How to load data fromAshby toBigQuery
Learn how to use Airbyte to synchronize your Ashby data into BigQuery within minutes.
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Start syncing with Airbyte in 3 easy steps within 10 minutes
Set up aAshby connector in Airbyte
Connect to Ashby or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.
Set upBigQuery for your extractedAshby data
Select BigQuery where you want to import data from your Ashby source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.
Configure theAshby toBigQuery in Airbyte
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How to Sync Ashby to BigQuery Manually
Step 1: Export Data from Ashby
Begin by exporting the data you need from Ashby. Depending on the options available, you can typically export data in formats such as CSV or JSON. Navigate to the data export section of Ashby, select the required datasets, and choose an appropriate format for export. Save the exported file(s) locally on your machine.
Step 2: Review and Clean Exported Data
Open the exported file(s) to review and clean the data. Ensure there are no corrupted entries or unnecessary columns that could affect the import process into BigQuery. This step is crucial for ensuring data integrity and simplifying data transformation later.
Step 3: Prepare GCP Environment
Set up your Google Cloud Platform (GCP) environment if you haven't already. Ensure you have a Google Cloud account and have created a project where you plan to import the data. Activate the BigQuery API within your project through the GCP Console.
Step 4: Upload Data to Google Cloud Storage (GCS)
Use Google Cloud Storage as an intermediary to move your data to BigQuery. Navigate to the GCS section in your GCP Console, create a new bucket (if necessary), and upload your cleaned data files to this bucket. Ensure that the bucket's permissions allow BigQuery to access the files.
Step 5: Create a BigQuery Dataset
In the BigQuery section of the GCP Console, create a new dataset where you intend to store your imported data. A dataset in BigQuery acts as a container for your tables, and you can configure it with specific permissions and settings as needed.
Step 6: Load Data from GCS to BigQuery
Utilize the BigQuery UI or the command-line tool to load your data from Google Cloud Storage into BigQuery. You will need to specify the source data format (e.g., CSV or JSON), the schema for your BigQuery table, and the GCS file path. Use the BigQuery import wizard to map the data fields appropriately.
Step 7: Verify and Validate Data Import
Once the data loading process is complete, verify the imported data within BigQuery. Run queries to ensure data accuracy and completeness, checking that all fields are correctly populated and that no data is missing. Validate that the data types and structures align with your expectations and make adjustments as necessary.
By following these steps, you can successfully move data from Ashby to BigQuery without relying on third-party connectors or integrations.
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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.
What is Ashby?
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.
What data can you extract from Ashby?
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.
How do I transfer data from Ashby?
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Ashby to BigQuery as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Ashby to BigQuery and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.
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:
