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Begin by exporting the data from Ashby. Log into your Ashby account and navigate to the data or reports section. Use Ashby's export functionality to download the data in a common file format, such as CSV or JSON, depending on what's available and best suited for your data types.
Set up a local environment where you can manipulate the exported data. Ensure you have a computer with enough storage space and necessary tools like a text editor or a spreadsheet application. You may also need Python or other scripting tools if transformations are required.
Transform the exported data into a format compatible with Snowflake. This typically involves cleaning the data (removing any corrupt or unwanted entries) and ensuring it adheres to the schema you plan to use in Snowflake. Use scripts or data processing tools to format the data correctly, ensuring date formats, numeric precision, and text encodings match Snowflake's requirements.
Log into your Snowflake account and create a new database and table that matches the structure of your transformed data. Use SQL commands in Snowflake's web interface or a Snowflake SQL client to define the schema, specifying column names, data types, and any constraints or indexes needed.
Before loading data into Snowflake, you need to stage it. Use Snowflake's internal stage or an external stage like Amazon S3 if your data is too large for direct upload. Upload the transformed data files to the stage using Snowflake's web interface or the SnowSQL command-line tool. Ensure that the files are accessible and correctly formatted for Snowflake to process.
Use the `COPY INTO` command in Snowflake to load the data from the stage into your database table. Ensure the command specifies the correct file format options, such as field delimiter, file type, and any transformations needed during loading. Execute the command and monitor for any errors or warnings.
Once the data is loaded, run queries to verify that the data in Snowflake matches the original data from Ashby. Check for completeness, correctness, and consistency. If discrepancies are found, you may need to adjust the transformation or loading process and reload the data.
By following these steps, you can successfully move data from Ashby to Snowflake without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Ashby uses a heavily-optimized infrastructure-as-a-service (IaaS) platform from Heroku and Amazon Web Services. Ashby is SOC2 compliant and Type 2 audited annually. Our SOC2 reports are available upon customer request. Ashby permits authentication from Google Workspace (formerly GSuite), Office 365 corporate accounts, Magic Links (sent via email), and SSO via SAML and OIDC. Ashby does not store any passwords. Ashby app is safe to use and requests are authentic with XSS and CSRF protection, signed and encrypted user authentication cookies, and session expiration.
Ashby's API provides access to a wide range of data related to the UK property market. The data can be categorized into the following categories:
1. Property Listings: Ashby's API provides access to a comprehensive database of property listings across the UK. This includes details such as property type, location, price, and features.
2. Property Valuations: The API also provides access to property valuation data, which can be used to estimate the value of a property based on various factors such as location, size, and condition.
3. Market Trends: Ashby's API provides access to data on market trends, including information on property prices, rental yields, and demand for different types of properties.
4. Demographics: The API also provides access to demographic data, including information on population density, age distribution, and income levels in different areas.
5. Property Ownership: Ashby's API provides access to data on property ownership, including information on the number of properties owned by individuals and companies, as well as details on property transactions.
6. Planning Applications: The API also provides access to data on planning applications, including information on the number of applications submitted, approved, and rejected in different areas.
Overall, Ashby's API provides a wealth of data that can be used by property professionals, investors, and researchers to gain insights into the UK property market.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
