How can you create a compelling data visualization with real-time and batch data?

- Select tools that seamlessly merge real-time and batch data, like Power BI, Tableau, or dedicated streaming visualization platforms. - Ex: Using Apache Kafka for real-time data ingestion and Apache Spark for batch processing, feeding a unified visualization in Grafana.

I Don't think choosing the tools is addressed on a day to day basis but only at the start of project. Most of decision making weight given to cost of acquisition and Maintenance, complexity of requirements and connectors for our data. Please correct of I am wrong in my observation

Some simple steps to start with: 1. If the data set is too large to be visualized at once, consider sampling the data to work with a manageable subset. However, be cautious of biased sampling that may not accurately represent the underlying dataset. 2. Perform exploratory data analysis to gain insights and a better understanding of the data. Be selective about what you visualize. Are there redundant or completely missing categories that should be dropped from the data? Identify and remove outliers that are not meaningful and could distort the data distribution. 3. When combining data from different sources, check for accidental duplicates that could affect the accuracy of the statistics and the overall data story.

- Ensure consistency in data structure, format, and quality for seamless integration and accurate visualization. - Ex: Implementing data cleaning and transformation pipelines to bridge differences between real-time sensor readings and historical batch data.

- Choose chart types that effectively highlight trends, patterns, and relationships in both data streams. - Ex: Combining a real-time line graph of website traffic with a historical bar chart of conversion rates to analyze user behavior patterns.

Testing your visualizations is a crucial step in ensuring their functionality and effectiveness. One important aspect is creating unit tests, which can be tricky for visualizations but highly beneficial in protecting against broken pipelines and incorrect data and code. It is also essential to invest time in proper CI/CD practices for your data and code. Engaging with end users and regularly seeking feedback can help ensure that your visualizations meet their needs and remain up to date. Additionally, having thorough documentation can assist future developers in understanding the process you followed, making it easier for them to address bugs, modify code, and add enhancements or new features. This includes your future self as well!

- Check for visual clarity, data accuracy, and performance under various data loads and user interactions. - Ex: Simulating peak traffic scenarios to ensure real-time visualizations update smoothly and accurately reflect changes.

- Publish visualizations in user-friendly formats (dashboards, web pages, reports) with clear explanations and interactive features. - Ex: Embedding real-time visualizations in a company's website to showcase key performance metrics and engage stakeholders.