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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).
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
Salesforce's API provides access to a wide range of data types, including:
1. Accounts: Information about customer accounts, including contact details, billing information, and purchase history.
2. Leads: Data on potential customers, including contact information, lead source, and lead status.
3. Opportunities: Information on potential sales deals, including deal size, stage, and probability of closing.
4. Contacts: Details on individual contacts associated with customer accounts, including contact information and activity history.
5. Cases: Information on customer service cases, including case details, status, and resolution.
6. Products: Data on products and services offered by the company, including pricing, availability, and product descriptions.
7. Campaigns: Information on marketing campaigns, including campaign details, status, and results.
8. Reports and Dashboards: Access to pre-built and custom reports and dashboards that provide insights into sales, marketing, and customer service performance.
9. Custom Objects: Ability to access and manipulate custom objects created by the organization to store specific types of data.
Overall, Salesforce's API provides access to a comprehensive set of data types that enable organizations to manage and analyze their customer relationships, sales processes, and marketing campaigns.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
