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Begin by exporting the data you need from Ashby. This could involve using built-in export features if available, such as CSV or JSON exports. Ensure you have the necessary permissions to access and export data from Ashby, and decide on the format that best suits your needs for transfer and import into ClickHouse.
Once you have exported the data, prepare it for import into ClickHouse. This involves cleaning and structuring the data files to ensure they are consistent and correctly formatted. Check for any discrepancies, duplicate entries, or missing values that need to be addressed. Maintain a backup of the raw export data for reference or reprocessing if necessary.
Before importing data, ensure your ClickHouse instance is properly set up. This involves creating the necessary databases and tables that match the structure of your data. Use ClickHouse's DDL (Data Definition Language) to create tables with the appropriate column types and settings that align with your data structure.
Transfer the prepared data files to the server where your ClickHouse instance is running. You can use secure file transfer methods like SCP (Secure Copy Protocol) or SFTP (SSH File Transfer Protocol) to move files from your local system to the ClickHouse server. Ensure the server has enough storage to accommodate the data files.
Utilize ClickHouse's command-line tools to import the data into your ClickHouse database. Tools like `clickhouse-client` allow you to execute SQL queries directly from the command line. Use the `INSERT INTO` statement or the `clickhouse-client --query` option with the appropriate format (CSV, JSON, etc.) to load data into your tables.
After importing the data, verify its integrity by running queries to check for completeness and accuracy. Compare the imported data with the original data from Ashby to ensure consistency. Check for any discrepancies or errors, and re-import data if necessary. This step helps in confirming that the data transfer was successful and accurate.
Once the data is successfully imported and verified, optimize your ClickHouse tables for performance. This may involve adding indexes, optimizing table settings, and running `OPTIMIZE TABLE` commands to improve query performance. Proper indexing and optimization help in making the data retrieval process faster and more efficient.
By following these steps, you can successfully move data from Ashby to ClickHouse without relying on third-party connectors or integrations, ensuring a smooth and controlled data transfer process.
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:
