PRATUL GOYAL

So, as far as my background goes, I've been working with the skill set I possess, which is based on Python, SQL, R, and Azure Machine Learning Studio. Here, I'm dealing with data sets, which are more focused on churn prediction modeling, A/B testing, and how to augment the customer experience, NPS score, review analysis. I'm also making a chatbot, which is being created on top of large language models with the help of OpenAI and along with that, we're also using Gemini in one of our recent use cases. I have almost 9 years of experience. I'm a bachelor's in engineering, and I did an MBA in data science and business analytics from the Indian Institute of Management, one of the elite institutes in India, from 2015 to 2017. I possess over 40 certifications in data science and machine learning, including certifications in deep learning, machine learning, and Azure cloud. I also have one patent in the virtual assistant field, which was filed in the Indian patent portal, and along with that, I have five research papers published in some of the elite international journals. Apart from this, I also have experience working with Power BI and recommendation engines, as well as edge devices. I also did a migration for one of the recent projects of Unilever in my current company. In my current role, I'm working on tasks related to how Unilever can enhance its business. My core objective is to improve the customer experience, product reviews, and customer experiences for six countries. I'm working with CICD pipelines and using GitHub actions, and I've been using Jira for assigning tasks. We're constantly following the agile methodology, where we're given different types of sprints as per user requirements. I'm currently handling a team of seven people, where I'm giving them regular tasks about project deliverables, and we're also dealing with regular data drift checks and model tweaking. So, these are some of the things I do. Overall, I have 7 to 8 years of experience in data science and machine learning.

Craft a strategy to migrate SQL-based legacy reports to a modern business intelligence system, ensuring data continuity and accessibility. This can be done in a variety of ways. Some examples I'd like to mention based on this is an assessment of the current environment. This would include understanding the existing SQL-based report structure, data structure, and dependencies. After that, identify key stakeholders and their requirements for the new system. Research and choose modern business intelligence platforms that align with the organization's needs, scalability, and compatibility with the existing technology. Data modeling and mapping involve mapping out data schema relationships and transformations needed to migrate the SQL-based system to the chosen BIE platform. Apart from this, data extraction and transformation is also one thing. We extract data from our legacy SQL databases using ETL tools and transform the data to fit the schema. Testing and validation can also be used, where we conduct thorough testing to validate the accuracy and integrity of the migrated data and reports, and involve stakeholders to verify that new reports meet the requirements and expectations. User training and adoption is also crucial. We provide training sessions for end-users on how to navigate and utilize the new BI platform effectively. Data continuity and backup is also one thing, where we implement backup and recovery procedures to ensure data continuity in case of unforeseen issues or failure. Security and access control is also important. We configure access control and security measures to protect sensitive data and ensure compliance with regulatory requirements. Monitoring and optimization is also necessary. We set up monitoring tools to track system performance, usage patterns, and data quality. Documentation and knowledge transfer is also essential. We document the migration process and best practices for future references. By following these steps, I think a successful migration is achievable.

Okay. So to develop a contingency plan for a data analytics team with a critical Tableau dashboard that fails to update the latest data. While developing a contingency plan for the data analytics team with a critical Tableau dashboard that fails to update with the latest data, we can actually mitigate some of the strategies. We can actually identify potential failures. We determine the possible reasons why the dashboard may fail to update, such as technical issues, so this can be done. We establish a monitoring system. We implement tools or scripts to regularly check the status of updates on the dashboards. This can involve alerts. We can set up alerts. We can create a backup of the data sources. If we regularly maintain a backup of the data sources to populate the dashboards, it will actually help if the primary data source fails, so it will help in referring to the alternative sources. We also develop workflows that primarily involve contingency workflows that create predefined workflows and procedures to follow in case of the dashboard update failing. We assign specific tasks. Apart from this, we will be developing a communication plan. We define communication channels and protocols for notifying stakeholders about the dashboard status and potential delays in data updates. A temporary solution is also one thing. We prepare a temporary solution or alternative method for accessing critical data if the dashboard is unavailable for an extended period. We establish an escalation process for escalating unresolved issues to a higher level of management or IT support if necessary. Training and documentation is also one thing. We provide training to team members on the contingency plan procedures and ensure that the documentation is up to date. Regular testing and review can also be done. We conduct testing of the contingency plan to identify mainly the weaknesses and gaps. Post-incident analysis can also be done. After any incident if the dashboard fails to update, we conduct a post-incident analysis to identify root causes and areas of improvement. User feedback for improvements can also be done. These are the things by which if we develop a contingency plan for the data analytics team, a critical Tableau dashboard.

So recommend a scalable method for managing data pipeline dependencies and scheduling in a system like Snowflake or a comparable cloud data platform. This generally requires a lot of steps. So, some of them are, we can create a workflow, like, orchestration tools. So, Apache Airflow, AWS Step Functions can be used to manage the dependencies between different components of the data pipeline. And these tools allow us to define workflows as directed acyclic graphs, DAGs, where tasks can be executed in parallel or sequentially based on the dependency. We break down the data into smaller manageable tasks and define them as part of the workflow orchestration tool. Okay? And, we parameterize the workflow to make them reusable and adaptable to different scenarios. Different parameters for input data sources, processing logic, destination tables, and scheduling parameters to customize the execution of each workflow instance. We also utilize trigger-based scheduling. So, we implement a trigger-based scheduling mechanism to initiate data pipeline execution based on events such as completion of the upstream task, arrival of new data, and predefined schedule intervals. We integrate with cloud data platforms, features such as native data platforms like Snowflake, Tasks, and Streams to facilitate data by flowing scheduling and management. These features often offer built-in functionality for automating common tasks and orchestrating data workflows within the platform itself. Monitoring and alerting. We implement monitoring and alerting mechanisms to track the execution status and performance of the data pipeline in real-time. We scale resources dynamically, configure the workload orchestration tool and cloud data platform to dynamically scale resources based on workload demands. This generally ensures that sufficient compute and storage resources are allocated to execute data pipelines efficiently. version control and CICD. We implement version control practices for the data pipeline, including definition and associated code artifacts. We integrate with continuous integration and development pipelines to automate the deployment and testing of changes. These are some of the strategies which can be done. Apart from this, documentation of workflows and dependencies, and regular performance optimization, where we monitor the pipeline and the workflow orchestration processes, while identifying bottlenecks and resource utilization. Also, taking time efficiency into account. These are some of the steps which can be followed to manage data pipeline dependencies in a system like Snowflake.

To determine how to resolve conflicts between differing data models in Salesforce and an enterprise data warehouse like Snowflake, so some of the things that can be done will be explained point by point. To identify conflict data models, we can do thorough analysis, like documenting the schema structure, data type relationships, and other dependencies between the two systems. We can understand the business requirements, gain a deep understanding of the business requirements driving the data integration between Salesforce and Snowflake, and identify key objects, fields, and relationships that are critical for reporting, analytics, and decision-making processes. We can do data mapping and transformation. We develop a comprehensive data mapping and transformation strategy to reconcile the differences between the data models in Salesforce and Snowflake, determine how the data will be mapped, transformed, and loaded from Salesforce into Snowflake while ensuring data integrity and consistency. Normalization and denormalization can also be done; we evaluate the normalization level of the data model in Salesforce and Snowflake to determine whether normalization and denormalization strategies are needed to align the data structures. We implement data integration tools. We utilize data integration tools such as Informatica, Talend, to streamline the process of extracting, transforming, and loading data between Salesforce and Snowflake. We establish data governance policies and standards to ensure consistency, quality, and compliance across Salesforce and Snowflake data. We automate data synchronization. We implement automated data synchronization processes to keep data models in Snowflake generally in sync and schedule regular data synchronization jobs to update Snowflake with the latest data from Salesforce and vice versa. Apart from this, we implement data quality assurance to detect and resolve any discrepancies or anomalies between Salesforce and Snowflake data. We monitor and audit data changes by establishing a monitoring and auditing mechanism to track data changes and modifications between Salesforce and Snowflake. We iterate and continuously evaluate and refine the data integration process based on feedback and evolving business requirements.

So to propose a method to automate data retrieval from Snowflake and visualize metrics in Tableau for a weekly performance report. Some of the steps we can follow are primarily based on data preparation in Snowflake. We generally ensure that the relevant data tables are properly structured and maintained in Snowflake, and implement necessary data transformation, such as aggregating data in Snowflake views or materialized views, to prepare the data for reporting. We create scheduled views or queries. We write SQL queries or views in Snowflake to retrieve the required metrics and dimensions for the weekly performance report. We schedule these queries to run automatically on a weekly basis using Snowflake task scheduling. We export data to Tableau Server. We configure Snowflake to export the query result or view output to a CSV file stored in a designated location accessible by Tableau server. We set up a data source connection in Tableau to the CSV file generated by Snowflake. We configure the connection to automatically refresh on a weekly basis to fetch the latest data for the performance report. We designed Tableau Dashboards. We designed Tableau Dashboards to visualize the performance metrics based on the data retrieved from Snowflake. We create interactive visualizations, such as charts, graphs, and KPIs, to present key performance indicators. We schedule the Tableau workflow to refresh automatically on a weekly basis to reflect the updated data from Snowflake. We configured Tableau server to send notifications or alerts if the refresh process encounters any errors or failures. We publish our Tableau workflow containing the performance dashboards to Tableau Server. We set permissions and access control to ensure that authorized users can view and interact with the weekly performance reports. We share the Tableau workbook with relevant stakeholders or email distribution lists. We generally monitor and maintain the automated process of data retrieval from Snowflake to visualization to ensure it runs smoothly without issues. We address any errors or performance issues promptly and make necessary adjustments to the automation workflow as needed.

So I'll be taking little time to analyze this. So I'm still thinking. Given why it might not return the expected result and how would you modify it to ensure accurate data retrieval. K. So, basically, So what I can figure out with this is that the having clause is used within queries that use an aggregate function with count star, but without a group by clause. The event clause is used to filter groups of rows based on the result of the birthday, but the query does not give any group based on the result before using having. So to ensure accurate data retrieval, what we can do is count the number of rows in the order table and return the count as total. And if you want the result based on the count of rows, we can use a where clause instead of a having clause. So while modifying this, it will help. So generally, what it is doing is the query doesn't group any rows before using the having clause. So that's one thing I can figure out after a lot of thinking.

So to formulate an approach for incorporating user feedback into the iterative development of the Tableau dashboard, some of the following steps can be involved. So, initial requirement gathering can be done. Where we start by gathering initial requirements from stakeholders and end-users to understand their needs, objectives, and expectations. For the Tableau dashboards, we identify key metrics, visualizations, and features that users want to see in the dashboard. Prototype development can be done based on initial requirements, developing an initial prototype of the dashboard. Keep the prototype flexible and able to accommodate changes based on user feedback. We encourage users to provide specific feedback on usability, scalability, and relevance of the metrics and overall user experience. This kindly correlates with my current experience also. We use surveys, interviews, or feedback forms to collect structured feedback from users, allowing them to rate and prioritize different aspects of the dashboards. We analyze the feedback collected from users to identify common themes, pinpoint areas of improvement in the dashboard. We prioritize feedback based on its impact on the overall usability and effectiveness of the dashboard, and we focus on addressing critical issues in high-priority feedback first. Iterative development incorporates user feedback into the iterative development process by making iterative updates and enhancements to the Tableau dashboards. We implement changes to the dashboard design, layout, data visualization, and interactivity based on the prioritized feedback from users. We then maintain a version control to track changes and iterations of the dashboard throughout the development process. User validation and testing: Validate the updated version of the Tableau dash with end-users to ensure that the implemented changes meet their expectations and address their feedback. We conduct additional user testing sessions or usability studies to gather feedback on the updated dashboard. Feedback Loop Closer: We close the feedback loop by communicating with users, telling them how their feedback has been incorporated into the Tableau dashboard. Continuous improvement can also be done. We establish a process for the continuous improvement of the Tableau dashboard based on ongoing user feedback and enabling business requirements. Regularly, we solicit feedback from users and stakeholders and iterate the dashboard to keep it relevant and useful. These are some of the things that can be incorporated.

So how would then architect a scalable business intelligence solution that incorporates Tableau dashboards and adheres to the data governance policies. Can be done in various ways, but some of which can be listed, for example, we can define a business requirement. We can start by understanding the business objectives and requirements from the BI solution, identify key stakeholders and their needs for data analysis and reporting. We established a governance framework. We established a robust governance framework that defines policies, standards, and procedures for managing data quality, security, privacy, and compliance. And we define roles and responsibilities for data stewardship and data owners, data custodians to ensure accountability and transparency in the data management process. Data integration and consolidation can also be done. We integrate data from diverse sources such as databases, data warehouses, data lakes, and cloud applications into a centralized data repository. We can use ETL processes or data integration platforms to consolidate and harmonize data from different data sources while ensuring data quality and consistency. We develop a comprehensive data model and semantic layer that provides a unified view of the data for reporting and analysis. We use dimensionality modeling techniques such as star schema, snowflake schema to optimize data retrieval performance and facilitate data query. We design a scalable architecture that can automate growing data volumes, user concurrency, and analytical workloads. Consider cloud-based platforms such as Snowflake, Amazon Redshift, and Google BigQuery for scalability, elasticity, and performance. We deploy Tableau Server or Tableau Online to host and manage the Tableau dashboards, workbooks, and data sources centrally. We configure Tableau Server with the appropriate authentication, authorization, and access control to enforce data governance policies and ensure secure access to BI assets. Data security and access control, we implement fine-grained control and role-based permissions with Tableau Server to restrict access to sensitive data and analytical capabilities based on user roles and privileges. We encrypt data in transit and at rest to protect data confidentiality and integrity. Metadata management and cataloging. We establish metadata management to capture and maintain metadata about data sources, data definitions, and data lineages. We use metadata management tools or platforms to catalog and govern metadata assets, enabling users to discover, understand, and trust the data used in Tableau dashboards. We implement mechanisms to track the health, performance, and usage of the solution. We monitor Tableau Server performance metrics such as CPU utilization, memory usage, and query response time to identify and optimize resources.

Adobe Analytics is not my area of expertise, but I would try to jot down some of the steps. So, like, let's say for an instance, what some of the steps can be done in order to use Adobe Analytics. Okay. See how would I adopt a team workflow to incorporate BA best practices with a tool like Adobe Analytics? This can be done in various ways. We define clear goals and objectives. These are very generic steps, but we can define clear objectives and goals. Planning and scoping can be done, in which we generally plan with a business analyst and a data analyst to define the scope of the project. Data collection and preparation: we use Adobe Analytics to collect the relevant data from various digital channels such as a website, mobile apps, and marketing campaigns. Ensure that data collection is configured accurately to capture the necessary metrics and dimensions. Conduct exploratory data analysis using Adobe Analytics to gain initial insights into the data flow, trends, patterns, and correlations to identify areas of interest for further investigation. Hypothesis testing can be done. We formulate hypothesis testing based on our insights gathered from EDM and business understanding. Iterative analysis and visualization. We use Adobe Analytics to perform iterative analysis and visualization of the data. We create custom reports, dashboards, and visualizations to communicate insights effectively to stakeholders and foster collaboration between business analysts, data analysts, marketers, product managers, and other relevant stakeholders throughout the analysis process. Documentation and knowledge management: we document analysis findings, methodologies, and assumptions to maintain transparency and reproducibility. Feedback and iterations can be done: we solidify feedback from stakeholders and team members at various stages of the analysis process and incorporate feedback to refine analysis approaches. Continuous learning and improvement: we create a culture of continuous learning and improvement within the team. We share learning best practices and success stories to enhance collective knowledge and capabilities. Performance quality assurance is taken to ensure the quality, accuracy, reliability, and validity of the analysis results. Governance and compliance: data governance policies, privacy regulations in the industry, standards when collecting, analyzing, and sharing data. Policies and external regulations such as GDPR or CCPA can be followed. So these are the steps which can be incorporated.

I'm still thinking about this. I'm looking at the Python function over here for calculating the factorial of a number and I think it has a logical error. The base case is n equals n, which is correct and returns 1. However, the recursive case should be factorial of n minus 1 instead of factorial of n, which is given since n is not properly defined. It should be factorial of n minus one because the thing is the factorial generally goes like this: if n is 0, it is returning 1, but n won't be 0. Let's say n is 5, so it will be going to the second case. So it will be going into the recursive logic where it will be multiplied by 5 and times the factorial of n minus 1. So basically, it will be returning n minus 1. So accordingly, n minus 1 would be something where it goes into the recursive logic. So this can be one issue. So, yeah. So basically, n minus 1 is one of the logical errors that I can figure out. So this can be our answer. Apart from this, okay.