So, you’re a Salesforce user, right? It’s always a pleasure talking to someone who gives utmost priority to the customer experience and behavior. Being focused on optimizing marketing & sales channels based on customer engagement is what makes you a great player. 

Salesforce has consolidated dashboards based on multiple reports of customer engagement, behavior, and support data. But, there would be times when this data needs to be integrated with that of other functional teams. That’s where you come in. You take the responsibility of replicating data from Salesforce to a centralized repository so that analysts and key stakeholders can make super-fast business-critical decisions.

Well, you’ve landed in the right place! We’ve prepared a simple and straightforward guide to help you replicate data from Salesforce to Databricks. Read the 2 simple methods to understand the replication process quickly.

Replicate Data from Salesforce to Databricks Using CSV Files

To replicate data from Salesforce to Databricks, you can do either of the following:

  • Use CSV files or 
  • A no-code automated solution

We’ll cover replication via CSV files next. Learn about Salesforce Connect.

Replicate Data from Salesforce to Databricks Using CSV Files

You can carry forward with the following steps to understand the replication process of replicating data from Salesforce to Databricks in CSV format:

Step 1: Export CSV Files From Salesforce Using the Data Loader Export Wizard

  • Log in to your Salesforce application. Then, go to setup.
  • Under Administer section, select the “Data Management”, following which click on the “Data Loader” option.
  • Choose the appropriate version for your computer and download the file. Then, download Data Loader for Windows or Mac.
  • Double-click the saved file to install it on your PC.
  • Launch the Salesforce Data Loader export wizard. Then click on the “Export” button.
  • Log in to Salesforce using your credentials. Then click on the “Next” button.
  • Select the object you want to export. For example, you can select the Leads object. To view an expanded view of all the objects available for export, select the “Show all objects” button. 
  • Create the CSV file to export the data to and click the “Next” button.
  • Create a SOQL (Salesforce Object Query Language) query for the data export. For example, for Lead data, you can select First Name, Last Name, Address, Email, Mobile, City, Lead Source, Lead Status, Campaign, Company, and Title in the query fields, and click Finish. As you follow the next steps, the CSV viewer displays all the lead names and the corresponding fields. 

Step 2: Import CSV Files into Databricks

  • In the Databricks UI, go to the side navigation bar. Click on the “Data” option. 
  • Now, you need to click on the “Create Table” option.
  • Then drag the required CSV files to the drop zone. Otherwise, you can browse the files in your local system and then upload them.

Once the CSV files are uploaded, your file path will look like: /FileStore/tables/<fileName>-<integer>.<fileType>

Step 3: Modify & Access the Data

  • Click on the “Create Table with UI” button.
  • The data now gets uploaded to Databricks. You can access the data via the Import & Explore Data section on the landing page.
  • To modify the data, select a cluster and click on the “Preview Table” option.
  • Then, change the attributes accordingly and select the “Create Table” option.

With this 3-step approach, you can easily replicate data from Salesforce to Databricks using CSV files. This method is optimal for the following scenarios:

  • Low-frequency Data Replication: When your marketing team needs the Salesforce data only once in a long period, i.e., monthly, quarterly, yearly, or just once. 
  • Limited Data Transformation Options: Manually transforming data in CSV files is difficult & time-consuming if this needs to be done on a regular basis. Hence, it is ideal if the data in your spreadsheets is clean, standardized, and present in an analysis-ready form. 
  • Dedicated Personnel: If your organization has dedicated people who have to perform the manual downloading and uploading of CSV files, then accomplishing this task is not much of a headache.

When the frequency of replicating data from Salesforce increases, this process becomes highly monotonous. It adds to your misery when you have to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

Rather, you should be focussing on more productive tasks. Being bombarded with constant requests to add custom ETL connectors and spending most of your time repairing the pipeline might not be the best use of your time. Learn more about Salesforce Connect.

To start reclaiming your valuable time, you can…

Replicate Data from Salesforce to Databricks Using an Automated ETL Tool

Going all the way to write custom scripts for every new data connector request is not the most efficient and economical solution. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare.

You can streamline the Salesforce to Databricks data integration process by opting for an automated tool. Here are the benefits of leveraging an automated no-code tool:

  • It allows you to focus on core engineering objectives while your business teams can jump on to reporting without any delays or data dependency on you.
  • Your sales & support teams can effortlessly enrich, filter, aggregate, and segment raw Salesforce data with just a few clicks.
  • The beginner-friendly UI saves the engineering team hours of productive time lost due to tedious data preparation tasks.
  • Without coding knowledge, your analysts can seamlessly create thorough reports for various business verticals to drive better decisions. 
  • Your business teams get to work with near-real-time data with no compromise on the accuracy & consistency of the analysis. 
  • You get all your analytics-ready data in one place. With this, you can quickly measure your business performance and deep dive into your Salesforce data to explore new market opportunities.

For instance, here’s how Hevo Data, a cloud-based ETL tool, makes Salesforce to Databricks data replication ridiculously easy:

Step 1: Configure Salesforce as a Source

Configuring Salesforce as a source while replicating data from Salesforce to Databricks
Image Source

Step 2: Configure Databricks as a Destination

Configuring Databricks as a destination while replicating data from Salesforce to Databricks
Image Source

All Done to Setup Your ETL Pipeline

After implementing the 2 simple steps, Hevo Data will build the pipeline for replicating data from Salesforce to Databricks based on your inputs while configuring the source and the destination.

The pipeline will automatically replicate new and updated data from Salesforce to Databricks every 15 mins (by default). However, you can also adjust the data replication frequency as per your requirements.

Data Pipeline Frequency

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
15 Mins15 Mins24 Hrs1-24

For in-depth knowledge of how a pipeline is built & managed in Hevo Data, you can also visit the official documentation for Salesforce as a source and Databricks as a destination.

You don’t need to worry about security and data loss. Hevo’s fault-tolerant architecture will stand as a solution to numerous problems. It will enrich your data and transform it into an analysis-ready form without having to write a single line of code.

By employing Hevo to simplify your data integration needs, you can leverage its salient features:

  • Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
  • Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
  • Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision-making. 
  • Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
  • Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
  • Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
  • Live Support: The support team is available round the clock to extend exceptional customer support through chat, email, and support calls.

Take our 14-day free trial to experience a better way to manage data pipelines.

Get started for Free with Hevo!

What Can You Achieve by Migrating Your Data from Salesforce to Databricks?

Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from Salesforce to Databricks. Does your use case make the list?

  • Customers acquired from which channel have the maximum satisfaction ratings?
  • How does customer SCR (Sales Close Ratio) vary by Marketing campaign?
  • How many orders were completed from a particular Geography?
  • How likely is the lead to purchase a product?
  • What is the Marketing Behavioural profile of the Product’s Top Users?

Summing It Up

Exporting and importing CSV files would be the smoothest process when your sales, support & marketing teams require data from Salesforce only once in a while. But what if the sales, support & marketing teams request data from multiple sources at a high frequency? Would you carry on with this method of manually importing & exporting CSV files from every other source? In this situation, wouldn’t you rather focus on something more productive? You can stop spending so much time being a ‘Big Data Plumber’ by using a custom ETL solution instead.

A custom ETL solution becomes necessary for real-time data demands such as monitoring campaign performance or viewing the recent user interaction with your product or marketing channel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources).

Visit our Website to Explore Hevo Data

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form. 

Want to take Hevo Data for a ride? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of replicating data from Salesforce to Databricks! Let us know in the comments section below!

Manisha Jena
Research Analyst, Hevo Data

Manisha Jena is a data analyst with over three years of experience in the data industry and is well-versed with advanced data tools such as Snowflake, Looker Studio, and Google BigQuery. She is an alumna of NIT Rourkela and excels in extracting critical insights from complex databases and enhancing data visualization through comprehensive dashboards. Manisha has authored over a hundred articles on diverse topics related to data engineering, and loves breaking down complex topics to help data practitioners solve their doubts related to data engineering.

No-code Data Pipeline for Databricks