Follow these data dictionary best practices: document each field’s meaning, format, and valid values; use consistent names; assign an owner; resolve conflicting definitions; and update the documentation whenever your data changes. Start with one important dataset, publish the dictionary where…
Continue reading Data Dictionary Best Practices: How to Build and Maintain One
In the world of data and analytics, data observability is an important but often overlooked process.Much of what goes on in data science today isn’t observable because we built most data pipelines to move data, not monitor it. In the…
Continue reading Data Observability Tools: Enhancing Data Quality & Insights
On a high-level, ETL transforms your data before loading, while ELT transforms data only after loading to your warehouse.In this post, we’ll look in detail at the differences between how ETL and ELT work to help you identify which process is…
Continue reading ETL vs ELT: Understanding the Key Differences
When it comes to storing data, we generally have two options: SQL (relational databases) and NoSQL (non-relational databases).The idea for SQL was first introduced in 1970 by Edgar F. Codd in his model for relational database management. This type of…
In our last post, , our intrepid analyst Regina was building a dashboard for her company’s management team. Regina works at a fictitious coffee delivery company named Mocha. One of Mocha’s projects is to acquire more specialty coffee houses to…
Continue reading Data Visualization 101: How Gestalt Principles Yield Better Charts
Data transformation tools help change data formats, apply business logic, and otherwise play the “T” role in ETL. This is especially important when consolidating both structured and unstructured data from disparate sources for analysis. These tools also allow you to…
Continue reading Best Data Transformation Tools | Top Solutions for Data Processing
Companies today are increasingly collecting large volumes of data from cloud services, mobile applications, and connected devices. Yet many struggle with deploying data. This prevents them from enjoying the full benefits of their digital investments. According to one recent study, just…
For years, Microsoft has been a leader in Gartner’s quadrant for intelligence and analytics platforms. In other words: it has a complete vision and can execute it.While Microsoft offers an extensive suite of business intelligence solutions, SSIS (SQL Server Integration…
Continue reading How to Visualize DynamoDB Data in Power BI | Analysis Guide
Quick verdict: Choose DynamoDB if you’re building on AWS, know your main access patterns, and want a managed database without maintaining servers. Consider MongoDB Atlas if your application needs flexible queries, aggregations, or managed hosting across AWS, Azure, and Google…
Continue reading DynamoDB vs MongoDB: Performance Comparison & Cost Analysis
Data blending combines information from multiple sources for analysis, often within a report rather than a new stored dataset. To do it reliably, identify the question, match shared fields, align what each row represents, clean the data, and validate the…
Continue reading What Is Data Blending? How to Blend Multiple Data Sources
One of the significant challenges to data-driven companies comes when integrating different application systems.Major public cloud providers, such as Amazon or Google, offer robust products that are ready for your analytics needs. But when it comes to exploring your data,…
Continue reading AWS Spectrum, Athena, and S3: Everything You Need to Know
Imagine that you are in a meeting with a potential investor, and they ask you for specific information about your company to help them decide whether to invest in your company or not. Maybe they ask you for sales records,…
