How to load data from Harvest to Clickhouse

Learn how to use Airbyte to synchronize your Harvest data into Clickhouse within minutes.

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Set up a Harvest connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Clickhouse for your extracted Harvest data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Harvest to Clickhouse in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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How to Sync to Manually

Step 1: Extract Data from Harvest API

Start by accessing the Harvest API to extract the required data. Harvest provides a RESTful API that you can use to fetch data. You will need to authenticate using OAuth 2.0 or Personal Access Tokens. Identify the specific endpoints that contain the data you need, such as time entries, projects, or clients, and make HTTP GET requests to these endpoints to retrieve the data in JSON format.

Once you've retrieved the data in JSON format, parse it using a programming language such as Python, JavaScript, or Ruby. This involves loading the JSON data into objects or data structures that allow you to manipulate and access the individual pieces of data. Ensure that the data is structured in a way that fits the schema of your ClickHouse tables.

With the data parsed, transform it to align with the schema of your ClickHouse database. This may involve renaming fields, changing data types, or aggregating data as needed. It’s crucial that the data transformation preserves the integrity and meaning of the data, ensuring it’s suitable for analysis once loaded into ClickHouse.

Before loading the data, ensure that your ClickHouse instance is ready to receive it. This involves setting up tables with the appropriate schema that matches the transformed data. Use ClickHouse's SQL-like syntax to create tables, defining columns and data types that correspond to your data structure.

Use a programming language that supports HTTP or TCP connections to interact directly with ClickHouse. Libraries like `clickhouse-driver` for Python can facilitate this connection. Configure your connection to authenticate and point to the correct ClickHouse server and database.

With the connection established, use SQL INSERT statements to load your transformed data into ClickHouse. You can do this by iterating over your structured data and executing insert commands through your connection. If your data volume is large, consider batching the insert operations to optimize performance.

After loading the data, execute queries on your ClickHouse database to verify that the data has been accurately transferred and is consistent with the source data from Harvest. Check for completeness, data type accuracy, and any potential discrepancies. Conduct sample analyses to ensure the data behaves as expected in your analytical queries.

By following these steps, you can move data from Harvest to a ClickHouse data warehouse effectively without relying on third-party connectors or integrations.