How do you profile data in large datasets?

Data profiling checks big data by looking at each part and how values are spread. It checks for weird or missing stuff, finds connections between things, and looks for mistakes or repeats. Tools speed up the process, handling lots of data, while pictures help understand it better. Then, everything found gets written down and shared for more study.

Profiling data in large datasets is akin to a chef who prefers to personally select and inspect each ingredient before crafting a culinary masterpiece. In the realm of data science, I share a similar preference for hands-on involvement in data extraction, EDA, and profiling. Much like sourcing ingredients directly allows a chef to better understand their quality and characteristics, working directly with the data enables me to gain a deeper understanding of its nuances and challenges. In many scenarios, we rely on data from secondary sources, and the data obtained from these diverse origins can be comparably messy to a chef dealing with ingredients from different suppliers. Hence, a systematic approach to data profiling becomes essential.

While these data profiling tools offer comprehensive features for data quality and analysis, it's important to align the choice of tool with your specific data infrastructure and project requirements. For instance, organizations heavily invested in Microsoft's ecosystem might find SQL Server DQS more integrable with their existing systems. Similarly, for those handling big data projects, Apache Griffin's capabilities in handling large-scale data quality measurements could be more beneficial. Therefore, it's crucial to evaluate not just the features of these tools but also their compatibility with your data environment and business objectives.

The selection of data profiling is usually the 2nd step in data ingestion; the first typically being the selection of right data sources and the right tools that help you with data sourcing. And, there's typically another set of tools that process the data. It's pretty clear by now, that avoiding multiple tools to do narrowly focused functions is not a good tech investment. Hence, the selected set of tools that perform all three functions—data sourcing & cleaning, data profiling, and data ingestion—stands the test of time. Excluding unique scenarios, Apache Griffin and Talend DF have been quite successful as data profiling tools.

I found Ataccama ONE is great tool to perform DQ profiling even on big datasets. It provides good coverage to connect with variety of data sources. What I really liked about it's profiling is it's simplicity to perform the profiling task and the output is easy to interpret. It also provides DQ rules options for validating the attribute which will be very useful to apply rules on specific situation. Results can be downloaded and shareable. It also has web based tool which is more robust to implement DQ rules in large organziation. When you don't have the right tools to handle big data, it's always good to take sampling to identify patterns and issues. 10% is usually a good sample size to start with.

In my experience When datasets are too large to process in full, consider using a representative sample. Ensure that the sample is statistically significant and representative of the entire dataset.

It's important to emphasize the iterative nature of data profiling. While these steps provide a comprehensive framework, data profiling is often a cyclical process, especially in dynamic data environments. After the initial profiling and subsequent actions, regular re-evaluation is necessary to adapt to changes in data sources, structures, and business requirements. This ongoing process helps maintain data quality and integrity over time.

One aspect I found useful is implementing early data quality checks (DQMs). It is imperative that you cannot correct at source as it is collected in the way convenient for providers. So, before ingesting the data into your warehouse like Redshift, you can apply DQMs. These shall be agnostic with file type, data type or ingestion method (ex: Data sync, API, file transfer, etc). At least 80% of profiling issues are resolved following 80-20 principle. If you could correct 20% of your errors while ingesting, it can fix 80% of your data. Using AI profiling tools while designing DQMs will enhance the speed of DQM execution and future proof for new sources of data

Due to cloud elasticity , similar process can be used for large or small datasets : ? Scalable Data Collection: Gather data from vast sources. ? Enhanced Metadata Analysis: Examine complex data types and structures. ? In-depth Data Structure Assessment: Analyze tables and schemas in big datasets. ? Extensive Statistical Summary: Generate key statistics for large volumes. ? Quality Assessment: Identify inconsistencies and outliers in vast data. ? Pattern Recognition: Detect patterns in large datasets. ? Relationship Analysis: Explore relationships in big data elements. ? Anomaly Detection: Spot unusual patterns in large-scale data. ? Big Data Cleaning Strategies: Develop scalable correction rules. ? Documentation & Reporting

Data quality improvement involves three key aspects: structure discovery, content discovery, and relationship discovery. Structure discovery focuses on ensuring a consistent data format throughout the database. Analysts employ processes like pattern matching to identify format-specific issues, such as missing values in phone numbers. Content discovery involves a closer examination of data rows to uncover errors or systemic issues, providing insights into individual elements of the database and aiding in identifying incorrect values. Relationship discovery revolves around understanding the connections between different data sets.

Identifying data ownership is a crucial step in large enterprises as the data owner can help in baselining and creating guardrails for data quality. Also, issues and alerts found during monitoring can be addressed to the data owner to create accountability for ensuring long term data cleanliness. Any new data brought into the data warehouse should have a data owner defined and documented as a best practice.

For large data set sampling is one of the best approach but you need to be very careful how years selecting the samples. Typically this is done, selecting the data across various periods like years or months, and we should also know what are the significant stage at which the data may have the data quality issues like at the initial implementation state, any point where the data has undergone changes due to changes in the system or due to business process change.

While defining the scope and objectives of a data profiling project, it's crucial to incorporate a flexible and iterative approach. Data environments are often dynamic, and new data quality issues can emerge as data evolves. Therefore, it's recommended to periodically revisit and adjust the data profiling strategy to accommodate changes in data sources, structures, and business requirements. In addition, when documenting the data profiling process and findings, it's beneficial to create a comprehensive data quality framework that includes metrics and thresholds for data quality dimensions like completeness, consistency, and accuracy.

Its imperative to have the scope defined and understood to kick start a data profiling project. Identifying the right technology/tool, data systems, sets, techniques are important aspects of the profiling exercise. While the out-of-box profiling reports do give insights of the data, these may be too technical and hence a simple English representation of the profiling report would surely help in the introspection of the data and decide the way forward for the necessary data quality corrective action.

Moreover, integrating data profiling as a continuous practice, rather than a one-time event, can significantly enhance the long-term quality and usability of your data warehousing solutions. Regularly revisiting and updating the profiling strategy in response to new data sources, changing business needs, or evolving data structures can help maintain data integrity and relevance, ensuring that the data warehouse remains a robust asset for decision-making and analytics.

In the rapidly evolving landscape of data management and analytics, it's crucial to stay informed about emerging technologies and methodologies. For instance, advancements in AI and machine learning are increasingly being integrated into data profiling and warehousing processes, offering more sophisticated ways to analyze and interpret large datasets. Additionally, as data privacy and security concerns continue to grow, incorporating robust security measures and compliance with data regulations should be an integral part of any data warehousing strategy. Keeping abreast of these developments and incorporating them into your practices can provide a competitive edge and ensure the long-term sustainability of your data infrastructure.

Example:- Netflix faces high customer churn. A dataset with customer information is analyzed to understand factors causing churn and propose retention strategies. Data Profiling: 1. Descriptive Stats: Mean, median, and SD for charges, tenure. Analyze monthly charges distribution. 2. Data Distribution: Visualize charge and tenure distributions, identify outliers. 3. Data Type: Check data types, assess contract type distribution. 4. Missing Values: Handle missing data, assess impact. 5. Data Quality: Check duplicates, explore tenure-churn relationship. 6. Patterns: Use correlation matrices, explore behavior patterns. This analysis guides strategies to reduce churn, improve customer satisfaction, and enhance company's overall performance.

In the era of extensive data storage in the cloud, prioritizing effective data profiling is crucial advice for companies. The prevalence of cloud-based data lakes, capable of storing vast amounts of data, along with the expanding realm of the Internet of Things (IoT), collecting diverse information from various sources, underscores the importance of this practice. To remain competitive in today's marketplace, where cloud-native big data capabilities play a central role, fully harness the potential of available data. Whether it's ensuring compliance standards or establishing a brand renowned for exceptional customer service, the application of data profiling emerges as the decisive factor between success and failure in managing data stores.

Data profiling is an iterative process that involves a combination of statistical analysis, visualization, and domain expertise. Utilizing a variety of techniques and tools allows for a comprehensive understanding of the data, aiding in decision-making and data preparation for further analysis or modeling.

Keep it simple. Do quick iterations showing your work to your stakeholders often. Don't be afraid to mockup things in excel before coding something.