Japneet Sachdeva’s Post

Top 5 ways to setup Test Data in your Automation Frameworks for SDETs 1) Hard coded data (if data instance is very small) 2) Setup of Excel or CSV files for storing data (Obsolete) 3) Setup of JSON files (Modern) 4) Mocking data using API (Modern) 5 Database integration (Obsolete) Now let's dive into basic setup for each type: 1) Hard Coded: Entering test data directly into our Test Scripts or deriving it through some other function. But its coded directly within our files. Ex: driver.findElement(By.id("username")).sendKeys("testUser"); 2) Using Excel or CSV files: Read test data from CSV or Excel files using libraries like Apache POI or OpenCSV Ex: List<Object[]> data = new ArrayList<>(); String csvFile = "path/to/data.csv"; try (BufferedReader br = new BufferedReader(new FileReader(csvFile))) { String line; while ((line = br.readLine()) != null) { data.add(line.split(",")); } } catch (IOException e) { e.printStackTrace(); } return data.toArray(new Object[0][]); } 3) JSON files: Reading test data using JSON files, use libraries like Jackson, GSON etc. Ex: (Used Jackson) ObjectMapper objectMapper = new ObjectMapper(); try { User user = objectMapper.readValue(new File("path/to/user.json"), User.class); System.out.println(user.getUsername()); System.out.println(user.getPassword()); } catch (IOException e) { e.printStackTrace(); } } 4) Using APIs: Retrieving test data via APIs allows for dynamic and up-to-date test scenarios, particularly useful when dealing with databases Now in this we can use OKHTTP or Rest Assured as per your requirements. As this is little complex to implement as we need to data driver class for APIs & then use it in our Test Classes. I am providing example here: https://shorturl.at/HIGP2 5) Fetching data directly using Database: Useful when data connection is available locally or is not env. dependent. This is usually used in legacy frameworks now, as most of us have migrated to use APIs now. Ex: List<Object[]> data = new ArrayList<>(); String url = "jdbc:mysql://localhost:3306/testdb"; String user = "testuser"; String password = "testpassword"; try (Connection conn = DriverManager.getConnection(url, user, password); Statement stmt = conn.createStatement(); ResultSet rs = stmt.executeQuery("SELECT username, password FROM users")) { while (rs.next()) { data.add(new Object[]{rs.getString("username"), rs.getString("password")}); } } catch (SQLException e) { e.printStackTrace(); } return data.toArray(new Object[0][]); } ⭐ Found this useful? Repost for your network. ♻️ -x-x- ⭐ P.S. Which one is the most useful for you? ++++ To learn more about Java for Cracking Test Automation Interviews use: https://lnkd.in/g5hr9bea Become Test Automation Architect with FullStackQALearn Docker, GitHub Actions, Jenkins, Test Automation etc. : https://lnkd.in/dWjpT3-3 #japneetsachdeva

Bad data caused by defects in the ETL process can cause data problems in reporting that can result in poor strategic decision-making. According to analyst firm Gartner, bad data costs companies, on average, $14 million annually with some companies losing as much as $100 million. Three Most Common Methods used for Data Warehouse/ETL Testing Method one to compare data movement from data sources to a target data warehouse is Sampling, also known as "Stare and Compare”. It is an attempt to verify data by extracting it from source and target stores and dumping the data into 2 Excel spreadsheets and then viewing or“eyeballing” the 2 sets of data for anomalies. Less than 1% of data is usually verified and reporting is manual. The Second method is a MINUS Query that uses the MINUS operator in SQL to subtract one result set from another result set to evaluate the difference. If there is no difference, there is no remaining result set. If there is a difference, the resulting rows will be displayed. It is inefficient and produces no audit trail or reporting. The Third method companies employ are homegrown tools, utilities or frameworks to perform ETL Testing. While this may be tailored to and work for a company’s specific needs, they are very expensive to build (think lots of consulting dollars) and maintain.

How do you ensure data quality in ETL processes? ✏️ Ensuring data quality in the ETL (Extract, Transform, Load) process involves several key steps: --->Data Profiling:- Analyze the data to understand its structure, patterns, and anomalies. This helps in identifying issues early on. --->Data Cleansing:- Use tools and techniques to clean and standardize data. This may involve removing duplicates, correcting errors, and filling in missing values. --->Data Validation:- Implement checks to validate data integrity during the ETL process. This includes ensuring data conforms to predefined rules and constraints. --->Error Handling:- Set up mechanisms to capture and handle errors that occur during data extraction, transformation, and loading. This could involve logging errors, retrying failed processes, or alerting stakeholders. --->Metadata Management:- Maintain metadata about the data, such as data lineage, source information, and transformation rules. This helps in tracking data changes and understanding data dependencies. --->Data Quality Monitoring:- Continuously monitor data quality metrics and KPIs to identify trends, anomalies, and areas for improvement. This allows for proactive management of data quality issues. --->User Feedback and Collaboration:- Engage with data users and stakeholders to gather feedback on data quality issues and incorporate their input into data quality improvement processes. By following these steps and incorporating data quality best practices into the ETL process, we can enhance the reliability, accuracy, and usability of our data.

In my new personal project, I'm dedicating myself to an ETL process. But what does this mean? The ETL process, which stands for "Extract, Transform, Load," is a fundamental step in the data lifecycle in many organizations. Let's understand what this is all about! The first step of the process, Extraction (Extract), is when data is collected from various sources: databases, files, web APIs, etc. The second step comes after extraction because it's common that data isn't in the right format for direct analysis. So, it needs to be transformed. This step is about cleaning, filtering, standardizing, and enriching the data. When data isn't suitable for analysis, it can lead to decision-making losses, affecting results and harming the business. Common examples in this step include converting data formats, removing duplicates, handling missing values, calculating new metrics based on the business problem at hand, creating relationships between different tables, etc. The third step, Loading (Load), is when data is stored in a proper destination, such as a data warehouse, a relational database, or an analytics platform. In the image below, I've outlined a possible ETL schema. An analyst could extract customer data from a CRM platform, which includes information about sold products, customers, transaction dates, and values. Then, they could use Python, for example, to transform raw data, standardizing date formats, removing duplicates, and filling in missing values. They could also calculate additional metrics, such as total sales per product, average ticket, return on investment (ROI) from campaigns, lead conversion, etc. Here, metrics are defined based on the business problem and what stakeholders want to analyze. Finally, data can be loaded into different locations. It can be loaded directly into Power BI for dashboard construction or into a data warehouse, making the data available to other professionals such as data scientists, for example. Tell me in the comments, have you ever done an ETL process? What tools did you use?

ETL stands for Extract, Transform, Load. It's a process used in data warehousing and data integration to transfer data from various sources into a destination database, typically a data warehouse, where it can be analyzed and queried. Here's a breakdown of each step: Extract: Data is extracted from one or multiple sources, which could be databases, files, applications, APIs, or any other data storage systems. Transform: The extracted data is then transformed or processed to fit the target schema or data model. This step may involve data cleansing, normalization, aggregation, joining with other data sources, or any other manipulation required to make the data usable for analysis. Load: Finally, the transformed data is loaded into the target database or data warehouse. This step involves inserting the data into appropriate tables or structures in the destination system. ETL processes are crucial for maintaining data quality, consistency, and reliability in business intelligence and analytics applications, as they enable organizations to integrate data from disparate sources and make it accessible for reporting and analysis purposes.

ETL Process Overview The ETL (Extract, Transform, Load) process is a data integration framework used to gather data from various sources, transform it into a suitable format for analysis, and load it into a data warehouse or database. Extract: Data is retrieved from diverse sources such as databases, flat files (e.g., CSV, Excel), and APIs. Transform: The extracted data is cleansed, formatted, and enriched to meet analytical requirements using techniques like filtering, aggregation, and joining datasets. Load: The transformed data is then loaded into a target system, such as a data warehouse, where it can be accessed for reporting, analysis, and business intelligence. This process ensures that organizations can make informed decisions based on accurate and relevant data.

Data Center Stakeholder Engagement Automation - @smartgeniushub we automate painful task using the power of scripting # Import necessary libraries import pandas as pd import numpy as np import datetime import controlm_api # Define function to generate performance and status reports def generate_reports(company_name, data_center, start_date, end_date):     # Connect to Control-M API     controlm = controlm_api.ControlM()     # Define job names for performance and status reports     performance_job = f"{company_name}_{data_center}_PerformanceReport"     status_job = f"{company_name}_{data_center}_StatusReport"     # Define job parameters for start and end date     performance_params = {"start_date": start_date, "end_date": end_date}     status_params = {"start_date": start_date, "end_date": end_date}     # Submit jobs to Control-M     performance_job_id = controlm.submit_job(performance_job, params=performance_params)     status_job_id = controlm.submit_job(status_job, params=status_params)     # Check job status and wait for completion     controlm.wait_for_job(performance_job_id)     controlm.wait_for_job(status_job_id)     # Retrieve job output from Control-M     performance_report = controlm.get_job_output(performance_job_id)     status_report = controlm.get_job_output(status_job_id)     # Save reports as CSV files     performance_report.to_csv(f"{company_name}_{data_center}_PerformanceReport.csv", index=False)     status_report.to_csv(f"{company_name}_{data_center}_StatusReport.csv", index=False)     # Send reports via email to stakeholders     email_subject = f"{company_name} {data_center} Performance and Status Report"     email_body = f"Attached are the performance and status reports for {data_center} from {start_date} to {end_date}."     controlm.send_email(stakeholders, email_subject, email_body, attachments=[f"{company_name}_{data_center}_PerformanceReport.csv", f"{company_name}_{data_center}_StatusReport.csv"])     # Print confirmation message     print("Reports generated and sent to stakeholders successfully.") # Define fictitious data for a large scale company company_name = "ABC Corp" data_center = "Data Center A" start_date = "2021-01-01" end_date = "2021-01-31" stakeholders = ["[email protected]", "[email protected]", "[email protected]"] # Generate reports for Data Center A generate_reports(company_name, data_center, start_date, end_date) # Output: # Reports generated and sent to stakeholders successfully.

The ETL process is a fundamental approach to managing and preparing data for analysis, reporting, and decision-making. It comprises three key stages. Extract: - Source Data Retrieval: Data is extracted from various sources such as databases, files, APIs, or other systems. - Staging Area: Extracted data is temporarily stored in a staging area, facilitating subsequent transformations. Transform: - Data Refinement: In this phase, data undergoes a series of transformations to ensure quality and relevance. This involves cleaning, where duplicates and inconsistencies are removed, restructuring to meet desired formats, and enriching by combining or deriving new data elements. - Business Rules Application: Transformation applies business-specific rules to align data with organizational needs. - Quality Assurance: Checks and validations are implemented to maintain data integrity, identifying and rectifying any issues during the transformation process. Load: - Destination Loading: Transformed data is loaded into the target system, typically a data warehouse, data mart, or database. - Indexing and Storage: Loaded data is organized, indexed, and stored in a manner that facilitates efficient retrieval and analysis. - Ready for Analysis: The data is now in a structured and optimized format, ready to be queried, analyzed, and utilized for business intelligence, reporting, or other data-driven activities.

How to build an end-to-end ETL pipeline and automated reporting process: 1. Work with credit loss forecasting senior director to develop CECL LLR forecast requirements and prototype 2. Work with model development engineers to implement and test model 3. Design and execute integrated UAT with senior business director 4. Get report templates for DFAST SF Credit Summary reports from senior business director. Review reports and develop product roadmap for implementation. Review roadmap with stakeholders. 5. Map CECL LLR model output cash flows, loan attributes, transformation logic output to report templates. Assess data size of cash flows for an execution, project expected monthly and peak data size of all executions. 6. Design logical model and work with data team to implement physical model in data warehouse, including access control and scalability. 7. Work with model engineer and data team to design and map ETL data pipeline from model engine to data warehouse, integrate with business execution. Provide requirements for metadata, pre-processing. 8. Design, develop, and test single SQL query (multiple CTEs) joining cash flows, loan attributes, calculated values and co-horting schemes to produce output in a common format of various dimension and measures supporting multiple report templates. Design for extensibility (new cash flows, data, output), tweak for performance. Provide SQL to data team to implement. 9. Work with data and reporting teams to automate population of report templates with SQL output. 10. Work with model execution, data, reporting teams to set up end-to-end workflow from UI set up > model execution > ETL > report SQL execution > report generation in UI 11. Set up, execute, and validate end-to-end testing in ST environment followed by integrated UAT and business owner review and approval. 12. Monitor production processing 13. Expand automated reporting functionality to include other reports in the next program increment. The report template shown below is publicly available at FHFA's website.

Extract, Transform, Load (ETL) processes are critical in data integration and warehousing, but they come with various challenges. Here are the top 10 challenges faced in ETL processes: 1. Data Quality:   - Ensuring the accuracy, completeness, and consistency of data during extraction and transformation stages can be difficult, especially when dealing with heterogeneous data sources. 2. Scalability:   - As data volumes grow, ETL processes need to scale efficiently. Handling large datasets can strain resources and affect performance. 3. Performance Optimization:   - ETL processes can be resource-intensive and slow. Optimizing performance to ensure timely data availability without compromising system performance is challenging. 4. Data Transformation Complexity:   - Transforming data to fit the target schema involves complex logic, business rules, and data cleansing. This can be time-consuming and error-prone. 5. Real-time Data Processing:   - Moving from batch processing to real-time ETL can be challenging, requiring sophisticated tools and architectures to handle continuous data streams. 6. Error Handling and Recovery:   - Detecting, handling, and recovering from errors during ETL processes is critical. Poor error handling can lead to data corruption or loss. 7. Security and Compliance:   - Ensuring data security and compliance with regulations (e.g., GDPR, HIPAA) throughout the ETL process is essential. This includes data masking, encryption, and auditing. 8. Data Lineage and Traceability:   - Maintaining data lineage to track data origins, transformations, and movements through the ETL pipeline is important for auditing and debugging. 9. Integration with Diverse Data Sources:   - ETL processes often need to integrate data from various sources, including databases, flat files, APIs, and cloud services, each with different formats and protocols. 10. Resource Management:    - ETL processes can be resource-intensive, requiring careful management of CPU, memory, and I/O to avoid bottlenecks and ensure efficient operation.