"Data Lake is a single window snapshot of all enterprise data in its raw format, be it structured, semi-structured, or unstructured. Starting from curating the data ingestion pipeline to the transformation layer for analytical consumption, every aspect of data gets addressed in a data lake ecosystem. It is supposed to hold enormous volumes of data of varied structures." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018
"The quality of data that flows within a data pipeline is as important as the functionality of the pipeline. If the data that flows within the pipeline is not a valid representation of the source data set(s), the pipeline doesn’t serve any real purpose. It’s very important to incorporate data quality checks within different phases of the pipeline. These checks should verify the correctness of data at every phase of the pipeline. There should be clear isolation between checks at different parts of the pipeline. The checks include checks like row count, structure, and data type validation." (Saurabh Gupta et al, "Practical Enterprise Data Lake Insights", 2018)
"For advanced analytics, a well-designed data pipeline is a prerequisite, so a large part of your focus should be on automation. This is also the most difficult work. To be successful, you need to stitch everything together." (Piethein Strengholt, "Data Management at Scale: Best Practices for Enterprise Architecture", 2020)
"A data pipeline is a series of transformation steps (functions) executed as the data flows from one step to another. Data mesh refrains from using pipelines as a top-level architectural paradigm and in between data products. The challenge with pipelines as currently used is that they don’t create clear interfaces, contracts, and abstractions that can be maintained easily as the pipeline complexity complexity grows. Due to lack of abstractions, single failure in the pipeline causes cascading failures." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)
"Data lake architecture suffers from complexity and deterioration. It creates complex and unwieldy pipelines of batch or streaming jobs operated by a central team of hyper-specialized data engineers. It deteriorates over time. Its unmanaged datasets, which are often untrusted and inaccessible, provide little value. The data lineage and dependencies are obscured and hard to track." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)
"Data mesh [...] reduces points of centralization that act as coordination bottlenecks. It finds a new way of decomposing the data architecture without slowing the organization down with synchronizations. It removes the gap between where the data originates and where it gets used and removes the accidental complexities - aka pipelines - that happen in between the two planes of data. Data mesh departs from data myths such as a single source of truth, or one tightly controlled canonical data model." (Zhamak Dehghani, "Data Mesh: Delivering Data-Driven Value at Scale", 2021)
"Data has historically been treated as a second-class citizen, as a form of exhaust or by-product emitted by business applications. This application-first thinking remains the major source of problems in today’s computing environments, leading to ad hoc data pipelines, cobbled together data access mechanisms, and inconsistent sources of similar-yet-different truths. Data mesh addresses these shortcomings head-on, by fundamentally altering the relationships we have with our data. Instead of a secondary by-product, data, and the access to it, is promoted to a first-class citizen on par with any other business service." (Adam Bellemare,"Building an Event-Driven Data Mesh: Patterns for Designing and Building Event-Driven Architectures", 2023)
"Gaining more insight into data, simplifying data access, enabling shopping-for-data, augmenting traditional data governance, generating active metadata, and accelerating development of products and services are enabled by infusing AI into the Data Fabric architecture. An AI-infused Data Fabric is not only leveraging AI but also likewise an architecture to manage and deal with AI artefacts, including AI models, pipelines, etc." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
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