Is it true that data lake is surpassing the data warehouse as user base?
The strengths of the on-demand data warehouse are comparable to those of other on-demand software (SaaS) services, including greater ease of use (especially for proofs of concept) and the elimination of associated responsibilities to version management. The service provider allows the customer to access the data warehouse over the Internet, through programming interfaces (APIs).
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Any organization that is considering building a data warehouse can choose to use DWaaS for its cloud analytics solutions. For data generated and accessible in the cloud, the choice of DWaaS seems more logical than that of an on-premises warehouse. On the other hand, for very large volumes of on-site data, whose transfer to the Cloud would take days or even weeks, some service providers ship storage media to the customer: once the data is loaded on it, these media are returned to the provider.
Even if the data warehouse is dedicated to decision-making needs, and brings together information from all types of sources such as operational production systems, ERP, accounting and financial management, sales, and purchasing, business decision-makers do not always have awareness of its value. However, its functions are not limited to copying operational data. The data is cleaned, contextualized and structured there automatically, according to a repository specific to the company and common to all the businesses.
And if we talk about the data lake then according to experts, a data lake is a scalable system for storing and processing data. Data comes in a variety of formats and is kept in its original state. Data Lakes are mostly used by data scientists, data scientists, and data analysts to extract knowledge and perform predictive analyses. The emergence of the concept of Data Lake has accelerated thanks to the convergence of the need for unifying platforms in companies and new economic technical means provided by big data technologies.
Concepts related to the movement of Big Data, the data lake is a space storing global information present within an organization. It’s about doing it with enough flexibility to interact with the data, whether it’s raw or very refined. One of the keys to this flexibility is the absence of a strict pattern imposed on incoming flows. This option allows you to insert all data, whatever their nature and origin. Beyond storage, one of the challenges of the data lake is to be able to very easily process and transform information in order to accelerate innovation cycles, and thus be a support for various data initiatives.
How is it different from a data warehouse?
The temptation is very often strong to compare the data lake to a classic data warehouse, but the differences between the two are significant, and this on several levels. The data lake aims to absorb raw data flows and make them usable by transforming them to meet different analysis needs. In the end, this remains extremely classic and does not add anything new to what the “ETL – datawarehouse – datamart” trio could do.
This new approach is different, however, in that it allows data to be loaded and then transformed to make it usable. Data initiatives are very often limited by the difficulties inherent in the collection and ingestion phases in the systems. On this point, being able to load the data onto a platform in a near-raw state, and iterate quickly to use it is a definite advantage. We often talk about an ELT (Extract-Load-Transform) approach rather than ETL (Extract-Transform-Load) that we were used to. With the help of Snowflake remote support from a well versed company such as Ducima Analytics, you can access the data lake and data warehouse without any confusion and effort.
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