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Gen AI and Test Automation
19May

Gen AI and Test Automation

Generative AI has brought transformative advancements across industries, and test automation is no exception. By generating code, test scenarios, and even entire suites, Generative AI enables Software Development Engineers in Test (SDETs) to boost efficiency, expand test coverage, and improve reliability. 1. Enhanced Test Case Generation One of the biggest hurdles in test automation is generating diverse, comprehensive test cases. Traditional methods often miss edge cases or diverse scenarios. Generative AI, however, can analyze existing data and automatically generate extensive test cases, including potential edge cases that may not be apparent to human testers. Example: An SDET can use Generative AI to create test cases for a web application by feeding it requirements and user data. This enables the AI to produce hundreds of test cases, capturing diverse user behaviors and interactions that manual testers may overlook. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def generate_test_cases(application_description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Generate comprehensive test cases for the following application: {application_description}”, max_tokens=500 ) return response.choices[0].text app_description = “An e-commerce platform for browsing products, adding to cart, and checking out.” test_cases = generate_test_cases(app_description) print(test_cases) Sample Output: 2. Intelligent Test Script Creation Writing test scripts manually can be labor-intensive and error-prone. Generative AI can simplify this by generating test scripts based on an application’s flow, ensuring consistency and precision. Example: If an SDET needs to automate tests for a mobile app, they can use Generative AI to generate scripts for various scenarios, significantly reducing manual work. pythonCopy codeimport hypothetical_ai_test_tool ui_description = “”” Login Page: – Username field – Password field – Login button Home Page: – Search bar – Product listings – Add to cart buttons “”” test_scripts = hypothetical_ai_test_tool.generate_selenium_scripts(ui_description) Sample Output for test_login.py: pythonCopy codefrom selenium import webdriver from selenium.webdriver.common.keys import Keys def test_login(): driver = webdriver.Chrome() driver.get(“http://example.com/login”) username_field = driver.find_element_by_name(“username”) password_field = driver.find_element_by_name(“password”) login_button = driver.find_element_by_name(“login”) username_field.send_keys(“testuser”) password_field.send_keys(“password”) login_button.click() assert “Home” in driver.title driver.quit() 3. Automated Maintenance of Test Suites As applications evolve, maintaining test suites is critical. Generative AI can monitor app changes and update test cases automatically, keeping test suites accurate and relevant. Example: In a CI/CD pipeline, an SDET can deploy Generative AI to track code changes and update affected test scripts. This minimizes downtime and ensures tests stay aligned with application updates. pythonCopy codeimport hypothetical_ai_maintenance_tool def maintain_test_suite(): changes = hypothetical_ai_maintenance_tool.analyze_code_changes() updated_scripts = hypothetical_ai_maintenance_tool.update_test_scripts(changes) for script_name, script_content in updated_scripts.items(): with open(script_name, ‘w’) as file: file.write(script_content) maintain_test_suite() Sample Output:“Updating test_login.py with new login flow changes… Test scripts updated successfully.” 4. Natural Language Processing for Test Case Design Generative AI with NLP can interpret human language, enabling SDETs to create test cases from plain-language descriptions, enhancing collaboration across technical and non-technical teams. Example: An SDET can use an NLP-powered tool to translate a feature description from a product manager into test cases. This speeds up the process and ensures that test cases reflect intended functionality. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def create_test_cases(description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Create test cases based on this feature description: {description}”, max_tokens=500 ) return response.choices[0].text feature_description = “Allow users to reset passwords via email to regain account access.” test_cases = create_test_cases(feature_description) print(test_cases) Sample Output: 5. Predictive Analytics for Test Prioritization Generative AI can analyze historical data to prioritize high-risk areas, allowing SDETs to focus testing on critical functionalities. Example: An SDET can use predictive analytics to identify areas with frequent bugs, allocating resources more effectively and ensuring robust testing of high-risk components. pythonCopy codeimport hypothetical_ai_predictive_tool def prioritize_tests(): risk_areas = hypothetical_ai_predictive_tool.predict_risk_areas() prioritized_tests = hypothetical_ai_predictive_tool.prioritize_test_cases(risk_areas) return prioritized_tests prioritized_test_cases = prioritize_tests() print(“Prioritized Test Cases:”) for test in prioritized_test_cases: print(test) Sample Output: Gen AI and Test Automation Generative AI has the potential to revolutionize test automation, offering SDETs tools to enhance efficiency, coverage, and reliability. By embracing Generative AI for tasks like test case generation, script creation, suite maintenance, NLP-based design, and predictive prioritization, SDETs can reduce manual effort and focus on strategic tasks, accelerating testing processes and ensuring robust, reliable software systems. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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May 19, 2024in Data, Generative AI
AI Training Options
29Feb

AI Training Options

As AI adoption accelerates, AI certifications and courses have proliferated, providing deeper knowledge of this rapidly evolving technology. AI Training Options. Numerous AI certifications cover the basics, so we’ve narrowed the field to 10 of the most diverse and comprehensive programs. AI Training Options Artificial intelligence is poised to become the key technology that drives business transformation and gives companies a competitive edge. According to a recent forecast by the International Data Corporation, global spending on AI—including AI-enabled applications, infrastructure, and related services—will more than double to $632 billion by 2028, growing at a compound annual rate of 29% between 2024 and 2028. AI helps businesses boost productivity by automating processes such as robotics and autonomous vehicles, while also supporting existing workforces with technologies like assisted and augmented intelligence. Companies are integrating AI across various sectors, including finance, healthcare, retail, smart home devices, fraud detection, and security surveillance. Why AI certifications are important: 10 of the best AI certifications and courses: Each certification offers unique benefits, whether you’re a beginner or an experienced professional aiming to stay ahead in AI-driven industries. Content updated September 2024. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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February 29, 2024in Artificial Intelligence, Data, Generative AI, Google, Technology
Zapier for Salesforce
02Feb

Salesforce and Zapier and Anything You Can Imagine

Zapier enables you to connect Salesforce with thousands of the most popular third-party apps, so you can automate your work and have more time for what matters most—no code required. Providing easy automation for busy Salesforce users. Zapier transfers info between your web apps automatically, so you can focus on your most important work. Zapier is “middleware” that sits between two other services. It allows software services to create Triggers and Actions. Each combination of a Trigger and an Action is called a Zap. Triggers are how your app’s users can start automated workflows whenever an item is added or updated in your app. There are two types of triggers in Zapier: Actions are how your app’s use the data. The action is the effect of the cause (trigger). What is the difference between actions and triggers in Zapier? Every Zap starts with a single trigger that watches for new or updated data, starting the user’s workflow. Action steps then make use of that data. Zapier actions create or update a single item in your app through API calls that include multiple details from user customized input fields. Is Zapier considered an API? Yes, Zapier is an API tool. Zapier is a web-based automation tool that allows you to connect and automate different web services and applications without the need for any coding. It uses APIs (Application Programming Interfaces) to communicate with the different services and applications that it connects to. About AI by Zapier Smart task and text handling powered by a flexible AI. Create text prompts to perform standard tasks like extracting lead (or other) data or answering discrete questions like “where should this message be routed?”. Or, create your own! What coding language does Zapier use? Zapier is a monolithic application built on Python and Django. There’s a lot of momentum with Python and it is very difficult changing course to another programming language while the service is running. Why is Zapier so popular? Zapier is a powerful tool for automating workflows and combining different business applications without requiring any additional coding skills. Is Zapier actually useful? Users have found that Zapier is a versatile automation tool that simplifies complex problems and enhances efficiency in various industries. With its easy-to-use interface, users can create automation workflows and connect different apps and platforms seamlessly. Around the world in 2024, over 9973 companies have started using Zapier as Integration tool. Netflix, Inc. PayPal Holdings, Inc. Trimble, Inc. If you need assistance connecting apps to Salesforce with Zapier, contact Tectonic today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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February 2, 2024in Salesforce
Python Alongside Salesforce
17Sep

Python Alongside Salesforce

Salesforce can integrate with Python, though the platform primarily relies on its proprietary languages and frameworks for core development. Python, however, plays a crucial role in enhancing Salesforce’s capabilities through integrations, automation, data analysis, and extending functionalities via external applications. Here’s an overview of how Python works within the Salesforce ecosystem: 1. Salesforce’s Core Development Stack Before exploring Python’s use, it’s important to understand the key development tools within Salesforce: These tools are the foundation for Salesforce development. However, Python complements Salesforce by enabling integrations and automation that go beyond these native tools. 2. Python in Salesforce Integrations Python shines when integrating Salesforce with other systems, automating workflows, and extending functionality. Here’s how: a. API Interactions Salesforce’s REST and SOAP APIs allow external systems to communicate with Salesforce data. Python, with its powerful libraries, is excellent for interfacing with these APIs. Key Libraries: Example: Extracting Data via API: pythonCopy codefrom simple_salesforce import Salesforce # Connect to Salesforce sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) # Query Salesforce data accounts = sf.query(“SELECT Id, Name FROM Account LIMIT 10”) for account in accounts[‘records’]: print(account[‘Name’]) b. Data Processing and Analysis Python’s data manipulation libraries like Pandas and NumPy make it ideal for processing Salesforce data. Example: Data Cleaning and Analysis: pythonCopy codeimport pandas as pd from simple_salesforce import Salesforce # Connect to Salesforce sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) # Fetch data query = “SELECT Id, Name, AnnualRevenue FROM Account” accounts = sf.query_all(query) df = pd.DataFrame(accounts[‘records’]).drop(columns=[‘attributes’]) # Process data df[‘AnnualRevenue’] = df[‘AnnualRevenue’].fillna(0) high_revenue_accounts = df[df[‘AnnualRevenue’] > 1000000] print(high_revenue_accounts) 3. Automation and Scripting Python can automate Salesforce-related tasks, improving productivity and reducing manual effort. This can involve automating data updates, generating reports, or scheduling backups. Example: Automating Data Backup: pythonCopy codeimport schedule import time from simple_salesforce import Salesforce def backup_salesforce_data(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, Name, CreatedDate FROM Contact” contacts = sf.query_all(query) df = pd.DataFrame(contacts[‘records’]).drop(columns=[‘attributes’]) df.to_csv(‘contacts_backup.csv’, index=False) print(“Salesforce data backed up successfully.”) # Schedule the backup schedule.every().day.at(“00:00”).do(backup_salesforce_data) while True: schedule.run_pending() time.sleep(1) 4. Building External Applications Using platforms like Heroku, developers can build external applications in Python that integrate with Salesforce, extending its functionality for custom portals or advanced analytics. Example: Web App Integrating with Salesforce: pythonCopy codefrom flask import Flask, request, jsonify from simple_salesforce import Salesforce app = Flask(__name__) @app.route(‘/get_accounts’, methods=[‘GET’]) def get_accounts(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) accounts = sf.query(“SELECT Id, Name FROM Account LIMIT 10”) return jsonify(accounts[‘records’]) if __name__ == ‘__main__’: app.run(debug=True) 5. Data Integration and ETL Python is commonly used in ETL (Extract, Transform, Load) processes that involve Salesforce data. Tools like Apache Airflow allow you to create complex data pipelines for integrating Salesforce data with external databases. Example: ETL Pipeline with Airflow: pythonCopy codefrom airflow import DAG from airflow.operators.python_operator import PythonOperator from simple_salesforce import Salesforce import pandas as pd from datetime import datetime def extract_salesforce_data(): sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, Name, CreatedDate FROM Opportunity” opportunities = sf.query_all(query) df = pd.DataFrame(opportunities[‘records’]).drop(columns=[‘attributes’]) df.to_csv(‘/path/to/data/opportunities.csv’, index=False) default_args = { ‘owner’: ‘airflow’, ‘start_date’: datetime(2023, 1, 1), ‘retries’: 1, } dag = DAG(‘salesforce_etl’, default_args=default_args, schedule_interval=’@daily’) extract_task = PythonOperator( task_id=’extract_salesforce_data’, python_callable=extract_salesforce_data, dag=dag, ) extract_task 6. Machine Learning and Predictive Analytics Python’s machine learning libraries, such as Scikit-learn and TensorFlow, enable predictive analytics on Salesforce data. This helps in building models for sales forecasting, lead scoring, and customer behavior analysis. Example: Predicting Lead Conversion: pythonCopy codeimport pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from simple_salesforce import Salesforce # Fetch Salesforce data sf = Salesforce(username=’your_username’, password=’your_password’, security_token=’your_token’) query = “SELECT Id, LeadSource, AnnualRevenue, NumberOfEmployees, Converted FROM Lead” leads = sf.query_all(query) df = pd.DataFrame(leads[‘records’]).drop(columns=[‘attributes’]) # Preprocess and split data df = pd.get_dummies(df, columns=[‘LeadSource’]) X = df.drop(‘Converted’, axis=1) y = df[‘Converted’] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train model model = RandomForestClassifier(n_estimators=100, random_state=42) model.fit(X_train, y_train) # Evaluate accuracy accuracy = model.score(X_test, y_test) print(f”Model Accuracy: {accuracy * 100:.2f}%”) 7. Best Practices for Using Python with Salesforce To maximize the efficiency and security of Python with Salesforce: 8. Recommended Learning Resources By leveraging Python alongside Salesforce, organizations can automate tasks, integrate systems, and enhance their data analytics, all while boosting productivity. Content updated August 2024. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 17, 2022in Data
snowpark container services
08Feb

Snowpark Container Services

Snowpark Container Services is a fully managed solution designed to simplify deploying, managing, and scaling containerized applications within the Snowflake ecosystem. Key Features Benefits of Snowpark Container Services in Native Apps Integrating Snowpark Container Services into Snowflake Native Apps enables developers to create solutions for various use cases, including: Developers benefit from Snowflake’s Native Apps Framework safeguards, which ensure secure and efficient operations. Development Workflow for Snowpark Container Services Here’s a quick guide to the development cycle for building and deploying containerized services within Snowflake. 1. Develop Code Locally 2. Build and Push an Image 3. Provision Resources 4. Deploy and Test Services Getting Started To build containerized applications with Snowpark Container Services: For a deep dive into architecture and workflows, explore Snowflake’s guides and tutorials on Snowpark Container Services and Native Apps. The Snowflake Native Applications Framework allows developers to package Snowflake features into standalone applications that can be installed across multiple accounts. This framework now supports advanced features for managing resource provisioning and deploying container services. Below is an overview of how to integrate container services into a Native App. Key Components 1. Manifest File The manifest file defines the configuration and setup properties for the application, including: Example Manifest File: Specifying Container Images yaml Copy code manifest_version: 1 version: name: V1 label: “Version One” comment: “The first version of my amazing Snowflake Native App” artifacts: readme: readme.md setup_script: scripts/setup.sql container_services: images: – /provider_db/provider_schema/provider_repo/server:prod – /provider_db/provider_schema/provider_repo/web:1.0 Here: Setting a Web UI as the Landing Page For apps with a web UI, specify a default_web_endpoint field to make the UI the landing page. yaml Copy code default_web_endpoint: service: ux_schema.ux_service endpoint: ui Here, service corresponds to the name defined in the CREATE SERVICE command, and endpoint refers to the service’s endpoint in the specification file. Using Streamlit as a Landing Page Alternatively, you can specify a default_streamlit field to use Streamlit as the landing page. Note: default_web_endpoint and default_streamlit are mutually exclusive—you can only use one. 2. Setup Script The setup script executes during the application installation and upgrade processes. It contains SQL statements for creating: Resource Provisioning for Snowpark Container Services The setup script also includes logic for provisioning resources like compute pools. However, these provisioning procedures are executed post-installation to ensure the consumer has granted the necessary privileges. Managing Compute Pools and Privileges Consumer-Controlled Resource Provisioning A foundational principle of the Snowflake Native Apps Framework is that consumers maintain full control over the resources the application accesses. This includes compute resources. Privileges Required To enable an application to provision resources, specific privileges must be requested in the manifest file: Example Privileges in the Manifest File yaml Copy code privileges: – CREATE COMPUTE POOL description: “Enable application to create its own compute pool(s)” – BIND SERVICE ENDPOINT description: “Enables application to expose service endpoints” Note: These privileges are not granted by default. The installation user may need to request them from another user with the appropriate permissions within the organization. By wrapping container services into a Snowflake Native App, developers can: This approach ensures that both providers and consumers retain control over their resources and data, while maximizing the capabilities of Snowflake Native Apps. Managing Privileges and Deploying Services in Snowflake Native Apps Once a Snowflake Native App is installed, consumers can grant the necessary privileges through various supported mechanisms. Here’s an overview of how to configure privileges, deploy services, and manage app components effectively. Granting Privileges 1. Snowsight UI Configuration Snowflake Native Apps with Snowpark Container Services provide a custom Snowsight configuration UI. When privileges are granted via this UI, it triggers the app’s grant_callback procedure specified in the manifest file. This procedure receives a list of privileges as input, making it ideal for initiating resource provisioning, such as creating compute pools. For containerized apps, this is the recommended approach. Example Manifest Configuration: yaml Copy code manifest_version: 1 configuration: log_level: debug trace_level: always grant_callback: setup.grant_callback 2. Streamlit Integration Using Snowflake’s Permissions SDK, developers can create Streamlit-based pop-ups to request privileges. After privileges are granted through the pop-up, resource provisioning can begin. Specify Streamlit as the landing page in the manifest using the default_streamlit field. This configuration allows Streamlit to invoke the necessary resource provisioning procedures. 3. SQL Worksheet Consumers preferring SQL-based interaction can grant privileges and execute resource provisioning procedures directly through the SQL worksheet. Example Grant Callback Procedure: sql Copy code CREATE OR REPLACE PROCEDURE setup.grant_callback(privileges ARRAY) RETURNS STRING AS $$ BEGIN CREATE COMPUTE POOL IF NOT EXISTS backend_compute_pool MIN_NODES = 1 MAX_NODES = 1 INSTANCE_FAMILY = CPU_X64_XS; CALL services.start_backend(‘backend_compute_pool’); RETURN ‘Callback successful’; END; $$; GRANT USAGE ON PROCEDURE setup.grant_callback(ARRAY) TO APPLICATION ROLE app_public; This procedure creates a compute pool and then calls another procedure to set up services within the pool. Deploying Services Once compute pools are created, the application can deploy one or more services. Scenario 1: The App Has All Required Resources If the app has everything it needs, it can directly execute CREATE SERVICE commands in the grant_callback procedure to set up its services. For services requiring warehouses, the app can request the CREATE WAREHOUSE privilege in the manifest and create warehouses as needed. Scenario 2: The App Needs Additional Consumer Resources If the app requires additional objects (e.g., external access integrations or secrets) from the consumer, references can be used. A reference includes a register_callback procedure that binds the required resources. This procedure can also trigger service creation after the reference is bound. Example Register Callback Procedure: sql Copy code CREATE OR REPLACE PROCEDURE v1.register_single_callback(ref_name STRING, operation STRING, ref_or_alias STRING) RETURNS STRING LANGUAGE SQL AS $$ BEGIN CASE (operation) WHEN ‘ADD’ THEN SELECT SYSTEM$SET_REFERENCE(:ref_name, :ref_or_alias); CASE (:ref_name) WHEN ‘external_access_backend_reference’ THEN CALL services.start_service_with_egress(); WHEN ‘secret_reference’ THEN CALL services.start_service_using_secret(); END CASE; WHEN ‘REMOVE’, ‘CLEAR’ THEN SELECT SYSTEM$REMOVE_REFERENCE(:ref_name); ELSE RETURN ‘Unknown operation: ‘ || operation; END CASE; RETURN ‘Operation ‘ || operation || ‘ succeeds.’; END; $$; This procedure: Packaging Service Specifications Service creation requires specification files, which must be included in the app package (typically alongside the manifest.yml). These files define the service

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February 8, 2022in Data, Snowflake
Python Alongside Salesforce
17Jun

Salesforce and Python

Python is not essential for Salesforce, but it can be extremely useful for various tasks, such as data analysis, automation, and integration: Salesforce’s Primary Programming Languages Salesforce’s primary programming language is Apex, which is used for backend development. For the frontend, Visualforce is used to build the user interface. Apex is similar to Java and allows developers to embed business logic within system events. Automating Data Extraction for Analysis In many organizations, analysts often need to automate the process of extracting data from Salesforce objects to perform data analysis or build reports locally. Previously, analysts followed a manual and time-consuming process to create KPI reports, which included: This method was prone to errors and inefficiencies. Fortunately, Python scripts and Salesforce’s API can automate the entire process. Retrieving Salesforce Data Using Python To get started, install the Simple Salesforce package, an open-source Python library that enables easy interaction with Salesforce’s REST API. This allows users to either download pre-built reports directly from Salesforce or create custom datasets using SOQL (Salesforce Object Query Language). Set Up Your Python Package and API Access Accessing Salesforce Data There are two primary methods for retrieving Salesforce data into a Python DataFrame: Automating Data Processing and Reporting Once you have your data in a DataFrame, you can perform data wrangling and aggregation tasks. You can export the results to a Google Sheet using the Google Sheets API and the pygsheets package, or ingest the data into a database table. aFurthermore, you can schedule your Python script to run automatically using a tool like Apache Airflow, which will run the script at designated intervals, aggregate the data, and update dashboards with the latest information. This approach automates the entire reporting process, saving time and reducing the likelihood of errors in your workflow. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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June 17, 2021in Data, Google, Salesforce
Salesforce Heroku
11Oct

Heroku Explained

scale modern apps. Their platform is characterized by its elegance, flexibility, and user-friendly nature, providing developers with the simplest path to bring their apps to market.

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October 11, 2019in Data, Heroku, Salesforce
gettectonic.com