There are several applications available to assist organizations in creating invoices. But finding the right invoicing system for your company can be difficult. Previously, companies created invoices using Word or Excel invoice templates. However, with the advent of invoicing software that automates many of the entries for you, eliminating the need to manually enter in or copy and paste a customer’s name and address. One such popular automated billing software is Invoiced.
While Invoiced can help address the billing woes, businesses still need to store all the data for analytics to help them in making better decisions. Though we have traditional data warehouses to cater to such demands, they are resource-intensive and struggle with data latency. Therefore, companies may seek to take advantage of cloud data warehouses like Google BigQuery’s ability to efficiently perform complex analytical queries over petabytes of data.
In this blog, you will walk through Invoiced and Google BigQuery and the need for connecting Invoiced to BigQuery platforms for data migration.
What is Invoiced?
Invoiced is a cloud-based account receivable automation program that automates collections and streamlines organizational payments. Invoiced can be a good alternative for automating routine procedures so your accounting team can focus on more work-intensive financial processes, regardless of the type of organization. Businesses could use the platform’s configurable features, which are ideal for high-volume billers who wish to simplify monthly invoicing. They may use the software to create billing plans, like installment payments, and send invoices through email or text messages ahead of time.
Invoiced provides an easy-to-use dashboard where users can access all of the controls, menus, and customization options they need to complete accounts receivable operations. These include drafting bills, reviewing client payment histories, and reviewing analytics information such as critical A/R indicators and the quickest and slowest paying customers.
Key Features of Invoiced
- Integrations and API: Invoiced connects with major accounting software, including Xero and Quickbooks, guaranteeing that your staff has all the information they need to fulfill their job without switching applications. It also employs REST API, allowing you to build customized integrations on top of the platform.
- Subscription Billing: Through its Subscription Billing module, Invoiced can assist in reducing the complexity and time-consuming nature of handling subscription billing. Users can select from a variety of plans inside the module, including basic, usage-based, and variable pricing. The addition of non-trivial renewal logic, like discounts, prorations, trial periods, or unique payment conditions, will allow users to further tailor the subscription billing.
- Flexible Payment Plans: Invoiced helps enhance your bottom line by integrating with online payment systems and different payment channels, including credit cards and debit cards, in addition to improving your internal invoicing practices. This gives your consumers extra payment alternatives, including the ability to set up autopay for regular transactions. This ensures that your consumers have a more contemporary, seamless payment experience.
What is Google BigQuery?
Google BigQuery is a cloud-based data warehouse with a Big Data Analytic Web Service that can process petabytes of data. BigQuery is separated into two parts: storage and query processing, and is designed for large-scale data analysis. It can be used in conjunction with other Google products and services to improve your workflows.
Based on Dremel, BigQuery features the Massively Parallel Processing (MPP) architecture, which enables it to query data by reading 1000s of rows in a single second. Data is kept in replicated, distributed units in this system, and it is processed by Compute Clusters made up of Shared-Nothing Nodes. As a result, the Google BigQuery architecture is extremely flexible, allowing customers to upload their data to a data warehouse and analyze it using standard SQL queries.
Key Features of Google BigQuery
- Flexible Scaling: You don’t have to explicitly tweak the cluster with BigQuery since computing resources are automatically adjusted according to the workload, and it can easily extend storage to Petabytes on demand. Patching, updates, computing, and storage resource scaling are all handled by Google BigQuery, making it a fully managed service.
- Storage: Google BigQuery leverages Google’s global storage system, Colossus, to store and optimize your data for free and with no downtime. To store data, Google BigQuery uses the opinionated Capacitor format in Colossus, which achieves various enhancements behind the scenes while burning a large amount of CPU/RAM, all without affecting query performance or imposing a bill limit.
- Programming Access: Google BigQuery may be easily accessed in applications using Rest API queries, Client Libraries such as Java, .Net, Python, the Command-Line Tool, or the GCP Console. It also includes query and database management tools. All query operations are ACID-compliant, and the integrity of the updated data is protected in the case of event failure.
- Federated Query: Google BigQuery employs a novel method for sending a query statement to an external database and receiving the results as a temporary table. Suppose the user’s data is stored in Bigtable, GCS, or Google Drive. In that case, they can query it directly from Google BigQuery by using the Google BigQuery Connection API to create a connection with the external database.
Invoiced is a platform that has won numerous awards for assisting mid-market to enterprise-level companies in receiving payments more quickly, quitting wasting time on collections, and improving the customer payment experience.BigQuery offers powerful streaming ingestion capabilities that let businesses capture and analyze data in real-time, ensuring that their insights are always up to date. Up to 1 TB of data can be analyzed and 10 GB of data can be stored for free with BigQuery.
When integrated, moving data from Invoiced to BigQuery could solve some of the biggest data problems for businesses. In this article, two methods to achieve this are discussed:
Hevo Data, an Automated Data Pipeline, provides you with a hassle-free solution to connect Invoiced to BigQuery within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process of loading data from Invoiced to BigQuery and enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
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This method would be time-consuming and somewhat tedious to implement. Users will have to write custom codes to enable two processes, streaming data from Invoiced to BigQuery. This method is suitable for users with a technical background.
Connect Invoiced to BigQuery
Method 1: Using Hevo Data to Connect Invoiced to BigQuery
Hevo provides an Automated No-code Data Pipeline that helps you move your Invoiced to BigQuery. Hevo is fully-managed and completely automates the process of not only loading data from your 100+ data sources(including 40+ free sources)but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using Hevo Data, you can connect Invoiced to BigQuery in the following 2 steps
- Step 1: Configure Invoiced as the Source in your Pipeline by following these steps:
- Step 1.1: In the Asset Palette, select PIPELINES.
- Step 1.2: In the Pipelines List View, click + CREATE.
- Step 1.3: Select “Invoiced” on the Select Source Type page.
- Step 1.4: Enter the following information on the Configure your Invoiced Source page:
- Pipeline Name: A distinct, 255-character-maximum name for the Pipeline.
- API Key: The API key that was retrieved from your Invoiced account.
- Step 1.5: Simply press TEST & CONTINUE.
- Step 1.6: Configure the data ingestion and establish the destination after that.
- Step 2: To set up Google BigQuery as a destination in Hevo, follow these steps:
- Step 2.1: In the Asset Palette, select DESTINATIONS.
- Step 2.2: In the Destinations List View, click + CREATE.
- Step 2.3: Select Google BigQuery from the Add Destination page.
- Step 2.4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.
- Step 2.5: Choose one of these:
- Using a Service Account to connect:
- Service Account Key file, please attach.
- Note that Hevo only accepts key files in JSON format.
- Go to CONFIGURE GOOGLE BIGQUERY ACCOUNT and click it.
- Using a user account to connect:
- To add a Google BigQuery account, click +.
- Become a user with BigQuery Admin and Storage Admin permissions by logging in.
- To grant Hevo access to your data, click Allow.
- Step 2.6: Set the following parameters on the Configure your Google BigQuery page:
- Destination Name: A unique name for your Destination.
- Project ID: The BigQuery Project ID that you were able to retrieve in Step 2 above and for which you had permitted the previous steps.
- Dataset ID: Name of the dataset that you want to sync your data to, as retrieved in Step 3 above.
- GCS Bucket: To upload files to BigQuery, they must first be staged in the cloud storage bucket that was retrieved in Step 4 above.
- Enable Streaming Inserts: Enable this option to load data via a job according to a defined Pipeline schedule rather than streaming it to your BigQuery Destination as it comes in from the Source. To learn more, go to Near Real-time Data Loading Using Streaming. The setting cannot be changed later.
- Sanitize Table/Column Names: Activate this option to replace the spaces and non-alphanumeric characters in between the table and column names with underscores ( ). Name Sanitization is written.
- Step 2.5: Click Test Connection to test connectivity with the Amazon Redshift warehouse.
- Step 2.6: Once the test is successful, click SAVE DESTINATION.
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- Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
- Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
- Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
- Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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Method 2: Using Custom Code to Move Data from Invoiced to BigQuery
Your Invoiced account provides extensive information on the customers, including payment history, subscription and usage billing, payment plans, and much more. It offers a huge volume of data-based statistics, including cash forecasting, top debtors, and much more. Business owners could use BigQuery to integrate this data with data from other applications to analyze data from various channels at once and generate reports instantly.
Companies can use data loaded from Invoiced to BigQuery to produce accurate, well-informed decisions on their revenue business model, installment plans, payment schedules, and so on. You could also personalize your invoices to distinguish yourself from the competition, manage cash flows, assist with sales documentation and automate internal operations.
Creating Reports in Invoiced to Export Data
- Go to Invoiced home Dashboard and navigate through the left-hand panel. To generate a new report, go to Reports and click New Report.
- Choose the data type on which you wish to build your report. Customers, invoices, line items, payments, and more are among the more than 40 types of data available.
- Choose a visualization style: Metric, table, or chart.
- Choose which fields you’d want to see on your report. The fields available will change depending on the type of reporting data selected.
- Filtering, grouping, and sorting conditions can be added to your report as required.
- Your report can include more than one section for which a separate reporting data type can be used in each section.
- To create your report, click the Generate button. You can save your created report after seeing it if you wish to use it again later.
- When generating your report, clicking the “Multi-Entity” toggle can create a multi-entity report. This will make data from all entities to which you have reporting access available to you.
- When you’ve finished creating a custom report, click the Save button to save it to your Saved Reports list.
You can have reports prepared and emailed to you automatically every week or month. Here’s how to create a reporting schedule:
- Go to the Saved Reports section of your account. You will need to save the report you wish to schedule if you haven’t already.
- Next to the report, you wish to schedule, click the Schedule button.
- Enter the desired frequency, weekday/month, and time of day for the report to be delivered.
- To schedule a report, click the Schedule button and fill in any needed report parameters.
- The system will send an email with the generated report as per the schedule.
To receive and access scheduled reports, you must have an Invoiced user account.
Importing Data to BigQuery
BigQuery may be populated with data via a variety of methods, including batch loading a set of records, streaming individual records or batches of information, and leveraging third-party software or services. Additionally, queries can be used to create fresh data and add to or replace existing data in a database.
In this article, we’ll concentrate on batch loading, a technique that involves loading the source data into a BigQuery database all at once. A CSV file or a collection of log files are a few examples of possible data sources. In this tutorial, you will learn how to import a CSV file.
You can load a CSV file into a new table or partition when you load it from Cloud Storage. You could also attach your CSV file to replace an existing table or position. Your CSV file gets transformed into the columnar format for Capacitor when it is put into Google BigQuery.
You will require the IAM permissions to load data into BigQuery or partitions before you can load the CSV file.
- Permissions to load data into BigQuery
To load data and add to or replace an existing table or partition in BigQuery, you require the following IAM permissions.
The permissions required to import data into a BigQuery table or partition are included in each of the predefined IAM roles listed below:
roles/bigquery.admin (includes the bigquery.jobs.create permission)
bigquery.user (includes the bigquery.jobs.create permission)
bigquery.jobUser (includes the bigquery.jobs.create permission)
- Permissions to load data from Cloud Storage
You need the following IAM permissions to load the data from Cloud Storage.
The predefined IAM role roles/storage.objectViewer comprises all the permissions you will require to load data from Cloud Storage.
- Using a CSV file to load data into a BigQuery table
The following commands will import the CSV file into a BigQuery table:
- Cloud Console.
- The QB command-line tool’s bq load commandsCalling the jobs.
- Insert API method.
- Client libraries
Follow the steps below to load the CSV file into a BigQuery table.
- Go to the BigQuery page in the Cloud Console.
- In the Navigation pane, expand your project and then choose a dataset.
- In the Dataset info section, click the Create table button. Then in the Create Table Form, select upload.
- Under Select File, click on Browse. Browse through the options, select the file you want to upload, and click Open.
- For the file format, choose CSV.
- Enter the following information in the Destination area:
- Choose the dataset for which the table will be created.
- In the Table area, type the name of the table you wish to create.
- Make sure that the Native table is selected in the Table type box.
- In the Schema section, provide the schema definition. Select Auto-detect to allow the Schema to be detected automatically. Any of the options listed below can be used to enter Schema information.
- Click Edit as Text and then paste the Schema as a JSON array. You can build the Schema when using a JSON array by following the same steps you would create a JSON schema file. To review the existing table’s schema in JSON format, use the command shown below.
bq show --format=prettyjson dataset.table
- Enter the table schema by clicking the Add field. Enter the Name, Type, and Mode of each field.
Click on the Advanced Options and follow these instructions to create a table in BigQuery.
Limitations of Connecting Invoiced to BigQuery
- Manual data migration is a time-consuming and challenging task. For configuration, the user must go through several procedures, including access to IAM permissions.
- Setting up BigQuery data transfer is an overly complicated task. You need experts to migrate the data. As a result, companies are compelled to employ a technical team.
- Real-time data transmission from Invoiced to BigQuery is not possible with the manual process.
In this article, we gain a holistic understanding of Invoiced, Google BigQuery, and their key features. Further in this blog, you explore the manual method to load your data by connecting Invoiced to BigQuery. However, given the fact that it is a manual and time-consuming process, it will be beneficial to switch to no-code/low-code ETL pipeline tools like Hevo Data for efficient and real-time data migration.
Visit our Website to Explore Hevo
Hevo Data offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.
This platform allows you to transfer data from 100+ sources (including 40+ Free Sources) such as Invoiced and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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