"Unfortunately, just collecting the data in one place and making it easily available isn’t enough. When operational data from transactions is loaded into the data warehouse, it often contains missing or inaccurate data. How good or bad the data is a function of the amount of input checking done in the application that generates the transaction. Unfortunately, many deployed applications are less than stellar when it comes to validating the inputs. To overcome this problem, the operational data must go through a 'cleansing' process, which takes care of missing or out-of-range values. If this cleansing step is not done before the data is loaded into the data warehouse, it will have to be performed repeatedly whenever that data is used in a data mining operation." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)
"More and more data is exchanged between the systems through real-time (or near real-time) interfaces. As soon as the data enters one database, it triggers procedures necessary to send transactions to Other downstream databases. The advantage is immediate propagation of data to all relevant databases. Data is less likely to be out-of-sync. [...] The basic problem is that data is propagated too fast. There is little time to verify that the data is accurate. At best, the validity of individual attributes is usually checked. Even if a data problem can be identified. there is often nobody at the other end of the line to react. The transaction must be either accepted or rejectcd (whatever the consequences). If data is rejected, it may be lost forever!" (Arkady Maydanchik, "Data Quality Assessment", 2007)
"Data lakes have been in existence for a while now, so their need is no longer questioned. What is more relevant is the specifics of the solution's implementation. Consolidating all the siloed data by itself does not constitute a data lake. However, it is a starting point. Layering in governance makes the data consumable and is a step toward a curated data lake. Big data systems provide scale out of the box but force us to make some accommodations for data quality. Age-old aspects of transactional integrity were compromised on a distributed system because it was very hard to maintain ACID compliance. Due to this, BASE properties were favored. All of this was moving the needle in the wrong direction and from pristine data lakes we were moving toward data swamps, where the data could not be trusted and hence insights that were generated on the data could not be trusted either." (Anindita Mahapatra, "Simplifying Data Engineering and Analytics with Delta", 2022)
"A Data Fabric needs to serve analytical and transactional data consumption patterns to, for instance, address MLOps, trustworthy AI, MDM, inferencing, IoT, edge, and 5G." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Data products should remain stable and be decoupled from the operational/transactional applications. This requires a mechanism for detecting schema drift, and avoiding disruptive changes. It also requires versioning and, in some cases, independent pipelines to run in parallel, giving your data consumers time to migrate from one version to another." (Piethein Strengholt, "Data Management at Scale: Modern Data Architecture with Data Mesh and Data Fabric" 2nd Ed., 2023)
"Delta Lake brings capabilities such as transactional reliability and support for UPSERTs and MERGEs to data lakes while maintaining the dynamic horizontal scalability and separation of storage and compute of data lakes. Delta Lake is one solution for building data lakehouses, an open data architecture combining the best of data warehouses and data lakes." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)
"Like data lakes, the lakehouse architecture leverages low-cost cloud storage systems with the inherent flexibility and horizontal scalability of those systems. The goal of a lakehouse is to use existing high-performance data formats, such as Parquet, while also enabling ACID transactions (and other features). To add these capabilities, lakehouses use an open-table format, which adds features like ACID transactions, record-level operations, indexing, and key metadata to those existing data formats. This enables data assets stored on low-cost storage systems to have the same reliability that used to be exclusive to the domain of an RDBMS. Delta Lake is an example of an open-table format that supports these types of capabilities." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)
"The Data Fabric architecture needs to guarantee this single version of the truth within the application and transactional landscape, which – depending on the deployment option of an MDM solution – could also mean to assemble this single version of the truth based on core information that is dispersed and maintained in various data stores." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"The main goal of the transaction log is to enable multiple readers and writers to operate on a given version of a dataset file simultaneously and to provide additional information, like data skipping indexes to the execution engine for more performant operations. The Delta Lake transaction log always shows the user a consistent view of the data and serves as a single source of truth. It is the central repository that tracks all changes the user makes to a Delta table." (Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)
"When you leverage Delta Lake with Structured Streaming, you get both the transactional guarantees of Delta Lake and the powerful programming model of Apache Spark Structured Streaming. With Delta Lake, you can now use Delta tables as both streaming sources and sinks, enabling a continuous processing model that processes your data through the Raw, Bronze, Silver, and Gold data lake layers in a streaming fashion, eliminating the need for batch jobs, resulting in a simplified solution architecture."(Bennie Haelen & Dan Davis, "Delta Lake: Up and Running - Modern Data Lakehouse Architectures with Delta Lake", 2023)
"Delta Lake is a transactional storage software layer that runs on top of an existing data lake and adds RDW-like features that improve the lake’s reliability, security, and performance. Delta Lake itself is not storage. In most cases, it’s easy to turn a data lake into a Delta Lake; all you need to do is specify, when you are storing data to your data lake, that you want to save it in Delta Lake format (as opposed to other formats, like CSV or JSON)." (James Serra, "Deciphering Data Architectures", 2024)
No comments:
Post a Comment