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Latency has two components: data latency and query latency. Data latency is about making decisions on fresh data, while query latency is about accessing data significantly faster. Both of these factors improve with various technologies in the cloud.
On the data latency side, the ability to leverage various integration patterns and match purpose-built storage offerings to attack latency issues is a significant improvement of cloud from on-premises offerings. Scalable data integration options address data latency challenges as well. On the query latency front, efficient caching and tiering options in cloud data warehouses address latency challenges.
Scaling performance is the most measurable benefit of cloud data warehousing for enterprises worldwide. The difference in performance between cloud data warehouses and traditional infrastructure is vast. The main advantages of cloud lie in the various options available to optimize price-performance.
Data throughput is the amount of data that can be transferred from one location to another. In the context of databases and data processing systems, throughput refers to the amount of data that the system can read/write/process within a given timeframe. High data throughput allows more data to be processed in less time, leading to faster data retrieval and improved system performance. For example, high data throughput in a stock trading application is critical to enable faster processing of market data and timely execution of trades.
Concurrency refers to a system's ability to simultaneously execute multiple tasks or processes. In a database, this would refer to the ability to handle numerous queries or transactions "concurrently." A highly concurrent system can handle a significant number of simultaneous users or tasks, leading to improved performance and responsiveness. For example, in an e-commerce website, high concurrency allows the system to handle multiple simultaneous searches, orders, and other user interactions without slowing down or failing.
Data democratization and self-service are driving high concurrency requirements from data warehouses. Serving insights through a complex array of dashboards and to an ever-increasing user base requires high-concurrency access to data. Proper data modeling, complemented with the right infrastructure, is essential to address this. Cloud data warehouse features such as de-coupled compute & storage, auto-scaling can help address these challenges. However, delivering high concurrency has financial implications.
Adding more infrastructure to address high concurrency can result in higher spend measured in the form of cost per concurrent query. The right data model and a highly efficient data warehouse platform are thus necessary to optimize cost per concurrent query. Note that the ability to deliver concurrent queries varies drastically across cloud data warehousing technologies.
One of the most commonly acknowledged benefits of cloud data warehousing is enhanced elasticity. Elasticity is a must for current-day enterprises. Customer demands, operational and infrastructural requirements, and innovation plans can change rapidly. When you need to quickly scale aspects of your operations up or down without bleeding money, elastic cloud data warehousing is the perfect solution. Concepts of scale-up, scale-out, scale-in, or scale-to-zero are all possible with cloud computing.
Having isolated workloads, often referred to as granular workload organization, helps companies segment and cordon off some of their cloud-based activities. Workload isolation drives performance and fortifies infrastructure by ensuring that security breaches and misconfigurations are limited to a finely detailed level. This enables businesses to reap the benefits of cloud data warehouses and not be severely affected by any granular-level negative incident.
Given the nature of various components of analytics workloads, including ingestion, transformation, and analytics, the ability to shape infrastructure to suit a workload's needs without dealing with “noisy neighbor” issues is a key benefit of cloud data warehouses. Simply put, different workloads can share data without stepping on each other. This can be achieved through workload scheduling, query optimization, and resource allocation.
Another key advantage of workload isolation is having the optimal workload-specific governance, policies, and configuration for each isolated segment. This allows for implementing workload-specific security measures as well as granular cost management for each segment.
Cloud data warehouses function as managed service or SaaS offerings. With this approach, a significant portion of infrastructure management, security, upgrades, patching, scaling, monitoring and reporting is offloaded to the cloud service provider or data warehouse service provider. For example, the intricate details of standing up infrastructure, networking and securing this infrastructure falls on the service provider. The extent to which is implemented varies with the offering. This reduced operational burden allows analytics engineers to focus on value added data transformation and insight generation tasks.
A consumption model calculates a price based on usage rather than other factors like the number of users or the length of a pre-agreed contract. Consumption pricing models can provide opportunities to access the cost savings advantages of traditional and cloud data warehousing. In the past, businesses have leaked money by paying for services and infrastructure they don’t always use. The elasticity of cloud data warehousing solves that problem.
Traditional models have been a detriment to organizations. Huge upfront and ambient expenses for infrastructure, IT experts, maintenance, and security have drained large portions of their budget. Newer pay-as-you-go models have been a major relief to these companies and resulted in higher-yielding investments.
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