How to load data from Apify Dataset to Convex

Learn how to use Airbyte to synchronize your Apify Dataset data into Convex within minutes.

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Set up a Apify Dataset connector in Airbyte

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

Set up Convex for your extracted Apify Dataset data

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

Configure the Apify Dataset to Convex 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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TL;DR

set up Apify Dataset as a source connector (using Auth, or usually an API key)
set up Convex as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Apify Dataset

Apify is a web scraping and automation platform that can extract structured data from any website or automate any workflow on the web. For example, imagine you found a website selling shoes and want to get a spreadsheet with all the shoe sizes, colors, prices, etc., but the website doesn't make that information accessible in tabular form. Youcould certainly manually create such a spreadsheet using copy and paste, but that would take a lot of time and cause a lot of frustration. Or you can set up Apify to do this for you in a few seconds.

What is Convex

Convex is a platform that provides a suite of tools for building and deploying machine learning models. It offers a user-friendly interface for data scientists and developers to create and train models, as well as a scalable infrastructure for deploying them in production. Convex also includes features such as automated model tuning, version control, and collaboration tools to streamline the machine learning workflow. The platform is designed to be flexible and customizable, allowing users to integrate their own libraries and frameworks. Overall, Convex aims to simplify the process of building and deploying machine learning models, making it accessible to a wider range of users.

Integrate Apify Dataset with Convex in minutes

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Prerequisites

A Apify Dataset account to transfer your customer data automatically from.
A Convex account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Apify Dataset and Convex, for seamless data migration.

When using Airbyte to move data from Apify Dataset to Convex, it extracts data from Apify Dataset using the source connector, converts it into a format Convex can ingest using the provided schema, and then loads it into Convex via the destination connector. This allows businesses to leverage their Apify Dataset data for advanced analytics and insights within Convex, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Apify Dataset as a source connector

1. First, navigate to the Apify website and log in to your account.
2. Once you are logged in, click on the "API" tab in the top navigation bar.
3. Next, click on the "Credentials" tab and then click the "Create new token" button.
4. Give your token a name and select the appropriate permissions for your use case.
5. Copy the generated token to your clipboard.
6. Navigate to your Airbyte dashboard and click on the "Sources" tab.
7. Click on the "Add Source" button and select "Apify" from the list of available connectors.
8. In the "Connection Configuration" section, paste the token you copied from Apify into the "API Token" field.
9. Enter the name of the dataset you want to connect to in the "Dataset Name" field.
10. Click the "Test" button to ensure that the connection is successful.
11. If the test is successful, click the "Save" button to save your configuration.
12. You can now use the Apify source connector in Airbyte to extract data from your chosen dataset.

Step 2: Set up Convex as a destination connector

1. First, navigate to the Airbyte website and log in to your account.
2. Once you are logged in, click on the "Destinations" tab on the left-hand side of the screen.
3. From there, click on the "Add Destination" button in the top right corner of the screen.
4. In the search bar, type "Convex" and select the Convex destination connector from the list of options.
5. Next, you will need to enter your Convex API key. This can be found in your Convex account settings.
6. Once you have entered your API key, click on the "Test" button to ensure that the connection is working properly.
7. If the test is successful, click on the "Save" button to save your settings.
8. You can now use the Convex destination connector to transfer data from Airbyte to your Convex account.

Step 3: Set up a connection to sync your Apify Dataset data to Convex

Once you've successfully connected Apify Dataset as a data source and Convex as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Apify Dataset from the dropdown list of your configured sources.
Select your destination: Choose Convex from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Apify Dataset objects you want to import data from towards Convex. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Apify Dataset to Convex according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Convex data warehouse is always up-to-date with your Apify Dataset data.

Use Cases to transfer your Apify Dataset data to Convex

Integrating data from Apify Dataset to Convex provides several benefits. Here are a few use cases:

Advanced Analytics: Convex’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Apify Dataset data, extracting insights that wouldn't be possible within Apify Dataset alone.
Data Consolidation: If you're using multiple other sources along with Apify Dataset, syncing to Convex allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Apify Dataset has limits on historical data. Syncing data to Convex allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Convex provides robust data security features. Syncing Apify Dataset data to Convex ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Convex can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Apify Dataset data.
Data Science and Machine Learning: By having Apify Dataset data in Convex, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Apify Dataset provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Convex, providing more advanced business intelligence options. If you have a Apify Dataset table that needs to be converted to a Convex table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Apify Dataset account as an Airbyte data source connector.
Configure Convex as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Apify Dataset to Convex after you set a schedule

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!

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Sync with Airbyte

Step 1: Set up Apify Dataset as a source connector

Step 2: Set up Convex as a destination connector

Step 3: Set up a connection to sync your Apify Dataset data to Convex

Once you've successfully connected Apify Dataset as a data source and Convex as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Apify Dataset from the dropdown list of your configured sources.
Select your destination: Choose Convex from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Apify Dataset objects you want to import data from towards Convex. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Apify Dataset to Convex according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Convex data warehouse is always up-to-date with your Apify Dataset data.

How to Sync Apify Dataset to Convex Manually

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Step 8:

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.

What is Apify Dataset?

What data can you extract from Apify Dataset?

Apify's API provides access to a wide range of data types, including:

1. Web scraping data: Apify's web scraping tools allow users to extract data from websites and APIs, including HTML, JSON, XML, and CSV formats.
2. Social media data: Apify's API can be used to extract data from social media platforms such as Twitter, Facebook, and Instagram, including posts, comments, and user profiles.
3. E-commerce data: Apify's API can be used to extract data from e-commerce platforms such as Amazon, eBay, and Shopify, including product listings, prices, and reviews.
4. Search engine data: Apify's API can be used to extract data from search engines such as Google, Bing, and Yahoo, including search results, rankings, and keyword data.
5. Financial data: Apify's API can be used to extract financial data from sources such as stock exchanges, financial news websites, and investment platforms.
6. Weather data: Apify's API can be used to extract weather data from sources such as weather APIs and weather news websites.
7. Government data: Apify's API can be used to extract data from government websites and APIs, including census data, crime statistics, and public records.

Overall, Apify's API provides access to a wide range of data types, making it a powerful tool for data extraction and analysis.

How do I transfer data from Apify Dataset?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Apify to Convex as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Apify to Convex and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

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How to load data from Apify Dataset to Convex

Learn how to use Airbyte to synchronize your Apify Dataset data into Convex within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Apify Dataset

What is Convex

Prerequisites

Step 1: Setup Apify Dataset

Step 2: Setup Convex

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Apify Dataset and Convex

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Apify Dataset as a source connector (using Auth, or usually an API key)
set up Convex as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Apify Dataset

What is Convex

Integrate Apify Dataset with Convex in minutes

Try for free now

Prerequisites

A Apify Dataset account to transfer your customer data automatically from.
A Convex account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up Apify Dataset as a source connector

Step 2: Set up Convex as a destination connector

Step 3: Set up a connection to sync your Apify Dataset data to Convex

Once you've successfully connected Apify Dataset as a data source and Convex as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Apify Dataset from the dropdown list of your configured sources.
Select your destination: Choose Convex from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Apify Dataset objects you want to import data from towards Convex. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Apify Dataset to Convex according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Convex data warehouse is always up-to-date with your Apify Dataset data.

Use Cases to transfer your Apify Dataset data to Convex

Integrating data from Apify Dataset to Convex provides several benefits. Here are a few use cases:

Advanced Analytics: Convex’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Apify Dataset data, extracting insights that wouldn't be possible within Apify Dataset alone.
Data Consolidation: If you're using multiple other sources along with Apify Dataset, syncing to Convex allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Apify Dataset has limits on historical data. Syncing data to Convex allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Convex provides robust data security features. Syncing Apify Dataset data to Convex ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Convex can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Apify Dataset data.
Data Science and Machine Learning: By having Apify Dataset data in Convex, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Apify Dataset provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Convex, providing more advanced business intelligence options. If you have a Apify Dataset table that needs to be converted to a Convex table, Airbyte can do that automatically.