TL;DR

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 Salesforce as a source connector (using Auth, or usually an API key)
2. set up AWS Datalake as a destination connector
3. 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 Salesforce

Salesforce is a cloud-based customer relationship management (CRM) platform providing business solutions software on a subscription basis. Salesforce is a huge force in the ecommerce world, helping businesses with marketing, commerce, service and sales, and enabling enterprises’ IT teams to collaborate easily from anywhere. Salesforces is the force behind many industries, offering healthcare, automotive, finance, media, communications, and manufacturing multichannel support. Its services are wide-ranging, with access to customer, partner, and developer communities as well as an app exchange marketplace.

What is AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

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Prerequisites

1. A Salesforce account to transfer your customer data automatically from.
2. A AWS Datalake account.
3. 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 Salesforce and AWS Datalake, for seamless data migration.

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

Methods to Move Data From Salesforce to aws datalake

• Method 1: Connecting Salesforce to aws datalake using Airbyte.
• Method 2: Connecting Salesforce to aws datalake manually.

Method 1: Connecting Salesforce to aws datalake using Airbyte

Step 1: Set up Salesforce as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.

2. Click on the "Salesforce" source connector and select "Create new connection."

3. Enter a name for your connection and click "Next."

4. Enter your Salesforce credentials, including your username, password, and security token.

5. Click "Test connection" to ensure that your credentials are correct and that Airbyte can connect to your Salesforce account.

6. Once the connection is successful, select the objects you want to replicate from Salesforce.

7. Choose the replication frequency and any other settings you want to apply to your connection.

8. Click "Create connection" to save your settings and start replicating data from Salesforce to Airbyte.

9. You can monitor the progress of your replication in the "Connections" tab and view the data in the "Dashboard" tab.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your Salesforce data to AWS Datalake

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

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Salesforce from the dropdown list of your configured sources.
3. Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
4. 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.
5. Select the data to sync: Choose the specific Salesforce objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
6. 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.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Salesforce to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your Salesforce data.

Method 2: Connecting Salesforce to aws datalake manually

Moving data from Salesforce to an AWS Data Lake without using third-party connectors or integrations involves several steps. You'll need to leverage the Salesforce APIs to extract data and use AWS services to store and manage the data in the Data Lake. Below is a step-by-step guide to accomplish this task:

Step 1: Plan Your Data Extraction from Salesforce

1. Identify the data you want to move to the AWS Data Lake.

2. Understand the Salesforce object model and determine which objects and fields you need.

3. Plan for the volume of data and the frequency of updates (full load vs incremental load).

Step 2: Set Up AWS Environment

1. Create an AWS account if you don't already have one.

2. Set up an S3 bucket where the extracted data will be stored.

   - Navigate to the Amazon S3 console and create a new bucket.

   - Configure the bucket settings according to your security and compliance needs.

3. Set up the necessary IAM roles and policies to securely access the S3 bucket.

4. Determine how you will manage the data within the Data Lake (e.g., AWS Glue, AWS Lake Formation).

Step 3: Extract Data from Salesforce

1. Create a Connected App in Salesforce to enable API access.

   - Go to the Salesforce Setup page.

   - Navigate to App Manager and create a new Connected App.

   - Configure OAuth settings for API integration.

   - Take note of the Consumer Key and Consumer Secret.

2. Authenticate with Salesforce using OAuth 2.0 to obtain an access token.

3. Use Salesforce REST API or Bulk API to extract data.

   - Choose REST API for real-time, small volume data extraction.

   - Choose Bulk API for large volume data extraction or batch processing.

4. Write a script (e.g., in Python or Java) that makes API calls to Salesforce to retrieve the data.

   - Handle pagination and API rate limits.

   - Optionally, you can use Salesforce's SOQL to query the data you need.

Step 4: Transfer Data to AWS S3

1. Format the extracted data as CSV, JSON, or Parquet files.

2. Use AWS SDK (e.g., boto3 for Python) in your script to upload the files to the S3 bucket.

   - Ensure the AWS SDK is configured with the correct IAM credentials.

   - Use the `put_object` or `upload_file` method to upload files to S3.

3. Implement error handling and logging to track the upload process.

Step 5: Manage Data in AWS Data Lake

1. Set up AWS Glue to catalog the data in S3.

   - Define a crawler to scan the S3 bucket and create metadata tables in the AWS Glue Data Catalog.

2. Use AWS Lake Formation for fine-grained access control and data governance if necessary.

3. Optionally, set up data transformation jobs in AWS Glue to prepare the data for analysis.

Step 6: Schedule and Automate the Data Pipeline

1. Use AWS Lambda to automate the extraction and loading process.

   - Write a Lambda function that triggers the data extraction script.

   - Set up the necessary triggers (e.g., scheduled events using Amazon EventBridge).

2. Monitor the data pipeline using Amazon CloudWatch to log and track the pipeline's performance and issues.

Step 7: Test and Validate

1. Perform a test run of the entire process to ensure that data is correctly extracted from Salesforce and loaded into the S3 bucket.

2. Validate the data in AWS to ensure completeness and integrity.

3. Monitor the system for a period to ensure it operates as expected.

Step 8: Documentation and Maintenance

1. Document the entire process, including API endpoints, data mappings, and the AWS setup.

2. Plan for regular maintenance and updates to the scripts and AWS configurations as needed.

Step 9: Security and Compliance

1. Ensure that the data transfer complies with all relevant data protection regulations.

2. Encrypt sensitive data both in transit and at rest.

3. Regularly review IAM roles and policies to adhere to the principle of least privilege.

This guide outlines a general approach to moving data from Salesforce to an AWS Data Lake without third-party connectors. The specific implementation details may vary based on the data's complexity and the AWS services you choose to use. Always test your implementation thoroughly to ensure data accuracy and security.

Use Cases to transfer your Salesforce data to AWS Datalake

Integrating data from Salesforce to AWS Datalake provides several benefits. Here are a few use cases:

1. Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Salesforce data, extracting insights that wouldn't be possible within Salesforce alone.
2. Data Consolidation: If you're using multiple other sources along with Salesforce, syncing to AWS Datalake 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.
3. Historical Data Analysis: Salesforce has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: AWS Datalake provides robust data security features. Syncing Salesforce data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Salesforce data.
6. Data Science and Machine Learning: By having Salesforce data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Salesforce provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a Salesforce table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Salesforce account as an Airbyte data source connector.
2. Configure AWS Datalake as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Salesforce to AWS Datalake 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!