Scene Preparation: Data Lake
The rise of big data and the challenges posed by traditional business solutions have inspired James Dixon to recall the term "data lake" (2010) in recent decades. The best solutions for data warehouses at their core promise to eliminate data forces by serving as an input repository that centralizes, organizes, and protects multiple data sources. It follows a read-only schema approach and can store structured, semi-structured, and unstructured data, typically on cloud storage platforms such as AWS S3.
These flexible storage solutions are becoming increasingly popular in modern societies, but a common misconception is that they include their analytics features. To be indexed, edited, queried, and analyzed, the data pool and algorithms must be connected to a combination of other cloud services and software tools. In a typical data warehouse architecture, the self-service data analysis module will be located in a cloud data warehouse. This is when an organization realizes the true benefits of data, but runs the full cost of its data resources.
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Rise of the Data Network
Until recently, data warehouses and data bars were the two leading solutions for enterprise data management. Last year, however, a new approach emerged - the concept of a "data network". It has become one of the most important rumors spoken of every day. Thoughtworks defines a data network as "the transition to a modern distributed architecture that uses platform thinking to create a self-service data infrastructure, to process data as a product."
This type of architecture supports the idea of ​​distributed data, where all data is accessible to those who have the right to access it. An important difference between a data lake and a data network is that in a data network, data does not have to be merged into a single data lake and can remain indifferent databases. As a result, the data network architecture combines multiple data sources and data quality, including data margins, into a single infrastructure.
Data Lake vs Data Mesh: What is the Difference?
Data leaks have been around for a long time since the first Hadoop build failed. While many industry experts still remember "data swamp" products, many innovations have taken place in this area since then. The new data process removes the limitations associated with traditional storage methods, infrastructure, and analytical access. Modern data pools are now native to the cloud and can be enabled to index many types of data and make this data easily accessible and accessible to a variety of stakeholders across the company.
Data retrieval on the data network is limited to the slowest query. For organizations that store more data but are looking for more efficient queries, it makes more sense to use a data market platform for internal analysis of the existing data network architecture. Some solutions can eliminate some of these problems that are known to data mesh architects. For example, cloud data platforms can virtually publish logical views of data to query within a data lake without complex extraction, transformation, and retrieval (ETL). This is a way to improve the democratization of data within an organization without the need for data scientists or data engineers.
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Because data pools and data network architectures use different approaches (eg data integration), these two strategies can be considered complementary and mutually optional. However, while everyone loves the vision of ubiquitous data, the reality is that companies are unaware of the demands to get there. Datamesh and data democratization are the same - you can't have a decentralized data architecture if there are security guards who restrict who has access. Therefore, to achieve this goal in a distributed data network, companies must first be able to allow free data flow within the organization, which is a natural by-product of data margins.
There is no one-size-fits-all solution to becoming a data-driven organization. For some, a data network is useful if they store their data in multiple databases, while those looking for a solution that allows queries without moving data can benefit from the data pool. . The desired goal of most organizations that benefit from one of these data management solutions is to have a unified analytics platform that provides powerful insights without the need for complex behind-the-scenes support from intermediaries. While many organizations are developing new ways to democratize access to data, this gap will be an important area to address in the coming years.
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