Textbook in PDF format The data landscape has undergone rapid evolution in recent years, necessitated by the exponential growth in information from an everexpanding variety and volume of data. As organizations deal with this big data surge, the existing infrastructure has struggled to harness its potential effectively. This has led architects and technology leaders to start conceptualizing new integrated systems that can adeptly consolidate the strengths of current data platforms. Let’s start with data warehouses. They provided immense value over decades for descriptive analytics and business intelligence use cases relying on predefined structured data. However, as the focus and needs expanded to predictive analytics and leveraging the latest machine learning advancements, the nature of workloads moved beyond what traditional warehouses could proficiently support. Descriptive analytics for business intelligence based on predefined datasets are no longer enough. Further varied data types such as unstructured, semi-structured, and streaming use cases require more extensive and agile processing than data warehouse infrastructures are designed for. The data lake concept therefore gained interest as an alternative to data warehouses, given its natural ability to ingest raw multistructured data quickly. One of the more popular technologies that was forefront of this was Hadoop and its ecosystem. However, lack of transactionality, data quality, and mixing modes inhibited unlocking the benefits promised by data lakes. The flexibility therefore came at the cost of governance, reliability, and vital enterprise capabilities. Consequently, the data lakes quickly turned into “data swamps.” Despite all these drawbacks, organizations with no better alternatives began using both these technologies in their data architecture: data warehouses for descriptive and business intelligence (BI) use cases and data lakes for AI/machine learning (ML) use cases with a variety of processing tools thrown in the mix (sometimes even a single tool for one use case). However, with two completely different systems, solving for two critical types of workloads started to be problematic. First, it created data silos, which necessitated moving data across the platforms and thus maintaining multiple copies of the same data. Second, the governance model of these disparate platforms was incompatible, thus requiring separate governance models for different systems. Finally, organizations started using different tools for BI and ML workloads, increasing operational efficiency and costs. Over time, the complexity of maintaining different systems increased. This is becoming not only costly but also slowing innovation. More than ever enterprises needed a unified data infrastructure capable of managing diverse information seamlessly through its entire lifecycle to serve exponentially expanding analytical use cases