There has been ongoing rigorous debate regarding the demise of the data warehouse, as data lakes continue to gain traction and prevalence. The adoption of cloud storage is also certainly transforming the traditional conception of what a data warehouse is, and the data warehouses of yesterday will need to be modernized to meet the needs of tomorrow. But contrary to popular belief, traditional data warehouses aren’t dead, nor is the data lake rendering them obsolete. Not all companies are prepared to make the jump to data lakes, nor do some even have a need for a big data environment based on size and scale. Perhaps one day this will be true, but right now data warehouses still play a vital role in serving the data management needs of organizations worldwide. And while some might be calling time of death because their organizations are focusing on the data lake model, the impression that the new, hot concept is bulldozing the older technology out of the way is simply misleading.
The reality is that the data warehouse and data lake models can comfortably coexist side by side, and should be viewed as symbiotic companions that together can tackle your data management needs. Where the data warehouse falls short, the data lake fills in the gaps and vice versa.
Since data warehouses do not have nearly the storage capacity that data lakes do, a considerable amount of time must be spent on deciding what data goes into the data warehouse and what doesn’t. Data warehouses also allow organizations to separate their data based on needs and even maturity of data. This allows different departments, within a single organization, to pull only the information they need without having to sift through unnecessary data.
In addition, data warehouses only store structured data, unlike data lakes which store raw, unstructured, semi-structured and structured data, giving the organization greater flexibility. However, because the data within a warehouse is structured, it is typically designed to be a trusted source of information that can be used as a master ledger or source of truth for auditing purposes because of the refined, high-quality data which is a huge advantage. But as the volume of unstructured data increases, organizations will increasingly be forced to implement data lakes as the first line of data collection.
The architecture, content, and structure of a data lake have traditionally been determined on the type of analytics project an organization is attempting to execute. When organizations cannot store and process data with a conventional data warehouse architecture, it is time to turn to a data lake, which also requires data scientists or analysts with considerable expertise in order to find the proverbial needle in a haystack. However, with the proper solution, organizations can establish trust in their data lakes and empower every user to operationalize the insights generated from analyzing big data.
Big data within data lakes continue to remain unused or underutilized because organizations lack confidence in the quality of that data. Unlike data warehouses that contain structured and consumable data, the data that is being fed into a data lake is still raw and potentially unchecked. To ensure quality data, organizations need a big data quality platform that bridges the gap between the ingestion, processing, and consumption of big data. The platform should be able to validate data throughout each phase within any lake, seamlessly integrate the data-to-insights process, and empower every user to operationalize the insights generated from analyzing big data.
The solution should allow end users to easily profile and prepare data, pinpoint data issues, conduct multi-dimensional data quality checks, perform in-depth analysis, and put the results to work immediately. It should also be able process high volumes of data and give organizations confidence not only in the validity and trustworthiness of their data, but in every business decision driven by that data.
It is important to recognize that while both data warehouses and data lakes are storage repositories; data lakes are not “data warehouse 2.0.” The data warehouse model has always been the foundation for organizations looking to uncover insights from their data, and they can still fulfill this important role. As an easily accessible repository of organized, structured data assets, data warehouses serve the organizational needs of both speed and efficiency. Data lakes today can either be a standalone solution, or serve as the first line of collection for data warehouses and not as a replacement for them. Data lakes and data warehouse are two different technologies that, when paired correctly, can properly serve varied business needs.
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