Data Engineers in the past mainly focused on collecting data from different sources and creating data pipelines to transfer this data to data warehouses. However, their work has become far more complicated, with added responsibilities in data analytics and building algorithms.
As a data engineer, your time is very valuable. You cannot afford to spend hours creating reports or finding methods to move the data.
But we are here to make your work a little easier. In this article, we will discuss how to move data from NetSuite to BigQuery in two simple ways.
How to Connect NetSuite to BigQuery?
Now you are going to learn two methods to connect NetSuite to BigQuery:
Method 1: Integrate NetSuite to BigQuery Using an Automated Data Pipeline Platform
Moving data into the warehouse manually using scripts and code is cumbersome. Scaling such a system is a nightmare due to frequent failures, pipeline errors, and the absence of data flow monitoring.
An automated tool is an efficient and cost-effective option that eliminates months of manual labor. It enables you to concentrate on core engineering objectives while your business teams can immediately begin reporting without delays or reliance on your data.
Step 1: Configure NetSuite as a Source
Configure NetSuite ERP as the source in Hevo.
Step 2: Configure BigQuery as a Destination
Configure Google BigQuery as your Destination.
That’s it, literally! You have connected NetSuite to BigQuery in just 2 steps. These were just the inputs required from your end. Now, everything will be taken care of by Hevo. It will automatically replicate new and updated data from NetSuite to Google BigQuery every 5 minutes (by default). However, you can also increase the pipeline frequency as per your requirements.
Data Replication Frequency
|Default Pipeline Frequency||Minimum Pipeline Frequency||Maximum Pipeline Frequency||Custom Frequency Range (Hrs)|
|3 Hrs||1 Hr||24 Hrs||1-3|
You can also visit the official documentation of Hevo for NetSuite as a source and Google BigQuery as a destination to have in-depth knowledge about the process.
In a matter of minutes, you can complete this No-Code & automated approach of connecting NetSuite to BigQuery using Hevo and start analyzing your data.
Hevo’s fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. By employing Hevo for simplifying your data integration needs, you get to leverage it’s salient features:
Get started for Free with Hevo!
- Fully Managed: You don’t need to dedicate any time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
- Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
- Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision making.
- Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
- Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
- Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website and all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
- Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Method 2: Write Custom ETL Scripts
The first method is by writing custom ETL scripts. This is briefly explained in 4 steps:
- Step 1: Accessing and extracting NetSuite data is the first step in loading it into any type of data warehouse. When working with the SuiteScript Model, RESTlets can be used to extend the SuiteScript API. Consequently, you can deploy server-side scripts that interact with NetSuite data in accordance with RESTful principles.
- Step 2: After accessing NetSuite data, you will need to transform it based on two primary factors.
- The constraints of the database that will be utilized.
- The type of analysis you intend to conduct.
- Step 3: To load data from NetSuite to BigQuery, you can post data via the JSON API, as APIs continue to play a crucial role in both the extraction and loading of data into a data warehouse. In its simplest form, it involves sending a single HTTP POST request using a tool such as CURL or Postman.
- Step 4: After loading data into Google Cloud Storage, you must create a Load Job for BigQuery to load the data into BigQuery. This Job should indicate the Cloud Storage source data that must be imported by providing source URIs that point to the appropriate objects.
Using CSV files and SQL queries is a great way to effectively replicate data from NetSuite to Google BigQuery. It is ideal in the following situations:
- One-Time Data Replication: When your business teams require these NetSuite files only quarterly, annually, or for a single occasion, manual effort and time are justified.
- No Transformation of Data Required: This strategy offers limited data transformation options. Therefore, it is ideal if the data in your spreadsheets is accurate, standardized, and presented in a format that is suitable for analysis.
- Lesser Number of Files: Downloading and composing SQL queries to upload multiple CSV files is a time-consuming task. It can be particularly time-consuming if you need to generate a 360-degree view of the business and merge spreadsheets containing data from multiple departments across the organization.
However, frequent data replication from NetSuite to BigQuery makes the import of CSV files tedious. And retrieving a large number of reports containing massive amounts of data burdens the process. In addition, checking for errors and cleaning the data each time would consume a substantial amount of time.
We’re aware that you’d rather focus on more productive tasks than downloading, cleaning, and uploading CSV files repeatedly.
In this case, you can leverage the power of an automated ETL/ELT solution to eliminate all of your repetitive tasks and multiply your productivity.
What can you achieve by replicating your data from NetSuite to BigQuery?
By migrating your data from NetSuite to BigQuery, you will be able to help your business stakeholders find the answers to these questions:
- How does CMRR (Churn Monthly Recurring Revenue) vary by Marketing campaign?
- How much of the Annual Revenue was from In-app purchases?
- Which campaigns have the most support costs involved?
- For which geographies are marketing expenses the most?
- Which campaign is more profitable?
- What does your overall business cash flow look like?
- Which sales channel provides the highest purchase orders?
The NetSuite objects are classified by Hevo into three categories to distinguish the type of data being ingested:
- Transaction: Transaction objects contain data about your business events, such as financial agreements between your business and your customers or vendors and inventory adjustments. All the objects listed under Transaction are ingested into the Transaction table.
- Item: Item objects contain details of the inventory items that you buy and sell, assemblies you manufacture, or services you provide to customers. All the objects listed under Item are ingested into the Item table.
- Standard: All other objects are classified as standard objects and are ingested into their respective tables.
This article has provided 2 simple steps for integrating NetSuite to Google BigQuery. Moving your data from NetSuite to Google BigQuery will open enormous possibilities for you. For example, you can integrate numerous BI tools with Google BigQuery to create visualizations and dashboards.
BigQuery’s “serverless” architecture prioritizes scalability and query speed and enables you to scale and conduct ad hoc analyses much more quickly than with cloud-based server structures. The cherry on top — Hevo will make it further simpler by making the data replication process very fast!
Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. With integration with 150+ Data Sources such as PostgreSQL, MySQL, and MS SQL Server, we help you not only export data from sources & load data to the destinations but also transform & enrich your data, & make it analysis-ready.
Feel free to catch up and let us know about your experience employing a data pipeline from NetSuite to BigQuery using Hevo.