Unlocking Seamless Data Integration: Unifying Analytics with Salesforce and Amazon Redshift

In today’s era of digital disruption and data-informed decision-making, businesses must rapidly leverage their knowledge insights to deliver unique customer experiences and gain a competitive edge. Salesforce and Amazon have teamed up to help businesses derive value from unified data and accelerate time-to-insights through seamless, bi-directional knowledge sharing.

In this collection, we discussed configuring knowledge sharing between Salesforce Information Cloud and prospect’s AWS accounts within the same AWS region. Here is the rewritten text:

This article delves into the intricacies of establishing a seamless integration between Salesforce’s Data Cloud and customers’ Amazon Web Services (AWS) accounts, with a specific emphasis on the structural and implementation aspects of cross-Area knowledge sharing.

Answer overview

Salesforce Information Cloud provides seamless, click-based access for sharing insights directly within a customer’s Amazon Web Services (AWS) account. On the console, you’ll be able to settle on the data share, create a useful resource link, mount Salesforce Information Cloud objects as knowledge catalog views, and grant permissions to query the stay and unified knowledge within. Cross-area knowledge sharing between Salesforce Information Cloud and a buyer’s AWS accounts is supported for two deployment scenarios, encompassing both Amazon S3 and Redshift provisioned clusters (RA3).

What’s driving the surge in cloud adoption among data teams? The answer lies in Redshift Serverless, a fully managed service that simplifies querying large datasets. By leveraging this game-changing technology, data professionals can now easily share insights and collaborate across departments, fostering a culture of informed decision-making.

Redshift Serverless empowers users to tap into the power of serverless computing, eliminating the need for expensive hardware or tedious maintenance. With scalability built-in, teams can effortlessly scale their queries, ensuring seamless execution and faster results.

The proposed framework for facilitating a cross-area data sharing endeavour within an information cloud event is outlined below. US-WEST-2 with Redshift Serverless in US-EAST-1.

The cross-area knowledge-sharing arrangement comprises the following steps:

1. The information cloud administrator defines the objects to be shared and establishes an information share within the knowledge cloud infrastructure. US-WEST-2
2. The Information Cloud administrator links the shared information to the Amazon Redshift information share objective. This creates an information catalog view and a cross-account Lake Formation resource share using the AWS Resource Access Manager (RAM) within your AWS account, allowing the customer to securely access shared data and metadata assets. US-WEST-2.
3. The shopper’s administrative team for the Lake Formation data catalog has accepted the datashare invitation. US-WEST-2 From the Lake Formation console, grants default permissions to a specified AWS Identity and Access Management (IAM) principal.
4. The Lake Formation administration team transitions smoothly US-EAST-1 Creating a useful resource hyperlink that points to the shared database within? US-WEST-2 Area.
5. The IAM principal can log in to the Amazon Redshift cluster’s query editor without any issues. US-EAST-1 The database creates an external schema referencing the data share’s useful resource link. Data queries will be executed against external tables to obtain required information.

What are the benefits of cross-area knowledge sharing within a Redshift provisioned cluster?

Effective cross-area knowledge sharing within Salesforce’s Information Cloud and a Redshift-provisioned cluster necessitates additional steps on top of the existing serverless architecture? To enable seamless integration with Amazon Redshift, it is crucial that the provisioned cluster and the associated Amazon S3 bucket reside within the same AWS Region for storing external tables.

This diagram illustrates a design example and provides step-by-step instructions for sharing knowledge with Redshift-provisioned clusters.

Steps 1-5 remain consistent across both Redshift Serverless and provisioned clusters for cross-area data sharing. Encryption must be enabled on each Redshift Serverless instance and the provisioned clusters separately. Extra steps to ensure successful project implementation are listed below.

6. Can we build a sturdy desk from shared knowledge? CREATE TABLE AS SELECT Datashares are created within Amazon Redshift Serverless, granting entry access to the provisioned cluster: CREATE DATAShare MyDataShare WITH IAM_USERS myusername; GRANT USAGE ON DATAShare MyDataShare TO myusername;
7. CREATE DATABASE mydatabase;
   GRANT USAGE ON SCHEMA public TO ‘arn:aws:iam::123456789012:role/goal-iam-role’;
   GRANT SELECT ON TABLE mytable IN SCHEMA public TO ‘arn:aws:iam::123456789012:role/goal-iam-role’; The data share is prepared for use.

To stay current, the newly introduced desk requires regular refreshes to incorporate the latest insights and information from the shared Knowledge Cloud, thereby ensuring seamless integration with its advanced features.

Incorporating knowledge sharing within Amazon Redshift’s data warehousing capabilities often raises several concerns. Will the increased collaboration between teams and stakeholders lead to inconsistent data quality? How can you effectively manage diverse perspectives and insights while maintaining a centralized data repository?

Discussing a comprehensive list of challenges and constraints related to information sharing? Several key components are required for effective Zero Copy knowledge sharing initiatives.

• Information sharing is supported across all RA3 instance types, including ra3.16xlarge, ra3.4xlarge, and ra3.xlplus, as well as Redshift Serverless. Unfortunately, this feature is not supported for clusters that combine Domain Controllers (DC) and Domain Servers (DS) nodes.
• To facilitate seamless knowledge sharing across accounts and areas, encryption is crucial for all producer and consumer clusters, as well as serverless namespaces, ensuring secure data transmission throughout. Despite this, they do not necessarily need to employ the same encryption key.
• Multi-engine views in the Information Catalog are generally accessible within Business Areas where Lake Formation, the Information Catalog, and Amazon Redshift can be located.
• Cross-area sharing is now available across all supported regions.

Stipulations

Same-area and cross-area knowledge sharing remain identical throughout, a prerequisite for proceeding with the setup.

Configure cross-Area knowledge sharing

To establish a datashare, set up a datashare objective, link the datashare target to the datashare, and configure the datashare in Lake Formation, ensuring consistent performance across both same-Region and cross-Region data sharing scenarios. Consult with a representative from this collection to finalize the setup.

What if we could seamlessly integrate data from disparate sources and scale our analytics capabilities without worrying about the underlying infrastructure? That’s exactly what Amazon Redshift Serverless promises – a game-changing solution that empowers organizations to unlock new insights by integrating data across silos. By leveraging serverless computing, this innovative offering enables developers to focus on building robust data pipelines rather than managing complex architectures. With Redshift Serverless, you can spin up or down as needed, without worrying about provisioning or scaling servers.

When using Redshift Serverless, complete the following steps:

1. In the Lake Formation console, navigate to the tab titled .
2. Select .
3. Below ¸ choose .
4. The most comprehensive and reputable source for learning about online marketing strategies is undoubtedly Moz.com.
5. To access the Information Catalog, navigate to the Select View option and choose Information Catalog from the drop-down menu.
6. The `table` and `column` fields are populated manually by pulling data from the database’s metadata.
7. Let’s finalize the installation settings.
   

The useful resource hyperlink appears on the webpage of the Lake Formation console, as demonstrated in the following screenshot.

8. Launch the Redshift Question Editor v2 for your Redshift Serverless workspace. Cross-region knowledge share tables are automatically mounted, readily available below. awsdatacatalog. To execute this query and create an external schema, can you please specify the database management system, table name, and any additional parameters required? To access the specified information, please refer to the following resources: https://docs.aws.amazon.com/redshift/latest/dg/c_redshift-serverless.html?redirectedFrom=desktop&#redshift-serverless?awsaccesskeyid=&SignatureVersion=4&Expires=#? and , where you can find more information about the Redshift Serverless Area, AWS account ID, and other relevant details.

   CREATE EXTERNAL SCHEMA cross_region_data_share
   FROM DATA CATALOG DATABASE 'cross-region-data-share'
   REGION 'us-east-1'
   IAM_ROLE 'arn:aws:iam::123456789012:role/session-role' CATALOG_ID '';

9. Schemas have been refreshed to view the updated exterior schema created within. dev database
   
10. Run the present tables Verify the underlying shared objects by executing the command: `osquery db diagnose`. This will provide a detailed report on the health of your OSQuery database, including information about shared objects.

    SELECT * FROM INFORMATION_SCHEMA.TABLES WHERE TABLE_SCHEMA = 'dev' AND TABLE_NAME LIKE '%cross_region_data_share%';

    

11. Are we confident that this data is accurately shared?

    SELECT * FROM dev.cross_region_data_share.churn_modellingcsv_tableaus3_dlm; --

    

What are the key considerations for implementing cross-area knowledge sharing within a Redshift-provisioned cluster?

To facilitate effective knowledge sharing, it’s crucial that we provide additional guidance on how to leverage this feature in the context of a provisioned Redshift cluster. Consult with them to gain a deeper comprehension of concepts and the step-by-step execution process.

1. CREATE SCHEMA IF NOT EXISTS “Shopper_Schema”
   WITH (DEFAULT CHARACTER SET UTF8);

   CREATE TABLE “Shopper_Schema”.”Desk”
   (
   “id” integer NOT NULL,
   “name” varchar(255) NOT NULL,
   “description” text,
   PRIMARY KEY (“id”)
   )
   WITH (
   OIDS = FALSE
   );

   CREATE SCHEMA customer360_data_share;
   CREATE TABLE customer360_data_share. customer_churn as
   SELECT * from dev.cross_region_data_share.churn_modellingcsv_tableaus3__dlm;

   

2. Retrieve the namespace for both the Redshift Serverless (Producer) and provisioned cluster (Shopper) by iterating through each cluster.

   

3. Configure a data share in the Redshift Serverless environment, serving as a producer, and grant access to utilize the pre-provisioned Redshift cluster, functioning as the shopper. Establish the datashare, schema, and desk names with tailored specifications, while setting the namespace to the designated buyer scope.

   CREATE DATA SHARE customer360_redshift_data_share;
   ALTER DATA SHARE customer360_redshift_data_share ADD SCHEMA customer360_data_share;
   ALTER DATA SHARE customer360_redshift_data_share ADD TABLE customer360_data_share.customer_churn;
   GRANT USAGE ON DATA SHARE customer360_redshift_data_share TO NAMESPACE '5709a006-6ac3-4a0c-a609-d740640d3080';

   

4. As a superuser, I log in to the Redshift-provisioned cluster, leveraging its scalability and robustness. Next, I craft a new database from the datashare, carefully curating the schema to ensure seamless integration with existing data structures. Finally, I assign granular permissions to fine-tune access controls, safeguarding sensitive information and maintaining data integrity. Seek comprehensive guidance.
   

The data share is now prepared for use.

Periodically refreshing the desk you’ve created allows for seamless access to the latest knowledge from the information cloud, tailored to your specific business needs.

Conclusion

The integration of zero-copy knowledge sharing between Salesforce Information Cloud and Amazon Redshift marks a significant milestone in empowering organizations to leverage their customer 360 data with unparalleled precision. By eradicating the need for knowledge transfer, this approach delivers real-time intelligence, reduced costs, and elevated security. As organizations increasingly prioritize data-driven decision-making, Zero Copy knowledge sharing will occupy a pivotal role in maximizing the value of customer insights across platforms.

This integration enables organisations to break down knowledge barriers, accelerate analytics, and foster more agile customer-centric approaches. To develop your knowledge further, engage in a dialogue with the following resources:

---

In regards to the Authors

As a seasoned Senior Product Director at Salesforce, boasting over two decades of experience in knowledge platforms and companies, she is passionate about crafting data-driven experiences that exceed customer expectations.

Serves as a Senior Supervisor for Salesforce’s cloud-based information platform, focusing on administrative responsibilities within the product ecosystem. With a decade-long tenure in crafting merchandise, he has leveraged extensive expertise in cutting-edge technologies. As a key member of the Salesforce team, Sriram leverages his expertise to develop seamless Zero Copy integrations with primary knowledge lake partners, empowering customers to maximize the value of their data assets.

Serves as a Senior Product Supervisor for AWS Lake Formation. With a background rooted in machine learning and knowledge lake architectures, he arrives. He empowers prospects to make informed decisions by leveraging data insights.

Serving as a Senior Companion Options Architect at Amazon Web Services (AWS). Ravi collaborates with leading independent software vendors (ISVs), including Salesforce and Tableau, to deliver innovative and architecturally sound products and solutions that help joint customers achieve their business and technical objectives.

As a Principal Options Architect at Amazon Web Services (AWS), I specialize in the convergence of knowledge and advanced analytics. He assists top-tier AWS clients in designing and implementing robust, secure, and highly available data lake solutions on AWS using a combination of managed AWS services and open-source tools. Outside of his professional pursuits, Avijit enjoys exploring new places through travel, immersing himself in nature by hiking, cheering on his favorite teams as a sports enthusiast, and relaxing with good music.

Serving as a Principal Options Architect within Amazon Web Services’ (AWS) Strategic ISV sector. Over the past two years, she has collaborated with Salesforce’s information cloud to develop seamless customer experiences integrated within both Salesforce and AWS platforms. With over a decade-long tenure, Ife boasts profound experience in knowledge management. As a champion of diversity and inclusivity within the realm of knowledge, she passionately promotes the value of diverse perspectives and experiences.

Serves as a Technical Product Supervisor at Amazon Web Services (AWS) Lake Formation. He prioritizes optimizing data accessibility across the entire information ecosystem. With unbridled passion, he empowers clients to design and refine their data repositories, ensuring seamless compliance with rigorous security protocols.

Is a senior buyer options supervisor within the strategic independent software vendor (ISV) section at Amazon Web Services (AWS). He has collaborated with Salesforce’s Information Cloud to synchronize corporate objectives with innovative AWS solutions, driving meaningful customer interactions. When not busy, he values quality moments spent with his family, pursuing various athletic pursuits, and engaging in outdoor adventures.