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In an era when what you know about your customer can easily make or break your business, the more information you can successfully gather and collect from your data warehouses, the better off you'll be. In the past, gaining access to useful data was notoriously difficult, requiring huge investments in hardware and expertise. Today, the sheer volume of data in any business has meant that companies have had to look beyond old-fashioned approaches to the new and disruptive potential of artificial intelligence.
Today's AI, machine learning and robust analytical platforms make it easier for businesses of any industry or size to unlock the insights hidden within their valuable customer information. With solutions like the Google Cloud, it's finally possible to eliminate the need for complex administrators to manage expensive data systems. The age of the on-premise big data solution is over, and agility is the weapon of choice for today's ever-evolving brands.
BigQuery: The Solution to the Data Management Problem
In the past, solutions for big data management weren't simple or cheap. Not only did businesses need to make a huge upfront investment in hardware and software, but they also had to find enough money to bring experts in data analytics into their staff too. Fortunately, there is a better solution, thanks to the specialists at Google. Google BigQuery is a fully-managed, cloud-based serverless data warehouse. Essentially, the system works by supporting analytics strategies in a huge-scale data environment.
BigQuery is a unique approach to data management because it scales the use of hardware and software in your system up or down according to the needs of your campaign. There's no need for massive investments into on-site infrastructure or database administrators. BigQuery helps to streamline your big data and storage requirements while minimizing the complexity and overheads of administration and hardware. Some of the most significant benefits of BigQuery for mid-market businesses include:
Accelerated time to value: Users will be able to access their data warehousing equipment online without needing expert-level administration in-house. This reduces the extra management work required to make the most of the data.
Simplicity and scalability: Complete all of your data warehousing and analytical tasks through a simple and effective interface, without extra infrastructure to manage. You can even scale your system up or down depending on cost, performance and size requirements.
Speed: With Google's BigQuery solution, you can scale your data needs up and down rapidly and ingest or expert datasets with amazing speeds using the Google Cloud Platform.
Security: Make sure that all of your projects are encrypted and protected with identity and access management support.
Reliability: Google's cloud and BigQuery give you access to always-on available, constant uptime running and geo replication across a wide selection of Google data centers.
All that, and Google BigQuery also comes with cost optimization built-in. You can either choose to pay a transparent flat rate or stick with pay-as-you-go pricing.
Ultra-Efficient Scalable Pricing with BigQuery
One of the biggest economic advantages of BigQuery is its scalability. The system adapts according to the needs of your datasets and projects so that you can get through data management processes as quickly and efficiently as possible. Once you've completed the required workload, BigQuery simply reallocates the necessary resources to other users and projects, so that you can get more done in your mid-market enterprise.
BigQuery is all about speed and efficiency in the cloud world. What's more, for enhanced durability and data performance, BigQuery provides exceptional reliability and availability through a geographic replication strategy that's highly transparent and doesn't require excess physical resources. Ultimately, BigQuery enables businesses of all shapes and sizes to overcome the challenges and complexities associated with designing, maintaining, and optimizing a highly resilient big data infrastructure.
With Google supporting your scalability, you can spin up thousands of servers, configure a highly-distributed big data service, and run an SQL query at the same time. With this solution, companies can focus more of their time on gaining valuable business insights. With the on-demand pricing model that Google BigQuery offers, companies can access a truly efficient and cloud-native approach to data management. This efficiency is fantastic for mid-market companies that rely on running complex analytical workloads.
BigQuery is unique in its pricing strategy because it offers both the convenience of a bottleneck-free and real-time resource allocation strategy with 100% resource utilization. Additionally, you only pay for the power that you consume and nothing more. On the other hand, most traditional data solutions in the industry can only manage between 25% or 50% utilization in their datasets, which means that your bill is often much higher than it needs to be.
The Economic Advantages of Google BigQuery
BigQuery offers a range of pricing options designed to help companies from any background to get the most out of their data strategy. For instance, some organizations would rather have cost predictability than cost efficiency, which is why BigQuery provides flat rate pricing alongside its pay-as-you-go option. With flat rate pricing you pay a simple flat monthly fee, and all of the queries that you send will be free.
Ultimately, BigQuery was both created and priced to offer customers in the mid-market enterprise the insight they need from their data warehouses, quickly, and in a cost-effective manner. According to an ESG economic value audit released by Google, BigQuery can deliver significant economic benefit opportunities to high-growth businesses. The report showed that customers see various cost benefits when comparing their new system to an on-premise based deployment with Hadoop, or the AWS RedShift cloud solution for big data. Some of the upfront capital investment savings that companies achieved included:
Google BigQuery eliminates the hardware investments required for an on-premise Hadoop deployment, as there is no need for licenses, networking infrastructure or nodes.
Google BigQuery reduces the amount of purchasing, planning, testing and configuration required before the company can start to see significant benefits from their big data strategy. This means that it's possible to achieve a quicker time to value.
Google BigQuery allowed companies to avoid their storage capacity being directly connected to memory and compute power, which was the case with both the on-premise Hadoop and AWS deployment. This means that organizations can scale more effectively, and according to the needs of their data budgets.
Google BigQuery meant that businesses of all sizes could reduce the amount of time they spent planning the overall size and requirements of their data warehousing deployment. In many cases, with Hadoop and AWS, businesses had a habit of "over provisioning" to accommodate the needs of their data warehouses in a worst-case scenario.
Google BigQuery provides companies with more pricing options to choose from. The majority of AWS and Hadoop customers often wanted to pay for their requirements up-front. If customers decide to buy their Google strategies up-front, they can benefit from 75% savings over on-demand pricing.
Cost Savings on the Cloud
One of the things that makes Google BigQuery such a good choice in terms of economics and scale is the fact that it's based and managed on the cloud. Cloud functionality is ephemeral. The cloud can spin up requirements, and reduce loads at the right times, according to shifts and changes in your digital environment. This means that businesses exclusively pay for when their functions are executing, metered by the millisecond. As a cloud-based system, Google's BigQuery solution:
Gives companies the option to choose between predictable up-front pricing models or pay-as-you-go pricing. Alternatively, with on-premise solutions like Hadoop, companies need to pay for more than they need to ensure that they can continue to perform well in any instance.
Offers independent scalability. AWS Redshift's on-demand pricing model needs to be based on the deployed virtualized instances throughout the enterprise, with fixed storage and compute requirements, which means that independent scaling is impossible.
Automatic upwards and downward scaling: Powering down the amount of data work you're doing on Redshift nodes will require the migration of data. Google BigQuery makes managing your needs much simpler.
Once a business has made the journey into the cloud and begun working with Google BigQuery for their analytics, strategic workloads, and applications, the IT organization will then have the opportunity to continue building and optimizing their data campaigns. With your data system located on the cloud, you'll have the freedom and flexibility to create a plan of action that modernizes your entire business structure.
For instance, you can decide exactly which services on Google you need to tap into to get the biggest benefits for your business. Google's BigQuery RECORD data type collocates detail and master information within the same table, so you can load nested data structures in a single BigQuery table, while still using SQL for greater compatibility with countless data technologies and tools. You'll also benefit from the extreme performance improvements associated with BigQuery's innovative columnar technology.
Additionally, for new data workloads that run at extreme volume, Google BigQuery users can also choose to capture their raw data in the Google Cloud Storage environment, then curate it withGoogle DataProc before they ever move it to BigQuery. This optimizes the flow of data within the Google framework and ensures that everything in your business data plan runs as smoothly as possible.
Administrative and Operational Savings with Google BigQuery
Finally, one of the reasons why Google BigQuery is such a cost-effective solution for the mid-market data strategy is that it's a fully-managed service. In other words, the backend configurations associated with your data management strategies are entirely handled by Google. This saves a great deal of money when it comes to hiring and maintaining data professionals to manage your clusters over time.
An on-premise deployment with something like Hadoop would require a full team of experts to manage the solution, including a software and hardware administrator, and multiple Hadoop operators. With an on-premise solution, analysts need to continually work with operators to process queries, rather than managing the questions themselves, and the on-premise deployment needs to be powered and cooled on-site, leading to an increased demand for floor space. On-premise data management solutions:
Require the support and services of consultants to ensure that the answer is fine-tuned to support the needs of the business. Often, this custom strategy goes beyond the skills of the standard administrator.
Must be maintained, including updates to security patches, firmware, the OS, and troubleshooting or resolving issues with software and hardware in the enterprise.
Require experts to be able to design and manage a data center environment for the various nodes and machines that must be kept running at all times to power the data strategy.
Even in an alternative cloud-based environment like AWS Redshift, nodes will often contain a fixed selection of resources. This means that growth and deployment are more complicated with these systems than with a flexible option like BigQuery's serverless solution.
BigQuery doesn't require the support of expert, dedicated administrators to launch a next-level data strategy. Many Google customers suggest that the process is simple enough that they can run queries by themselves by merely cutting and pasting their requirements into a self-service portal. On the other hand, it's often far more complex to run a data analytics and warehousing campaign on alternative on-premise and cloud-based solutions from other vendors. Google sets itself apart by making the possibilities and advantages of big data available to everyone - even the mid-market enterprise.
Ready to Discover Google BigQuery?
If you're ready to launch your big data strategy with BigQuery, now's the ideal time to get started. Discover what you can accomplish with a solution designed for simplicity and accessibility in any business environment. If you need help launching your Google BigQuery solution, reach out to the team at Coolhead Tech today. We can help to guide you through the best possible data systems for your business, ranging all the way from Google Cloud, to Google DataProc and BigQuery.
Start your data journey today.