Harvest to Snowflake Data Migration: 2 Easy Methods

Imagine your HR and accounting departments suddenly requested a connection between Harvest and Snowflake. And you have to sacrifice your engineering bandwidth to handle this task head-on. This can be challenging when you’re already busy monitoring and configuring your current data pipeline.

We know you’re looking for a fast and accurate solution, and we’ve got you covered. We have prepared a simple, step-by-step guide to help you replicate data from Harvest to Snowflake That’s enough chit-chat.

What Is Harvest?

Harvest is a cloud-based time-tracking and invoicing platform that helps businesses monitor time spent on various tasks and projects, create invoices, manage expenses, etc.

Key Features Of Harvest

• Time Tracking: Easily track work hours with timers or manual entries.
• Invoicing: Convert tracked time into professional invoices and send them to clients.
• Expense Tracking: Track and categorize project expenses, adding them to invoices.
• Reporting: Generate detailed reports on time, expenses, and project performance.

What is Snowflake?

Snowflake is a cloud-based data warehousing platform that enables businesses to store, process, and analyze large amounts of data efficiently. It’s known for its scalability, flexibility, and ability to handle diverse workloads across multiple cloud environments without requiring extensive infrastructure management.

Key Features Of Snowflake

• Scalability: Instantly scale compute and storage independently, based on workload demands.
• Multi-Cloud Support: Operates across major cloud providers (AWS, Azure, Google Cloud).
• Data Sharing: Allows secure, real-time data sharing across organizations without moving data.
• Concurrency Handling: Handles multiple queries at the same time without performance degradation.

How Can You Integrate Data from Harvest to Snowflake?

To transfer data from Harvest to Snowflake, you can either:  

• Utilize CSV Files 
• Employ a no-code automated solution 

Let’s Discuss Replication Via CSV files next.

Method 1: How Can You Migrate Data from Harvest to Snowflake Using CSV files?

Let’s discuss the stepwise process to integrate data from Harvest to Snowflake in CSV Format: 

Step 1: Export Data from Harvest as CSV Files 

You can export the following type of data from your Harvest account:

• Time Entries 
• Clients and Contact
• Invoices
• Projects and Tasks 
• Expenses
• Reports 

And then with the above data, you can build the following type of report on your Harvest account:

• Time Report
• Detailed Time Report
• Detailed Expense Report
• Uninvoiced Report
• Invoiced Report
• Payments Received Report
• Contractor Report

Let’s jump into the stepwise process to export data from Harvest in CSV Format.

To export data about contacts, people, clients, and tasks:

1. Go to the Manage tab.
2. Go to the top right corner of the page and click Export.
3. A list of your required data will be downloaded in a comma-separated values (CSV) file format.

To export projects data:

1. Go to the Projects tab.
2. Go to the top right corner of the page and click Export.
3. Export projects from Harvest with the desired status: active, budgeted, or archived.
4. A list of your required project data will be downloaded in a CSV file format.

To export reports data:

1. Go to the Reports tab.
2. Select the name of the report you want to export.
3. Export a report from Harvest with desired timeframe, projects, filters, etc.
4. Click the Run Report button.
5. Go to the top right corner of the page and click Export. Then, choose the format as CSV.
6. A list of your required report data will be downloaded in a CSV file format.

To export estimates data:

1. Go to the Estimates tab.
2. To export a list of open estimates, click on the Open option and select the Export button.
3. To export all of your estimates, click on the All Estimates option. Now, you can filter the data by estimating status, client, or timeframe. Then select the Export button.
4. Choose your format as CSV. A list of your estimated data will be downloaded in a CSV file format.

Step 2: Import CSV Files into Snowflake 

Login to your Snowflake account and select the database where you want to upload the files. 

USE DATABASE test_db;

CREATE OR REPLACE FILE FORMAT harvest_csv_format
  TYPE = csv
  FIELD_DELIMITER = ','
  SKIP_HEADER = 1
  NULL_IF = ('null', 'NULL')
  EMPTY_FIELD_AS_NULL = true
  COMPRESSION = gzip;;

1) Create the New Table in Snowflake

Considering no destination table exists, employ the CREATE OR REPLACE TABLE command to create the new table.

CREATE OR REPLACE TABLE harvest_data (
  id INT,
  project_name VARCHAR(255),
  task_name VARCHAR(255),
  start_date DATE,
  end_date DATE,
  hours INT
);

2) Load Data to Target 

Upload the CSV file to the Snowflake staging area using the PUT command and load the data into your target table using the COPY INTO command.

PUT file://D:\harvest_data.csv @test_db.PUBLIC.%harvest_data/harvest_data.csv.gz;

COPY INTO harvest_data
FROM (
  SELECT $1 AS column1, $2 AS column2, ... 
-- Specify the column names based on your CSV file
  FROM @test_db.PUBLIC.%harvest_data/file://D:\harvest_data.csv
  (file_format = (format_name = 'harvest_csv_format',
                  error_on_column_count_mismatch = false),
   pattern = '.*harvest_data.csv.gz',
   on_error = 'skip_file')
);

This method is particularly valuable in the following scenarios:

1. One-Time Replication: If your Finance team needs data from Harvest yearly, quarterly, or once in a while, then this manual effort and time are justified.
2. Dedicated Team: If your organization has dedicated staff who manually select categories, customize templates, and download and upload CSV files, then replicating data from Harvest using CSV files is the way to go
3. Less number of Reports: Managing multiple CSV files can be time-consuming and tedious, especially when you need to generate a 360-degree view of your business and merge data from multiple departments.

However, downloading CSV files and transforming Harvest data is not a scalable solution for businesses that need to integrate data from multiple sources. 

This approach is time-consuming, error-prone, and requires specialized skills. 

So, if you’re aiming to replicate data from multiple sources, you can….

Method 2: Integrate Data From Harvest to Snowflake Using an Automated ETL Tool

Creating custom scripts manually for every new data connector request is not a practical or cost-effective solution. Frequent failures, pipeline errors, and a lack of data flow monitoring make it challenging to scale such a system.

An automated tool can streamline the data replication process and improve efficiency and accuracy. Here are some of the benefits of using an automated tool:

• Reduced time to market: Automated tools can help you get your data into production faster. This is because they can handle the complex tasks of data extraction, transformation, and loading (ETL) automatically.
• Improved data quality: Automated tools can help you improve the quality of your data by detecting and correcting errors. This can help you avoid costly mistakes and improve the accuracy of your reports and analysis.
• Increased scalability: Automated tools can scale easily as your data volumes grow. This means you can easily add new data sources and users without having to worry about the performance of your system.
• Reduced costs: Automated tools can help you reduce the cost of data replication by automating manual tasks and eliminating the need for expensive IT staff.

If you want to improve the efficiency, accuracy, and scalability of your data replication process, then an automated tool is a good option.

For instance, here’s how Hevo, a cloud-based ETL tool, makes Harvest to Snowflake data replication ridiculously easy:

Step 1: Configure Harvest as a Source 

Step: 2 Configure Snowflake as a Destination 

Your ETL pipeline is now set up!

Hevo Data will automatically create a pipeline to integrate Harvest and Snowflake. The pipeline will replicate new and updated data from Harvest to Snowflake every hour by default. You can also, adjust the replication frequency to meet your needs.

Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. 

By using Hevo to simplify your data integration needs, you can leverage its salient features:

• Reliability at scale: Hevo’s world-class fault-tolerant architecture scales with zero data loss and low latency.
• Monitoring and observability: Hevo provides intuitive dashboards that reveal every stat of the pipeline and data flow, allowing you to monitor pipeline health and bring real-time visibility into your ELT with alerts and activity logs.
• Stay in total control: When automation isn’t enough, Hevo offers flexibility in terms of data ingestion modes, ingestion and load frequency, JSON parsing, destination workbench, custom schema management, and more, so that you can have total control.
• Auto-schema management: Hevo automatically maps the source schema with the destination warehouse, so you don’t have to worry about schema errors.
• 24×5 customer support: Hevo offers 24×5 support through live chat, so you can always get help when you need it.
• Transparent pricing: Hevo’s transparent pricing model gives you complete visibility into your ELT spending. Choose a plan based on your business needs and stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.

Hevo is a powerful data pipeline platform that can help you simplify your data integration needs. With its reliable architecture, comprehensive features, and 24×7 support, Hevo is a great choice for businesses of all sizes.

What Can You Achieve by Migrating Your Data From Harvest to Snowflake?

Replication of data from Harvest to Snowflake can provide critical business insights. Here are five questions that you can answer by replicating your data:

1. What are the total expenses incurred for each project or client?
2. What are the billable hours for each client during a particular time period?
3. Is there any specific trend or pattern in client purchase behavior?
4. What is the average revenue per client or project?
5. Who are the Important contributors to a project?

Final Thoughts  

Downloading CSV Files and connecting data from Harvest to Snowflake can bring absolute bliss when you receive a request from your HR and finance team once in a while. But what if the frequency of this request becomes constant or never-ending?  

Would you still be dependent on the tiring and laborious task of manually exporting and importing CSV files from different sources? In this scenario, you can free yourself and your team from the tiring task by opting for an automated reliable ETL solution.

A custom ETL solution becomes necessary for real-time data demands such as monitoring email campaign performance or viewing the sales funnel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo.

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s preload data transformations get it done in minutes via a simple drag n-drop interface or your custom Python scripts. There is no need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

It has pre-built integrations with 150+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial.

Check out the pricing details to understand which plan fulfills all your business needs.

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