How do you manage large data sets?

For any BI solution, keeping the problem statement that we are trying to solve in mind. Talking about large datasets, always ask these questions: 1. Do we really need the entire dataset to be loaded on the visualization tool? 2. Are there fields that are not needed and can be removed? 3. Can we push the transformations to data source?. 4. Live data or extracts/imports? 5. Can we use incremental refreshes? Allows faster and reliable refreshes while allowing model to grow? 6. On the data source, can we implement techniques for faster retrieval? 7. If we need transformations on the BI tool, should we use calculated columns or measures? These are some basic questions that will help in deciding the best course with whichever tool we work with.

Managing large datasets in a Business Intelligence (BI) tool requires a strategic approach to extract meaningful insights effectively. Start by optimizing data storage, and utilizing efficient compression techniques to reduce storage costs. Employ data modelling practices, like creating data hierarchies and aggregations, to enhance query performance. Utilize data partitioning to enhance load times and prioritize critical data. Consider implementing a data warehouse for centralized storage and efficient data retrieval. Leverage indexing and caching mechanisms for faster query responses.

Ensuring that the data is cleansed and meaningful is a critical aspect, particularly in IoT scenarios. The other oft overlooked aspect is around the intent of the data. Most times the decision of "get everything in", we'll figure out later, just accumulates data which isn't meaningful or useful.

I would follow two steps here, first, even before you get to coding or any tool, understand the requirement and is the stakeholder looking to achieve from the analysis, most times limits the amount of data that needs to be analyzed. Second, perform data cleaning/transformation not in the BI tool (Power BI, Tableau etc), instead do it on data warehouse tool (SQL Server Studio, Databricks etc) then bring the prepared layer data in BI tool

In my role as a data analyst at a healthcare startup, I employed several strategies. First, I implemented a data warehouse to store and organize the vast amount of patient and operational data. I also used data compression techniques to optimize storage space. Additionally, I leveraged parallel processing and distributed computing tools to efficiently process and analyze the data. Finally, I established data archiving and backup protocols to ensure data integrity and security. These practices ensured that we could effectively manage and derive valuable insights from the extensive healthcare data, improving decision-making and patient care.

To derive insights, employ advanced analytics, data visualization, and interactive dashboards within your BI tool. Implement smart filtering and data summarization to handle large datasets without overwhelming users. Regularly refresh and validate data to ensure accuracy. Lastly, consider predictive analytics and machine learning models for deeper insights and forecasting capabilities.

I will add my perspective as Data Scientist. Use Only Required Columns If certain columns in your dataset are not necessary for your machine learning model, you can drop these columns to save memory. Also,If your dataset is too large to fit into memory, you can read and process it in chunks. Even, Sparse data formats can be used to store data more efficiently when your dataset has a lot of missing or zero values. Some other Libraries like Modin or Vaex can be used as alternatives to pandas for handling large datasets. Various OSS/BSS vendors uses Apache Spark as it is a powerful open-source analytics engine for large scale data processing and than applying machine learning models.

Regardless of the origin of the data, if the BI software is not properly optimized to work with large datasets, it will be a formula for disaster. For those who opt for Power BI in these cases, undoubtedly, programming incremental refresh is the formula for success in terms of scalability, performance, and cost savings. Many BI developers overlook this crucial function due to the challenges that Power BI faces in efficiently managing datasets. In any case, competent professionals should embrace the challenge

Choosing the right tools for a business intelligence project with a large dataset involves considering several factors. Here is what you need to know to make informed decisions: - Understand Requirements - Scalability of tools - Data Integration Capabilities - Data Cleaning - Visualization and Reporting - Ease of Use - Security Features - Cost Considerations

Data cleanse is another area where one should think about a tool that can help inspect data, identify anomalies, correct anomalies and dashboard the data cleanse process.

The larger the data, the more chances of missing, repetitive and inconsistent data. Data quality is one of the most important aspects. Make sure to have the data consistent in terms of data type, handling the null or missing values, replacing unnecessary long characters to numbers whenever possible.

An essential step in managing large data sets is meticulously checking for null values and comprehending their implications within your data. In certain scenarios, it may be appropriate to replace null values with zeros, but this should not be uniformly applied; careful judgment is crucial. For example, if you aim to calculate the median amount spent per customer, replacing null values with zeros can accurately reflect spending, including that of customers who made no purchases. However, if your goal is to determine the median purchase value, it's advisable to filter out null values so your data set exclusively represents customers who have made purchases.

Managing vast datasets is an art. Clean data by addressing missing values, outliers, and validation. Transform it through feature engineering, normalization, and reshaping. Use efficient tools and smart sampling. Visualize and automate processes while keeping meticulous documentation. Adapt for incremental updates.

One of the biggest hurdles when pulling data from different source together is working with dates. It's usually a good idea to have one data source dedicated to time divisions - one source can also be a stacked format working with larger time scales (century, decade) and working your way down to date/time. In each of those date chunks, you can also include tertiary information such as week-of-year, semester, day of the week, weekend, workdays, etc.

Applying retention is a best practice to avoid unnecessary storage of data that is seldom used for analysis. Avoid duplication of datasets. Create a layer of data for common business logic across consumption tables to avoid data duplication.

Consider a fractional data pipeline to take data from a raw state to its final purpose. There are great tools like dbt and airflow which can manage this. For example: - Tier 1: Raw Data from sources - Tier 2: Cleaned data from source - Tier 3: Business Marts (transformed and blended data sets for specific teams and use cases) - Tier 4: Summary tier with aggregate metrics for ultra-fast BI queries and cost optimization

While working on a retail sales analysis dataset for one of my grad courses, I faced a colossal dataset containing detailed transaction records spanning several years. To expedite the analysis and save computational resources, I applied data reduction techniques. Initially, I utilized random sampling to extract a manageable but representative subset of sales data. Subsequently, through aggregation, I summarized daily sales into monthly figures, reducing the dataset's granularity without losing key insights. Finally, dimensionality reduction helped me focus on the most influential sales channels, simplifying the analysis and enhancing decision-making speed while preserving essential information.

Automated Aggregations can be an Analyst's best friend - creating datasets that capture these aggregations that refresh on a regular basis (per your needs), rather than trying to do it all in Tableau/PowerBI saves a lot of performance, and makes your visualization workspace all the more cleaner!

One thing I have found helpful when managing larger dataset is defining your goals and setting your objectives. It can be difficult sometimes working with a large datasets without setting your goal. You can get stuck and frustrated and exhausted The objectives set will serve as a roadmap for getting insights needed. It will also help you in determine the right tools and resources to use for the analysis.

I have observed that the majority of people create reports and dashboards without a solid understanding of data visualization fundamentals. For instance, they may not be aware of the reasons to avoid using red and green together. Additionally, I've noticed individuals using funnel charts for categorical data. I recommend two books: 1. "Storytelling with Data" 2. "The Big Book of Dashboards"

Think of it like a picture book – pictures make the story more interesting. In the data world, those pictures are your charts, graphs, maps, and dashboards. They help you tell the story of your data in a way that's easy to grasp. But just having pictures isn't enough. You also need to be a good storyteller. That means sharing what you've learned with the right people, like your bosses, clients, or partners. They're like your eager audience waiting to hear your tale. To make sure your story hits the mark, you use the best tools and techniques for your audience and message. It's like using different voices and styles when you tell a story to kids versus adults.

Start by choosing the right visualization techniques, such as bar charts, line graphs, scatter plots, or heatmaps, depending on your data and objectives. Ensure your visualizations are clear and easy to interpret, using appropriate labels and legends. Use color coding and scales to highlight key points. When communicating your findings, create a narrative that tells a story, explaining the context, methodology, and the significance of your results. Tailor your message to your audience, simplifying complex technical details for non-experts and providing deeper insights for those with domain knowledge. Visual aids, such as graphs and charts, can support your narrative and make it more engaging.

From my experience of more than 3 years developing dashboards in Power BI. Comment that it's very important to understand and persuade as much as possible in what the owner wants to capture in the reports, identifying the amount of kpis and how they are calculated is vital. Then it's important to size the number of kpis that can be displayed in each sheet of the report, choosing the most appropriate graphics so that the information displayed is as simple and easy to understand as possible. For this point I recommend reading a little about UX/UI.

Emphasize storytelling in your visualization and communication. Storytelling can help in translating complex data insights into a narrative that is engaging and easy to understand for your audience. Utilize visualization tools that allow for interactive storytelling, enabling a more profound engagement with the data.

As my experience, when you work with a real large dataset (billions of records), you need to consider every aspect of best practices and focus on what is your main objectives. You may need to use deferent tools and environment to answer all user questions. But by using correct methods, you can navigate the challenges of working with large datasets more effectively.

One thing I found useful is creating aggregated data marts, it gives you an overview and general analytics, data marts are handy to use for BI visualizations.

If we find a large dataset we can handle it in multiple ways depending on the following: 1.If it is transactional data and it needs a delta load daily, we must think of opting for a migration to a large scalable database like snowflake, Azure databricks, Azure data lake, AWS cloud database, etc(Essentially it depends on volume of data your organisation handle.) 2. If that large dataset meant to be consumed by any data analytical platforms like tableau, powerbi, we have dedicated connection types to handle huge datasets like (Live connection mode) It streams live data from the connected data source. 3. Implementation of Query reduction techniques, Excellent and concise design of Data model helps dataset to perform in an efficient manner.

Data Partitioning: Divide data into smaller partitions to improve query and processing performance. Distributed Computing: Distribute data processing across multiple nodes or clusters to parallelize computations. Data Indexing: Create indexes to accelerate data retrieval operations. Data Security: Apply access controls and encryption to protect sensitive data. Data Archiving and Tiering: Move less frequently accessed data to lower-cost storage options. Scalability: Design systems to scale horizontally by adding more nodes or resources as data grows. Metadata Management: Maintain metadata catalogs to keep track of data lineage, schema changes, and data versions. Data Governance: Establish data governance policies and practices.