Thanks to the frequent and common use we are now giving to digital applications, big data is an issue general managers face today as part of their strategic planning. It is imperative that business leaders embrace a proper data management system, so they can make their business become sustainable in the long term.
The storage, processing and handling of information is an essential matter in any business in this decade. Through the process of creating a Data Architecture scheme, it is strongly recommended to consider implementing a Data Warehouse engine as a key step to achieve an optimal, reliable and scalable data pipeline. Enabling so, you will be enhancing the use of data analytics tools and allowing your business to move forward to advanced analytics techniques adoption.
A Data Warehouse is a system that joins together different data sources, aggregates and combines them into an unique and centralized storage unit, that allows organizations to deploy different use cases that add value to their core business. This enables the maximization of data in different ways including business intelligence, data mining, artificial intelligence (AI) and machine learning. By having a Data Warehouse service, companies can perform complex analysis of large volumes (petabytes and petabytes) of data and get actionables insights.
So basically, a Data Warehouse (DWH) facilitates companies’ ability to access accurate information so that key decision makers are able to make timely and data-driven decisions.
David Florian, Chief Data Officer at Datavalue has written an extensive and detailed report about the current platforms that offer DWH, and selected the top three performers according to the most recent Forrester Wave Q1, 2021 report, which are: BigQuery, part of Google Cloud Platform, Redshift, part of AWS services, and Snowflake, an independent provider.
So, what are the advantages that each one of these platforms offers?
Big query is a fully automated system that doesn’t require a programmer or Data Ware House manager to jump into compute scaling. The system assigns computing machines to each query according to its own internal calculations.
Redshift and Snowflake, on the other hand, are less autonomous by nature and require human intervention for scaling. In Redshift, a different warehouse can be used for each query, and operators can quickly change from one to the following query directly from the interface or the DDL. In Snowflake, scaling is performed at the cluster level, consisting of compute nodes, which in turn makes the process even more manual. A different database is used for each cluster. In addition, Snowflake is not a server-less service, unlike BigQuery and Redshift.
If we consider the mechanics that are used to perform queries, the method of internal data conversion, connection times, the restrictions on the amount of data that can be loaded, the costs of executions and transfers, the automation of the scaling used, the existence of dynamic computation management, among other elements, means that each platform has its own advantages and disadvantages.
If you want to know more about these three platforms, leave us your email and you will get the complete original report. Once you dive deep into it you´ll have a better idea of what data warehouse deployment is and how it will sum up in your advanced analytics use cases. As a result, you´ll be able to evaluate what you need to implement and which one best meets your company's unique IT needs.
The conclusion's second task is to make a final comment that supports the thesis in a memorable way. The closing lines of a paper must therefore place the thesis in a larger context by showing its significance within the field of study.
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