Data models are a lot like sellable products: building them is an investment based on the understanding of consumers and working with stakeholders. Data model or product usage can be measured, consumers interviewed, and business… Read More »Product Thinking in Data Modelling
Azure Boards tool has a lot of options and ways to organize work. Here are discussed some of the key options in Azure Boards to ease the prioritization and implementation of analytics and data engineering… Read More »How to Manage Analytics and Data Engineering Work with Azure Boards
When an analytics team follows the same working, documentation, and communication practices the team members have an easier time understanding each other as there is a single source of truth. Furthermore, solutions are accessible, transparent,… Read More »Operating Model for an Analytics Team
Spark SQL, for example in Databricks, is different to T-SQL in Microsoft SQL Server. Below are some examples of the differences, considering dates. In general, Spark SQL and T-SQL are relatively similar, date queries are… Read More »Spark SQL vs T-SQL Date Query Differences
Data warehouse, data lake and delta lake data platform architectures behave differently in the terms of data upserts or merge, and file partition compaction management. Big data often requires inserts, updates, or other incremental data… Read More »Data Lake VS Delta Lake – Data Upsert and Partition Compaction Management