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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.
Close is the inside sales CRM of choice for startups and SMBs. Make more calls, send more emails and close more deals starting today. Close is the sales engagement CRM designed to assist SMBs to turn more leads into revenue. With Close, you can email, call, and text your leads without adding any additional features. Power Dialer and task reminders help you follow up more frequently and reach more leads.
Close.com's API provides access to a wide range of data related to sales and customer relationship management. The following are the categories of data that can be accessed through Close.com's API:
1. Contacts: This includes information about individual contacts such as name, email address, phone number, and company.
2. Leads: This includes information about potential customers who have shown interest in a product or service, including their contact information and any interactions they have had with the company.
3. Opportunities: This includes information about potential sales opportunities, including the value of the opportunity, the stage of the sales process, and any associated contacts or leads.
4. Activities: This includes information about any activities related to sales or customer relationship management, such as calls, emails, and meetings.
5. Tasks: This includes information about tasks that need to be completed, such as follow-up calls or emails.
6. Custom Fields: This includes any custom fields that have been created to store additional information about contacts, leads, or opportunities.
Overall, Close.com's API provides access to a comprehensive set of data that can be used to improve sales and customer relationship management processes.
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.
Close is the inside sales CRM of choice for startups and SMBs. Make more calls, send more emails and close more deals starting today. Close is the sales engagement CRM designed to assist SMBs to turn more leads into revenue. With Close, you can email, call, and text your leads without adding any additional features. Power Dialer and task reminders help you follow up more frequently and reach more leads.
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Close.com" source connector and select "Create new connection."
3. Enter a name for the connection and click "Next."
4. Enter your Close.com API key and API secret in the appropriate fields.
5. Click "Test" to ensure that the connection is successful.
6. If the test is successful, click "Next" to proceed to the next step.
7. Select the tables you want to replicate from Close.com.
8. Choose the replication frequency and the initial replication start date.
9. Click "Create connection" to finalize the setup.
Once the connection is created, Airbyte will begin replicating data from Close.com according to the specified settings. You can monitor the progress of the replication in the "Connections" tab on the Airbyte platform. If you need to make changes to the connection settings, you can do so by clicking on the connection and selecting "Edit."
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Close.com's API provides access to a wide range of data related to sales and customer relationship management. The following are the categories of data that can be accessed through Close.com's API:
1. Contacts: This includes information about individual contacts such as name, email address, phone number, and company.
2. Leads: This includes information about potential customers who have shown interest in a product or service, including their contact information and any interactions they have had with the company.
3. Opportunities: This includes information about potential sales opportunities, including the value of the opportunity, the stage of the sales process, and any associated contacts or leads.
4. Activities: This includes information about any activities related to sales or customer relationship management, such as calls, emails, and meetings.
5. Tasks: This includes information about tasks that need to be completed, such as follow-up calls or emails.
6. Custom Fields: This includes any custom fields that have been created to store additional information about contacts, leads, or opportunities.
Overall, Close.com's API provides access to a comprehensive set of data that can be used to improve sales and customer relationship management processes.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: