Salesforce and Amazon Web Services (AWS) are among the biggest names in technology. Odds are, your organization has contracts with one or both. But what are the differences between the two? And how do the two technologies interoperate? For example, once data has been generated or uploaded into Salesforce, how should it be moved into AWS services like AWS Redshift? Let’s dig into those questions below.
Automate Data Preparation from Salesforce to Redshift
Salesforce vs AWS
First, let’s review the primary differences between Salesforce and AWS. The two technologies serve very different functions, which are important to understand.
When most people think of Salesforce, they think of its customer relationship management (CRM) software, Salesforce Sales Cloud (though that’s not all the company offers). A CRM serves the critical function of tracking the entirety of an organization’s customer interactions, whether those customers be actual or potential. It is a Software as a Service (SaaS), which means, like all SaaS applications, Salesforce Sales Cloud is self-managed and allows users to get up and running right away—no set-up time required. In addition, Salesforce offers a range of other applications related to customer service, marketing automation, analytics, and application development, both on the SaaS and platform as a service (PaaS) side.
- AWS (Amazon Web Services)
Amazon Web Service (AWS) offers more than 100 infrastructure and platform services, making it both an infrastructure as a service (IaaS) and a platform as a service (PaaS). In addition, AWS Marketplace enables you to discover, buy, and launch dozens of SaaS and API products. AWS is one of the “big three” cloud computing platforms, which also includes Microsoft Azure and Google Cloud Platform.
The data storage options on the AWS platform include Amazon Simple Storage Service (Amazon S3), which is object storage, and AWS Redshift, a data warehouse built for business intelligence tools and familiar SQL-based clients using standard ODBC and JDBC connections.
In summary, Salesforce vs AWS can be thought of in this way: Salesforce is a leader in SaaS; AWS is a leader in IaaS. In fact, Salesforce currently uses AWS as its public cloud infrastructure provider.
Organizations look to AWS when they’re interested in purchasing infrastructure (computing power, data storage, etc.). Salesforce, on the other hand, has made its name in the business of customer service, whether that be customer databases, customer analytics, or custom-built applications.
Salesforce to Redshift
<span style=”font-weight: 400;”>It’s not uncommon for organizations to accumulate data in Salesforce Sales Cloud (CRM) and then wish to transfer that data to their data warehouse on AWS, AWS Redshift. Storing Salesforce data in AWS Redshift allows all of an organization’s valuable customer information—everything from contact information to product lists to annual or quarterly sales—to be enriched with other organizational data found in Redshift. It’s a key data strategy that can be found across many of today’s organizations: reduce as many data silos as possible in order to make way for greater data exploration and innovation. </span>
<span style=”font-weight: 400;”>But how, exactly, does one move data from Salesforce to Redshift? Though there are a number of applications that exist to solve this problem, let’s look at two examples: </span><span style=”font-weight: 400;”>
<li style=”font-weight: 400;”><i><span style=”font-weight: 400;”>Extract, Transform, and Load (ETL) </span></i><i><span style=”font-weight: 400;”>
</span></i><span style=”font-weight: 400;”><span style=”font-weight: 400;”>In this scenario, engineering teams write custom ETL scripts in order to get data from Salesforce’s API to S3 and then to Redshift. Organizations must be prepared to both maintain infrastructure for this process and monitor the scripts on an on-going basis.</span></span>
This method dates back to the 80s and 90s and has been a tried-and-true method since because of the automation it brought to coding. Increasingly, however, organizations are moving on to the next phase(s) of ETL, which come in the form of all sorts of user-friendly interfaces that move and cleanse data without burdening engineering and IT teams.</li>
<li style=”font-weight: 400;”><i>Data preparation platform
</i>One such example of an alternate to ETL (at least in part) is a data preparation platform. Though data preparation can’t replace ETL in every scenario, moving data from Salesforce to Redshift is one such case where it can.Data preparation platforms allow users to visually understand how the data coming from Salesforce needs to be cleansed—whether that be outliers or errors—and leverage user-friendly techniques to aid the transformation process. In sum, it involves the people who know that Salesforce data best to transform it and move it into AWS Redshift.</li>
The Trifacta Data Preparation Platform
<span style=”font-weight: 400;”>With the Trifacta data preparation platform, users are only clicks away from storing clean and enriched data in Redshift for reporting. Learn more about the main benefits of using the Trifacta platform to move data from Salesforce to Redshift: </span>
<li><i><span style=”font-weight: 400;”>Faster Than Code</span></i><span style=”font-weight: 400;”>
</span><span style=”font-weight: 400;”>Trifacta’s advanced profiling capabilities highlight common issues and discrepancies with Salesforce data like anomalies, inconsistent data and duplicate data in seconds.</span><span style=”font-weight: 400;”>
</span><span style=”font-weight: 400;”>
</span><span style=”font-weight: 400;”>Trifacta’s interface detects metadata about the data being ingested from Salesforce and automatically recognizes the schema, data types, missing values and outliers. When joining different datasets, Trifacta will infer potential join keys across the tables, enabling you to get to value faster.</span> </li>
<li style=”font-weight: 400;”><i><span style=”font-weight: 400;”>Automation at Scale
</span></i>Refresh your reports with new incoming data using Trifacta’s powerful automation capabilities. When a scheduled job is triggered, data gets automatically pulled from Salesforce and transformed on a recurring basis.With webhook notifications you can also trigger downstream processes on successful completion of the scheduled job, or send out email notifications to yourself or others in your company. With dynamic inputs you can also select which records Trifacta should pick up and which should be skipped in every execution.</li>
<li style=”font-weight: 400;”><i>Transformation Suggestions
</i>Trifacta’s proprietary machine learning algorithm interprets how you want to transform data you select and provides you with a ranked set of suggestions and patterns that accelerate your Salesforce data preparation efforts.</li>
Salesforce and AWS are both vital technologies. But creating a data pipeline between the two isn’t always an easy task. Data preparation platforms are one of the ways that organizations are doing this more easily, intelligently, and with less maintenance and costs.