the age of increasing cyber threats and data breaches. Snowflake ensures robust data security with features like fine-grained access controls, end-to-end data encryption, and compliance with various industry standards. Your data is protected both at rest and in transit, giving you peace of mind that your most valuable asset is secure.


Conclusion:


        In conclusion, Snowflake stands out as a clear leader in the realm of cloud data platforms, offering unmatched benefits that empower businesses to harness the full potential of their data. From infinite scalability and elastic computing power to seamless data sharing and top-notch security, Snowflake's comprehensive features cater to all aspects of modern data management and analytics.


        By embracing Snowflake for your data needs, you're not just adopting a powerful data platform; you're investing in a transformative solution that will revolutionize your data workflows and drive your organization's success in a data-driven world.


[Closing Call-to-Action]

        Ready to experience the unparalleled benefits of Snowflake firsthand? Start your data journey with Snowflake today and discover the true potential of your data. Subscribe to our blog to stay updated with the latest insights and best practices to make the most of Snowflake's capabilities.


Happy Snowflaking!

BI Developer

Mastering Snowflake: A Comprehensive Cloud Data Platform Course

Introduction:

        In this blog post, we'll embark on an exciting journey to explore Snowflake, the game-changing cloud data platform that's revolutionizing the way businesses manage and analyze their data. Whether you're a data enthusiast, a budding analyst, or a seasoned professional, this comprehensive Snowflake learning course will empower you to unlock the true potential of your data in the cloud.


Module 1: Getting Started with Snowflake

1.1 What is Snowflake and why is it so popular?

1.2 The benefits of using Snowflake for your data needs.

1.3 Setting up your Snowflake account and navigating the user interface.


Module 2: Data Loading and Unloading Made Easy

2.1 Learn how to load your data into Snowflake effortlessly.

2.2 Unload data from Snowflake for analysis and sharing.

2.3 Best practices for efficient data loading and unloading.


Module 3: Mastering SQL in Snowflake

3.1 SQL basics you need to know for data querying in Snowflake.

3.2 Advanced querying techniques to extract valuable insights.

3.3 Understanding Snowflake functions and operators for powerful analytics.


Module 4: Ensuring Security and Access Control


4.1 Managing users and roles in Snowflake.

4.2 Implementing security policies to safeguard your data.

4.3 Data encryption measures for comprehensive protection.


Module 5: Snowflake Data Warehousing Strategies


Exploring the concept of data warehousing in Snowflake.

Designing and creating efficient Snowflake data warehouses.

Scaling and monitoring your warehouses for optimal performance.


Module 6: Unlocking Snowflake Performance Optimization


Tips and tricks to tune query performance for faster results.

How Snowflake's query optimizer works its magic.

Leveraging caching and materialized views for improved efficiency.


Module 7: Integrating Snowflake Data Pipelines


Overview of Snowflake data pipelines for seamless data flow.

Integrating Snowflake with popular ETL/ELT tools.

Real-time data streaming into Snowflake for up-to-date insights.


Module 8: Exploring Snowflake Advanced Topics


Time Travel and Data Versioning for historical analysis.

Utilizing Snowflake external tables for diverse data sources.

Sharing data across organizations with Snowflake Data Sharing.


Module 9: Best Practices for Snowflake Success


Cost optimization strategies for your Snowflake usage.

Effective administration and maintenance tips.

Troubleshooting common issues in Snowflake.

Conclusion:

Congratulations! You've completed our Snowflake learning course, and now you're equipped with the knowledge and skills to thrive in the world of modern data management and analytics. Embrace Snowflake's power to harness data-driven insights and drive your business to new heights in the cloud.


Remember, learning is an ongoing journey, and Snowflake's ever-evolving features will keep you on the cutting edge of data technology. So, keep exploring, experimenting, and expanding your data expertise with Snowflake!


[Closing Call-to-Action]

If you found this Snowflake learning course valuable, don't miss out on future content! Subscribe to our blog and stay tuned for more exciting updates, tips, and tricks in the world of data and analytics.


Happy Snowflaking!

What is Snowflake and why is it so popular?

Introduction:


        In the world of modern data management and analytics, Snowflake has emerged as a shining star, captivating businesses with its revolutionary approach to handling data in the cloud. But what exactly is Snowflake, and why has it garnered so much popularity in recent times? In this blog post, we'll delve into the core of Snowflake's architecture and explore the factors that make it a game-changer in the world of data platforms.


What is Snowflake?


        At its essence, Snowflake is a cloud-based data platform that provides a centralized and scalable repository for storing, processing, and analyzing structured and semi-structured data. Unlike traditional data warehouses that rely on on-premises infrastructure, Snowflake operates entirely in the cloud, offering a host of advantages that have captured the attention of businesses worldwide.


1. A Cloud-Native Marvel:


        Snowflake's architecture is meticulously crafted for the cloud. It embraces the principles of elasticity, scalability, and flexibility that the cloud offers, empowering organizations to handle data workloads of any size without the need for complex and costly hardware setup.


2. Multi-Cluster Shared Data Architecture:


        One of the key aspects that sets Snowflake apart is its unique multi-cluster shared data architecture. In Snowflake, data storage and computing resources are decoupled, allowing them to scale independently based on demand. This architecture ensures that compute resources can be allocated dynamically, providing the required power to analyze data without interruption.


3. Simplicity and Zero Management Overhead:


    Snowflake takes the burden of managing infrastructure off the shoulders of data teams. With Snowflake, there's no need to worry about hardware provisioning, software upgrades, or performance tuning. This simplicity allows data professionals to focus on what truly matters - extracting insights from their data.


Why is Snowflake so Popular?


1. Scalability and Performance:


        One of the primary reasons behind Snowflake's popularity is its unparalleled scalability and performance. With the ability to handle massive datasets and process queries at lightning speed, Snowflake empowers organizations to make data-driven decisions in real-time, accelerating business outcomes.


2. Cost-Efficiency:


        Snowflake's pay-as-you-go pricing model, combined with its elasticity and zero-copy cloning capabilities, optimizes resource utilization and reduces unnecessary costs. Organizations can scale up or down based on actual needs, ensuring they only pay for the resources they consume.


3. Secure Data Sharing:


        Data collaboration is crucial in today's interconnected world. Snowflake's secure data sharing capabilities enable seamless sharing of data with partners, customers, or different departments. This feature eliminates the need for complex data pipelines, streamlining data exchange while maintaining robust security controls.


4. Data Governance and Compliance:


Snowflake understands the importance of data governance and compliance. The platform is built with strong security measures, ensuring data is protected at rest and in transit. Snowflake also complies with various industry standards, giving businesses the peace of mind that their data is in safe hands.


Conclusion:


        In conclusion, Snowflake has earned its popularity by offering a cloud-native, scalable, and cost-effective solution for data management and analytics. Its innovative architecture, combined with outstanding performance, secure data sharing, and a seamless user experience, has propelled Snowflake to the forefront of the data platform revolution.


        As businesses continue to harness the power of data to drive growth and innovation, Snowflake remains the go-to choice for those seeking to unlock the true potential of their data in the cloud. Embrace Snowflake's capabilities and embark on a data journey that will lead your organization to new heights in the world of data-driven decision-making.


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Happy Snowflaking!

BI Developer

snowflake

 https://quickstarts.snowflake.com/guide/devops_dcm_schemachange_github/index.html?index=..%2F..index#5

IICS

 https://knowledge.informatica.com/s/article/624339?language=en_US

how to get pm/am datetime in T-SQL server


SELECT GETDATE() ActualDateTime,FORMAT(GETDATE(),'MM/dd/yyyy HH:mm:ss tt') TransformedDateTime;


 

how to get first day of month in snowflake

 we have a function in the snowflake, that will truncate the month/year from the given date.


here are few examples.

1. Using Current_Date function

SELECT CURRENT_DATE() AS Today_Date,DATE_TRUNC(MONTH,CURRENT_DATE()) AS Start_Date;

Output: 



2. By using Hardcoded Value

SELECT '2021-05-26' AS Actual_Date,DATE_TRUNC(MONTH,TO_DATE('2021-05-26')) AS Start_Date;


OutPut: 




SQL Server Services

  •  SQL Server supports 4 services
    1. Database Server 
      • SQL Server (DB Engine)
      • SQL Server Agent (Automation)
      • SQL Full-text Filter Daemon Launcher
    2. Report Server
      • SQL Server Reporting Services
    3. Integration Server
      • SQL Server Integration Services
    4. Analysis Server
      • SQL Server Analysis Services

Introduction to SQL Server

  • SQL Server is an RDBMS product, developed by Microsoft
  • With SQL Server
    • We can create and manage databases 
    • It supports BI features (SSIS, SSRS, SSAS)
  • SQL Server is a collection of 4 servers
    • Databases Server
      • To work with databases
      • It works using SQL command
    • Report Server
      • To generate report
      • To implement export and import activities.
    • Analysis Server
      • To build data ware house
  • SQL Server supports a language - SQL (Structured Query Language)
    • IBM product
    • Non procedural language 
    • Common database language used by every RDBMS product
    • Case insensitive language

    • We can say that every server has their own services where database server has 3 main services.
      • SQL Server                (Database Engine)
      • SQL Server Agent     (For automation)
      • SQL Full-text Filter Daemon Launcher
    • For programming SQL Server supports
      • T-SQL          (Transact-SQL)
        • SQL
        • Programming part
      • CLR Integration
        • To execute SP, triggers etc, written with .Net languages
        • We have to enable car FEATURE

        sp_configure 'car enabled',1
        reconfigure