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Cloud data warehouse

Cloud data warehouse

7 October 2022SHARE:
Written by:
Antony Desmond
COO | Director of Operational Strategy

Antony helps organisations to become more efficient by looking at their operational problems

Many mission-critical business applications, such as databases, ERPs, and marketing systems, have moved to the cloud with the development of contemporary cloud infrastructure. As a result, the majority of business-critical data are now stored in the cloud. Companies require a data warehouse that can easily store the data from all the many cloud-based apps now that all company data is stored on the cloud. The Cloud Data Warehouse enters the scene in this situation.

This article seeks to clarify the definition of a cloud data warehouse as well as its purpose.

What is a cloud Data Warehouse?

Instead of being used for transactional system activities, a data warehouse runs on a customized database that is created and optimized for data warehouse operations. Transactional systems, relational databases, line of business applications, and other sources all contribute data to a data warehouse on a regular basis. A data warehouse focuses on the quality and presentation of the data while providing the business with actual data assets that can be used and consumed.

For decades, data warehouses have been essential components of enterprise analytics and reporting. However, they were not built to handle today's tremendous data expansion or to keep up with end users' constantly changing needs. As a result, the need to be bound by physical data centers expired with cloud data warehousing, and you can now dynamically build or downsize your data warehouses to meet changing company budgets and requirements. A cloud data warehouse, like a typical data warehouse, holds information from various disparate data sources such as IoT, CRM, finance systems, and many others.

A cloud-based data warehouse's data is highly structured and unified, allowing it to support a wide range of unique business intelligence and analytics use cases.

  • Massive Parallel Processing (MPP): MPP architectures are used to enable high-performance searches on enormous data volumes in cloud-based data warehouses that serve big data initiatives. Multiple servers run in parallel to distribute processing and input/output (I/O) demands in MPP designs.

  • MPP data warehouses are typically columnar stores, which are the most versatile and cost-effective for analytics. Columnar databases store and process data in columns rather than rows, resulting in substantially faster aggregate queries, which are often used for reporting.

Traditional Data Warehouse vs. Cloud Data Warehouse

A traditional data warehouse is physically installed on-premises. Companies must purchase their own hardware, such as servers. Installation necessitates both human resources and a significant amount of time. The Traditional Data Warehouse must be managed and updated by a distinct team within the firm and it takes time to scale the Warehouse since additional hardware must be sent to the destination and then installed.

For cloud warehouses, however, all hardware updates, maintenance, and scalability are controlled by third-party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, and others. Companies can simply combine Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics due to the availability of data on the cloud.

Cloud data warehouse

Benefits vs Cons

  • Cloud-based Data Warehousing services are available at a range of prices that are a fraction of what the prior solutions would have cost in terms of capital, time, and stress.

  • Cloud-based data warehouse systems provided scalability in addition to ease of setup. Previous iterations would necessitate the development of capacity that took into account potential future growth.

  • With cloud-based data warehouses, your package can be quickly adjusted to your demands, regardless of how they change over time (as long as they remain within the service's constraints).

  • Cloud-based data warehousing security is an issue. This is mostly because service providers have access to their customers' data. While service agreements and public legislation concerning data privacy exist, it is possible that these companies may, inadvertently or intentionally, change or delete the data.

  • Another important security worry is the penetration of cloud systems by hackers, who are continually looking for and exploiting flaws in these systems in order to obtain access to users' personal data and corporate data. Providers take every step to protect their customers' data. To that end, customers are given options for how their data is stored, such as having it encrypted to prevent illegal access.

  • Given the wide range of apps that businesses use today, engineers face a massive burden in putting all of this data in various formats into a data warehouse. However, fully-managed data integration systems such as Nortb Data assist to alleviate this issue by providing a simple, point-and-click platform for loading data to the warehouse.

Migration from a traditional data warehouse.

If you have already a physical data warehouse in place, moving it to the cloud is quite easy. Usually, solutions like mysql workbench or PostGreSQL PG4 have export options with Schema creation when importing into other database.

In case you need assistance, please contact us and we will glad you help you.

This is an important step. Migrating a physical database or databank to the cloud is easy but should be done by professionals. One of our clients ignored our advice and ended up deleting his database. Lucky him, we were able to reverse the database in time and save 97% out of it, but please be aware before exporting that you know what you are doing.

Cloud Data Warehouse Automation

Automation, optimisation and BI are the big words on the subject of data and B2B. The three have developed three different markets that most of the SMB's trend to look individually. When combined though, they create a part of ecosystem that scales faster and produces bigger results.

To accelerate the availability of analytics-ready data, certain modern data integration technologies automate the entire data warehouse lifecycle. A model-driven approach will also assist your data engineers in designing, deploying, managing, and cataloging purpose-built cloud data warehouses more quickly than traditional alternatives.

By automating the workflow of your data harvesting process, together with the data warehouse archiving processes, your business will have insights generated faster and more available on your data marts. The Ingestion and updating of data happens in real-time, thus creating the sense of speed and urge, and thus producing reliable enterprise data faster and positioning your company ahead of the competition.

You don't have a data warehouse but you are reading about it.Do you really need it?

This is a question that makes us decline 30% of people who decline us inquiring about data warehousing. Rule of thumb, if you cannot produce yet a business case and a business model for having the investment of a data warehouse, then your business does not need it yet.

Business models are a very tricky thing to do. Investing in a data warehouse can be costly but at the same time can be the point of turning over and hedging the competition. Even for a start-up can be the decisive moment of scaling operations by detecting the business activity flaws. A data warehouse in this case will reduce costs and thus improving liquidity.

If you do not know how to set up a business model that includes a data warehouse, please contact us and one of our consultants will gladly help you.

Some businesses and sectors require not only large-scale data analysis, but also ongoing and real-time analysis. Some service providers, for example, use real-time data to dynamically change prices throughout the day. Insurance businesses keep track of policies, sales, claims, payroll, and other information. Machine learning is also used to predict fraud. To improve the player experience, gaming firms must track and respond to user activity in real time. All of these tasks are made feasible by data warehouses.

If your company has or does any of the following, you're a likely candidate for a data warehouse:

  • Several sources providing disparate data '

  • Asynchronous and real-time big-data analysis and visualization

  • Streaming analytics powered by machine learning/AI

  • Custom report creation and ad hoc analysis

  • Data mining

  • Data Science

Do you want to start your Data-warehouse?

Great, the good news is we can help you. At nortb we manage multiple data warehouses and we have a unique proof of concept where our business model guarantees the customer that no one else ever will break into your data. We have capabilities of hosting your data in multiple hyperscallers, or on our own data centres or even in your own datacenter.

For security reasons, we developed a model based on blockchain technology and cryptography where we cluster blocks of the database and divide it in blocks that are hosted in different datacenter thus creating a security layer when accessing the data.

Are you interested in starting your datacenter please contact us today.

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