Cloud Data Warehouse Architecture

Cloud Data Warehouse Architecture – Amazon Redshift was announced in November 2012 and became the first cloud data warehouse, opening up an entirely new technology segment. What exactly is a cloud data warehouse?

While cloud data warehouses are relatively new, at least this decade old, the concept of a data warehouse is not. A data warehouse is a data warehouse designed to store large amounts of data over a long period of time. Centralize data from multiple systems into a single source of truth. It is often loaded in batches, with minimal updates, and read repeatedly. Look at this old diagram of a data warehouse.

Cloud Data Warehouse Architecture

Cloud Data Warehouse Architecture

Given the data processing needs of a data warehouse, they tend to be implemented in massively parallel processing (MPP) systems. The MPP architecture responds to a shared-nothing concept to distribute data across multiple segments. Compute nodes are layered on top of storage and process queries for data that resides on their local slice. The control node is responsible for taking a query and breaking it into smaller queries to run in parallel on the compute nodes.

Data Warehouse Vs. Data Lake Vs. Data Lakehouse: An Overview Of Three Cloud Data Storage Patterns

Before understanding cloud data storage, it is important to understand data storage devices. The term data storage device may have been coined by the founder of Neteeza, but the first data storage device was probably made by Teradata in 1990. Data storage devices follow the MPP architecture, which first emerged in the early 1990s. from the 1980s.

These data storage devices have been the best options for large-scale data processing for some time. There is also a variety of options. To name a few…

These appliances responded to business needs, but also offered staggering bills and challenging scale-out options that resulted in underutilized systems. There are a number of benefits to a cloud data warehouse, however I would say that the two most important reasons to consider a cloud data warehouse are to address the cost prohibitive nature of data warehouse appliances and to gain the enhanced flexibility that is cloud-native.

Modern data warehouse architecture hasn’t changed since the relic I showed you in the first section, but it has grown a lot. Instead of just ingesting data from a few operating systems, a data lake must be supported, third-party data, non-relational data, Internet of Things (IoT) data, social listening, machine learning, and predictive analytics are all in play.

Data Warehousing And Analytics

Each of the public cloud providers, with data storage offerings, have very different means of implementing the same MPP concept. However, a cloud data warehouse is more than just a cloud data warehouse device. These providers offer a platform and ecosystem to host and support the data warehouse in the cloud, connecting the warehouse with types/sources of data and services that are difficult to implement on-premises.

It is not possible to compare or list all services in this blog post due to the number of cloud data storage providers and the extent to which they vary in their services and features. However, I will highlight a few of my favorite features that are available natively from most cloud providers.

Companies like Microsoft, Amazon, and Google operate on such a large scale that, for the individual customer, there are no limits to the amount of data storage capacity and computing power that can be harnessed. These companies offer petabyte and exabyte scale solutions. By the time someone needs a zettabyte or a yottabyte, it will be available.

Cloud Data Warehouse Architecture

In addition to the potential scale, expanding an existing deployment is much easier and faster than on-premises. Each vendor implements their systems differently and meets requests for scaling at different degrees of speed and disruptive activity.

Getting Started With Snowflake Cloud Data Warehouse

With Amazon Redshift, you can add nodes to your cluster, with each node adding CPU, memory, and disk space. Storage capacity jumps in increments as small as 160GB and as large as 16TB. Adding a node requires data to be redistributed, which can take a couple of hours and provides compute power in a non-linear fashion.

With Microsoft Azure SQL Data Warehouse, storage scales seamlessly and computing power scales by adding data warehouse units (DWUs). Under the covers, more Azure SQL databases are brought up to support performance, but the details are more abstracted than with Redshift. Scale operations offer a linear increase in performance and are disruptive, but they only take minutes because you don’t need to redistribute data.

By leasing their hardware on a large scale, cloud providers don’t need to force their customers to sign contracts to use it. This means that you can scale up and down to suit your needs. As mentioned once before, each cloud provider will offer different types of services and capabilities, but again, I’ll use Microsoft and Amazon as examples.

ADW requires all connections to be removed when scaling but other than that it will only take a few minutes to scale up or down. This opens up opportunities to perform scale operations multiple times a day. You no longer need hardware to meet your peak hours and then underutilize that hardware for the rest of the day/week/month. You can, for example, scale up a large set of ingestion processes over the weekend and then scale back for the rest of the week to save money.

A Cost Effective Data Warehouse Solution In Cdp Public Cloud

Redshift also provides the ability to scale up and down. In Redshift, you do not need to kill queries to add nodes and the process is online, but it puts the system in a read-only state until the data redeployment process is complete. Downscaling takes a bit longer. You must take a snapshot of the cluster and restore from the snapshot to a smaller cluster. This can take longer than ADW, but is much faster than ordering hardware and provisioning it on-premises.

Cloud data warehouses are platform-as-a-service offerings. That means the cloud provider is managing the infrastructure while you work alone within the data storage software itself. For some, this is scary. You will no longer control when patches are applied to the underlying hardware. However, you never have to worry about patching yourself. Everyone is way above their game too. This post shows that the entire Azure infrastructure was patched on the same day the Meltdown vulnerability was announced. AWS and the other providers reacted similarly, making their servers more secure than most other local data centers for many months to come.

In addition to patches, they handle seamless end-of-life hardware replacement. When new hardware is provisioned, you may see performance gains that you didn’t have to pay for because new generations of hardware perform better natively. Ultimately, these cloud providers can afford to invest heavily in engineering/optimizing their data centers and continue to drive down costs. Customers typically never see an increase in prices, only a decrease, unless you choose to raise your performance level.

Cloud Data Warehouse Architecture

I’ve talked to a couple of cloud providers on a pre-sales basis. With each company, they pushed hard to sell me their roadmap and the value of the cloud ecosystem. There is so much emphasis on this because entering into a relationship with a cloud provider is the same as working with a large development team that will build their services and products for free and then hope you like it enough to start using it. They innovate and push the cutting edge and then you can choose which pieces were successful and which pieces meet your business needs.

What Is A Data Mesh — And How Not To Mesh It Up

If one day your company decides it wants to incorporate R for predictive analytics, then the cost of entry is extremely low. Depending on the provider, you can be up and running in less than an hour with seamless integration with your data warehouse.

Cloud data warehouses are an exciting and evolving segment of technology. There is great value for any business in need of a data warehouse and it is attractive to organizations with existing data warehouse appliances that are nearing the end of their useful life. When researching the various cloud providers, make sure you understand what’s most important to your business. Each provider has its pros/cons. There is no single clear winner among them. Having a weighted list of features and service level goals is critical when choosing a provider. I also recommend a full-scale proof of concept with your top 2-3 vendors before making a final decision. Data warehouses, whether on premises or in the cloud, are a significant investment, with significant value. The growth of data from terabytes to petabytes or even exabytes brought with it the separation of storage and compute, and a modernized data warehouse, where the data warehouse makes it possible to extract insights from all the data.

BigQuery – Data warehouse (modernized) to collect structured, semi-structured, and unstructured data for data mining. It provides aspects such as Scalability, Performance, Security, etc.

Digital transformation requires that we understand data and use it innovatively and efficiently; the key component that can help in

Google Cloud Bigquery

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