How can you design data visualization projects for large-scale and distributed data systems?

a) Always start with the business problems and sort them by $value of the decision b) Here is my 1-5-8 principle: Think about identifying the “North Star” KPI for each department, and not more than 5 Metrics which the department should track, along with nothing more than 8 breakdown hierarchical dimensions. c) Focus on empowering these departments with prioritised $ problems in the first quarter or the first year and then double down on picking the right business problems. In case of doubt, always follow this top down approach for mapping business cases to large datasets. Hope this helps.

In large-scale data visualization projects, strategically employing data marts is a game-changer. This approach involves distributing information across different segments of the organization in a highly focused manner. Each data mart serves as a hub for specific insights. Every team and department is accessing and interpreting the data most relevant to their goals. This method ensures that data isn't just a vast ocean of numbers, but a well-organized, easily navigable resource that drives decisions and strategies. It's about transforming data into a powerful, functional asset for every part of the organization.

- Use distributed computing and parallel processing to speed up data retrieval, filtering, mining, rendering, and display. This can reduce the latency and bandwidth requirements for data visualization and enable interactive exploration of large data sets. For example, Named Replicas feature in Azure SQL DB Hyperscale built on distributed architecture is commonly utilised across industries. - Use dimensionality reduction and data abstraction methods to reduce the complexity and noise of high-dimensional data and extract the most relevant and informative features. - Use interactive and dynamic visualizations that allow users to manipulate, query, filter, and drill down the data according to their needs and interests.

1) Understand the classification of data (Public/Private/Internal/Confidential/Restricted). The backend architecture and user authentication mechanism will depend on the classification of data. Large organisations have Infosec approval processes. 2) Estimate the Volume and Count of users 3) Understand the data latency requirements - i) The difference between the occurrence of a transaction to it being available as a report ii) time taken for loading data from backend storage to visualization layer 4) Housekeeping and launch pre-requisites: Define data archival and backfilling requirements 5) Preventive, Detective and Reactive measures for system upkeep. Ex: System should have auto Tickets when reports have not arrived/delayed

Clearly outline the objectives of your data visualization project. Understand the key questions your audience needs answers to and define the scope of the visualizations. Identify the metrics and KPIs that will drive your visualizations and align them with the goals of your organization. For example, what type of end reporting I need, what are it's data sources, what format they have, while aggregating which format will be performance efficient. So understanding your requirements with your budget is most important.

When selecting tools for large-scale and distributed data systems, consider factors such as scalability, integration capabilities, ease of use, and the specific requirements of your project. It's also essential to ensure that the chosen tools can effectively connect to and visualize data stored in distributed data environments. Here are some popular tools and platforms suitable for such projects: - Apache Superset. - Cloud Computing (Azure & AWS). - Power BI Service. - Grafana.

Select appropriate tools and platforms based on the scale and nature of your data. Consider factors such as data volume, real-time processing requirements, and the technical expertise of your team. Popular tools for large-scale data visualization include Apache Hadoop, Apache Spark, Tableau, and Power BI. Check your source then find a data warehouse which can support your analytical queries. Find the data integration tool which can be used to load data from your source to target in real time and batch.(example - Airbyte, Fivetran, Cloud Services Aws,Gcp).

If you are looking for a tool specifically for data visualization, you might want to explore options such as Tableau, Power BI, or Looker. These tools are designed to connect to various data sources, including databases, and create interactive and insightful visualizations. Yet, if your focus is on designing the underlying database structure for database modeling, SqlDBM can be a suitable choice. SqlDBM is primarily used for creating and designing database schemas visually, allowing you to model and document your database structure. It provides a collaborative platform for designing and sharing database diagrams.

Choosing a visualisation tool is a key part as part of Data stack , the tools you select should always complement and add value to your business . Also the tool you select should be in such a way that you should not be locked to a single tool meaning do not rely or implement too much tool specific functions , if in case in future you choose to migrate to a different BI tool in future the migration efforts has to be minimal , you should also rely much on moving the heavy calculations to database . Tableau and Power BI tools which are hot in market as of today but new tools may come and go and are tailored to certain use cases , we have to do in depth analysis in adapting a tool to your data stack

Engineering stack indeed plays a pivotal part in the tool selection, and will have an impact on a number of functional and nonfunctional aspects of the potential visualization tools.  Beyond compatibility, here are a few of these dimensions: 1. Skill Set of the Team 2. Information Security and Compliance 3. Scalability and Performance 4. Integration Capabilities 5. Customization and Flexibility 6 Collaboration and Sharing Features during implementation. Depending on the significance of the decision, organizations should setup formal tool evaluation exercises such, as Weighted Scoring Model.  This would involve putting together a comprehensive list of factors and assign a weight based on its importance to each selection criterion.

Pipelines and models are the backbone of good analytics. If you want to work on the insights rather than questioning the output of visualisations, you need to set these up properly, so they deliver robust data. Here’s one example. A production dashboard in an airline consumes data from several departmental sources. The latest update (manual) switched columns and replaced the sum of passengers on a flight with a sum of luggage. Internal checks were inadequate so they didn’t pick up on the error until the review in a monthly meeting. The differences were subtle to notice right away until the experienced CCO did a sense check. Getting this info wrong would have resulted in potential route cuts. Material impact.

Develop robust data models and pipelines to handle the complexity and scale of your data. Ensure that data is cleaned, transformed, and stored efficiently. Implement distributed processing frameworks if dealing with massive datasets. Consider using technologies like Apache Kafka for real-time data streaming or pub sub models available on cloud services like GCP pub-sub.

Automation in data pipelines is essential for large-scale systems: 1. **Data Ingestion**: Automated collection from diverse sources. 2. **Data Transformation**: Cleansing and formatting automatically. 3. **Workflow Management**: Tools like Apache Airflow for task orchestration. 4. **Quality Checks**: Automatic checks for consistency. 5. **Monitoring**: Automated performance monitoring. Example: Netflix uses Apache Kafka for real-time streaming and Apache Flink for processing, automating pipelines to efficiently handle billions of events for analytics. This approach ensures effective large-scale data management.

Data Model and Pipelines is cornerstone in any visualisation project. When it comes to designing a data model and pipeline for the project, requirements that were collected earlier while sitting down with users in earlier phases comes handy. For example, When users complain that they are not able to continually track down Sales taking place within a day, it is a direction for you to have your data model support near real time or periodic refreshes, one of the common ways to do that would be to create a denormalised object at the final layer to refer to while refreshing, allowing near real time / periodic refreshes without hampering performance of the DB as it does not have to run heavy queries all the time to gather the data.

Designing data models and pipelines is a crucial step in any data-driven project. It involves defining how data will be structured, stored, and processed to extract valuable insights. . Understand the Data Sources; . Define Data Models; . Design Data Pipelines; . Consider Scalability and Performance; . Document and Maintain Data Models and Pipelines; In my view, byy following these steps and carefully considering the specific requirements of your project, you can design data models and pipelines that effectively manage and transform data into actionable insights.

Data prep is pretty important indeed. However, a bad visualisation can easily discard all the efforts and good work data engineers have done prepping the data. Points to consider: - A simple visualisation that is easy to understand is better than a flashy visualisation - Think about which data dimensions and how many of them you want to put on the visualisation - if your platform allows, keep the visualisation simple with at most two dimensions but use interactions to dive deeper in visualisations - Think about Color-blind safe colours, not all people see things the same Edward Tufte has a book "the visual display of quantitative information" which is a pretty good for guidelines on creative meaningful visualisations.

Once the data infrastructure is in place, design your visualizations. Choose appropriate chart types and visualization techniques based on the nature of your data and the insights you want to convey. Consider interactive dashboards for exploratory analysis, and static visualizations for reports. Ensure consistency in design and use color-coding wisely.

Create your data visualizations: Use appropriate visualization techniques and tools to represent insights effectively. Ensure scalability and responsiveness.

As a data science student, I approach crafting data visualizations by meticulously selecting the appropriate types, elements, and layouts to represent my data effectively. I utilize diverse visual tools like bar charts, line charts, scatter plots, and more to highlight different data aspects. Employing colors, shapes, sizes, labels, and legends helps enhance the visual appeal and comprehension of my visualizations. Organizational elements like grids, panels, tabs, or menus assist in structuring and presenting the information coherently. Throughout this process, I prioritize adhering to key design principles: clarity, simplicity, accuracy, consistency, and aesthetics, ensuring that my visualizations are both informative and visually engaging

Creating data visualizations involves selecting types, elements, and layouts. Choices range from bar charts to heat maps, with colors and shapes enhancing clarity. Organizing visualizations with grids or tabs is key, while adhering to design principles like clarity, simplicity, and aesthetics ensures effective communication. #surfalytics

Moving from MVP to full fledged production deployments have taught us to utilize the same level of rigor on visualizations as we do on business logic components. A high-level, and often scripted, UAT would validate choice of visualizations and tools. Yet, a formal QA process would need to validate extreme/edge case conditions -- e.g. nulls, 0s, extremely high and low values. It is these edge conditions and or extreme loads that often break the visualization and become a point of reputational damage.

Completely test your visualizations to identify any anomalies or discrepancies. Conduct user testing to gather feedback from the intended audience. Refine your visualizations based on user input and ensure that they align with the initial goals. Pay attention to performance, especially when dealing with large datasets.

Create staging environment with real-life data. On every change, first deploy to staging and validate it didn’t break the functionality. Run a set of functional & integration tests on it. Once validated, it’s safe to deploy to production.

I like to test my data by including the end users in the process. I present it to them, get their feedback, ask pointed questions to ensure that the numbers align with what they expect. If they don't, debug and investigate. If they do, then the end user is involved in the process and will champion the work done!

Testing and refining data visualizations is crucial. Evaluate their accuracy, completeness, and timeliness. Check responsiveness, interactivity, and scalability. Ensure clarity, intuitiveness, and informativeness. User feedback is key to addressing any gaps or issues. #Surfalytics

Deploy your visualizations on a scalable and reliable infrastructure. Monitor the performance and address any issues promptly. Implement version control for both data and visualization code to manage updates effectively.

Streamlit is one of my favorite frameworks to tie together and deploy interactive data visualizations. If you haven't already, check out Streamlit. It has the following pros: - Create web-app for your data all using python - Native support for data-visualization libraries like Plotly, Sklearn, and more. - Infinitely customizable

Deploying and maintaining data visualizations involves selecting delivery methods and channels. Options include web browsers, mobile devices, or desktop apps. Sharing via email, social media, or blogs, and hosting on cloud or servers is essential. Regular updates ensure they stay current with data changes and trends. #Surfalytics

My focus extends to deploying and maintaining data visualizations, ensuring accessibility and reliability for the intended audience. Choosing the most suitable methods and channels for delivery becomes paramount—be it through web browsers, mobile apps, or desktop applications. Sharing via email, social media, or blogs aids in widespread dissemination. Hosting on cloud storage, servers, or databases ensures seamless access. Regular monitoring and updates form the crux of maintenance, ensuring that the visualizations stay current with the latest data trends and changes. This continuous upkeep ensures that the insights offered remain pertinent and valuable to users over time.

Deploy and maintain your data visualizations: Implement a deployment strategy for seamless integration into your data system. Establish a maintenance plan for updates and enhancements.

Past project had revenue impact:Tech Stack 1. Data Collection and Storage: - Amazon S3 - AWS Glue for data cataloging and ETL (Extract, Transform, Load) jobs. 2. Data Processing: - Amazon EMR 3. Data Warehousing and Analytics: - Amazon Redshift - AWS Athena for ad-hoc querying directly on data stored in S3. 4. Visualization Tools: - ELK stack , Kibana. 5. Integration and Automation: - AWS Lambda for serverless computing to automate data processing and trigger visualization updates. 6. Security and Access Control: - AWS IAM - AWS KMS 7. Scalability and Performance Optimization: - Configured auto-scaling for services like EMR or Redshift to adapt to varying workloads. 8. Monitoring and Maintenance: - AWS CloudWatch

The project of mine, titled "Airline Customer Satisfaction Prediction and Service Enhancement," encompasses predictive modeling using machine learning to determine customer satisfaction based on various airline service attributes. It involves thorough data preprocessing, exploratory analysis, and the implementation of decision tree and random forest classifiers. Beyond traditional model evaluation, it explores customer segmentation, sentiment analysis, and the consideration of external factors to enhance predictive accuracy and provide actionable insights for airlines to optimize service quality and passenger satisfaction.

Here’s what else to consider: Scalability: Ensure your visualizations can scale with growing data volumes. Interactivity: Incorporate interactive elements for user engagement. Real-time updates: If relevant, design for real-time data updates. Security: Implement security measures to protect sensitive data. User feedback: Gather feedback from users to enhance usability. Documentation: Document your design, tools, and processes for future reference.

Building Dashboard Visualization with Machine Data Analytics. Whose Data is Right? Machine data is pivotal in data visualisation, offering real-time insights for prompts, decisions. Its vast volume and variety necessitate efficient & unified visualization with context and truth. These new 'data-driven' dashboards provide a more holistic view by integrating logs and metrics, offering greater control and customization. They address previous limitations in classic dashboards for dynamic data synthesis and deciphering for easier migration, portability & and interoperability, enhancing the visual representation of data but also empowering proactive monitoring and decision-making, ensuring comprehensive visibility and efficiency in DevSecOps.