"A cloud data warehouse is an enterprise data warehouse offered as a managed service (PaaS) on public clouds with optimized integrations for data ingestion, analytics processing, and BI analytics." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"Churn refers to rapidly changing the activities and your plan when they are in flux - this is disruptive to your organization and slows your progress. Change refers to an inevitable movement in requirements and helps you plan for and execute this movement thoughtfully." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"Data mesh relies on a distributed architecture that consists of domains. Each domain is an independent unit of data and its associated storage and compute components. When an organization contains various product units, each with its own data needs, each product team owns a domain that is operated and governed independently by the product team. […] Data mesh has a unique value proposition, not just offering scale of infrastructure and scenarios but also helping shift the organization’s culture around data," (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"If there is one thing I strongly recommend, it is to invest in a cloud data lake and start collecting and processing data that you believe is useful to your organization today." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"It’s true that data and data strategy are critical to the organization; however, it’s also true that data by itself is a means to the end of business or customer impact unless you’re a provider of data or data-related services." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"Plan for customer impact, and prepare to learn and fine-tune as you progress. Make choices based on the impact they offer to customers, and stay consistent in your implementation while keeping open-minded for learnings. Especially if you are an early adopter of a technology, you can help develop the technology with the provider and thus get ample support from the technology provider in return. Similarly, identify highly motivated early adopters within your customer base and offer to develop your solution with them." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"Real-time stream processing refers to the ingestion, processing, and consumption of data with a specific focus on speed, targeting near real time - that is, almost instantaneous results. […] Real-time stream processing pipelines involve data that is arriving from its source at very high velocity; in other words, it is data that is streaming into the system, just like rain or a waterfall." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"The lakehouse provides a key advantage over the modern data warehouse by eliminating the need to have two places to store the same data. [...] Data lakehouses offer the key benefit of being able to run performant BI/SQL-based scenarios directly on the data lake, right alongside the other exploratory data science and machine learning scenarios." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"The promise of a cloud data lake architecture lies in the boundless diversity of scenarios that it enables." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"The very simple definition of cloud data lake storage is a service available as a cloud offering that can serve as a central repository for all kinds of data (structured, unstructured, and semistructured) and can support data and transactions at a large scale." (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
"When it comes to data lakes, some things usually stay constant: the storage and processing patterns. Change could come in any of the following ways: Adding new components and processing or consumption patterns to respond to new requirements. […] Optimizing existing architecture for better cost or performance" (Rukmani Gopalan, "The Cloud Data Lake: A Guide to Building Robust Cloud Data Architecture", 2022)
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