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What this blog covers:

  • The main factors that contribute to slow queries include high data volume, intricate calculations, simultaneous user loads, etc.
  • Steps for implementing smart aggregates, simplifying complex calculations, embracing cloud-native solutions and selecting cloud-based query accelerators.
  • Benefits of cloud-based query acceleration to handle large workloads, offer cost-efficiency and adapt to changing demands.
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Modern businesses rely heavily on large datasets to gain insights and make informed decisions. They often require real-time or near-real-time insights for accurate and fast decision-making. However, as data volumes increase, traditional querying methods using relational databases or SQL-based queries usually struggle to provide timely results.

Slow query performance can negatively impact the speed of decision-making and ultimately affect the bottom lines. Query acceleration solutions can overcome these challenges, particularly those leveraging cloud-native technology. While dealing with billion-scale datasets, understanding how to leverage a query accelerator effectively can make all the difference. So, let’s get into the significance of query accelerators, explore the primary causes of slow query performance, and outline effective strategies for maximizing the business benefits of query acceleration.

Factors Contributing to Slow Queries

Slow queries can impede an organization’s ability to glean valuable insights from its data. There are multiple contributing factors to this sluggishness, which often compound each other.

Data Volume and Scale
Large datasets, ranging from terabytes to petabytes, pose a significant challenge in terms of query performance by slowing down their execution. This occurs because a larger dataset requires scanning and redistributing a greater number of rows within the cluster. Consequently, the query processing time is prolonged.
Organizations using high-volume datasets often encounter challenges when using traditional query methods, specifically those reliant on relational databases or SQL. The greater the size of the dataset, the more difficult it becomes to retrieve, process and analyze information.

Complex Calculations
When queries involve intricate operations or extensive historical data, traditional methods based on simple SQL queries often struggle to handle the computational load efficiently. Complex calculations can result in longer query execution times, causing delays in obtaining crucial insights. This becomes particularly troublesome when organizations require real-time or near-real-time data analysis for making timely decisions.

Concurrent User Loads
Multiple users requesting data simultaneously strain the system’s resources. Consequently, this can cause performance bottlenecks and lead to slower query response times. The impact of concurrent user loads becomes particularly pronounced during peak usage periods, hindering timely insights.

Lack of Parallel Processing
Traditional databases may not effectively utilize parallel processing capabilities, especially when faced with complex queries. Parallelism enables queries to be divided into smaller tasks that can be executed simultaneously, resulting in faster query tuning overall.

The Consequences of Delayed Queries

The delayed insights in data analysis can have profound consequences like missed opportunities, inaccurate decision-making and increased costs, which significantly hinder a company’s success. Let’s see how.

Missed Opportunities
When queries take too long to provide the necessary information, hidden opportunities may go unnoticed. For example, in the retail industry, identifying trends or customer preferences with a delay can result in lost opportunities to optimize product offerings, marketing strategies or inventory management. These missed chances can ultimately impact revenue generation and market competitiveness.

Inaccurate Decision-Making
Delays in obtaining insights may lead to inaccurate decision-making, especially when relying on outdated or incomplete data. This can result in decisions that fail to align with the current business situation. For instance, within the financial services industry, the timely availability of accurate market data is crucial for making informed investment choices. If this data is delayed or contains inaccuracies, it can lead to detrimental decisions, financial losses and damage to an organization’s reputation, eroding trust in its ability to navigate market dynamics effectively.

Increased Costs
Inefficient data processing prolongs the working hours of data analysts and IT teams responsible for optimizing query execution. Moreover, extended query times necessitate investments in more powerful hardware, increasing infrastructure expenses. Delays in obtaining insights can impact operational costs in supply chain management, leading to suboptimal restocking decisions and higher storage costs or stockouts.

Customer Dissatisfaction
When query performance is slow, it can result in customer dissatisfaction. This is particularly critical in industries like e-commerce that heavily rely on customer support, where real-time interactions are vital. Delays in accessing customer data can lead to frustrated customers, as they experience sluggish response times when making inquiries or lodging complaints. Such experiences have the potential to harm customer relationships, ultimately affecting loyalty and retention rates.

How To Leverage a Query Accelerator on the Cloud

Leveraging query accelerators on the cloud is essential for maximizing the value of data. The key strategies include:

Implement Smart Aggregates
Traditional methods involve constructing predefined multidimensional cubes based on historical usage data or assumptions about significant data combinations. However, these static cube structures can be resource-intensive and inflexible when it comes to adapting to evolving business needs.

Unlike these traditional OLAP approaches, Kyvos’ AI-based smart aggregation technology revolutionizes the process of creating smart aggregates. Before designing the data model, Kyvos gets a comprehensive understanding of the data and query patterns. This crucial advantage ensures that only the most relevant combinations are included in the data model. Instead of incorporating all possible combinations, which can result in an unnecessarily large model size, Kyvos optimizes the data model design. It specifically focuses on the combinations that business users frequently query, effectively preventing combinatorial explosion and minimizing redundant computation.

Furthermore, Kyvos provides an ML-powered smart recommendation engine. This powerful tool assists organizations in configuring essential aspects such as memory settings, partitioning strategies, materialization strategies and the overall physical view of the data model. It acts as a guide for organizations to construct highly optimized data models that efficiently store data in a granular form. As a result, it enables in-depth analysis across numerous dimensions and measures while maintaining interactive response times.

Simplify Complex Queries with Kyvos
Traditional query methods often struggle with intricate joins or extensive historical data when it comes to handling complex queries.

Kyvos adopts a unique approach by consolidating calculation logic within a unified semantic layer. Most aggregates are pre-calculated long before executing any queries. Let’s consider the scenario of analyzing years of data for comparisons. In Kyvos, the system computes aggregates for each year in advance. Consequently, during runtime, the sole task remains to compare these pre­computed results. As a result, query execution time is dramatically reduced, regardless of dataset size.

Embrace a Cloud-Native Solution
Kyvos seamlessly integrates with leading cloud platforms like Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). Its purpose-built architecture harnesses the scalability, flexibility and cost-efficiency of the cloud.

Kyvos stands out for its unique ability to dynamically allocate computing resources based on demand. This means that when query workloads spike, the platform automatically scales up to maximize query performance. Conversely, during periods of lower demand, it can scale down to minimize unnecessary costs. This elastic feature is essential for organizations looking to efficiently manage variable workloads.

Furthermore, Kyvos operates on a pay-as-you-go model that aligns perfectly with cloud environments. In this model, organizations are only charged for the computing resources they actually utilize, eliminating the need for significant upfront investments in hardware infrastructure. With Kyvos, businesses can optimize query performance and effectively manage costs.

Utilize Advanced Data Profiling
By analyzing data characteristics like cardinality, value distribution and usage patterns, businesses can make well-informed decisions about which data dimensions should be included in the acceleration process, ensuring improved query performance and reduced processing times.

Kyvos’ robust querying mechanisms guarantee speedy responses, processing both standard and ad hoc queries within fractions of a second. As a result, organizations can effortlessly explore their extensive cloud-scale datasets without experiencing any delays, regardless of the complexity or size of the queried data.

Ensure Compatibility with Existing Infrastructure
The chosen query accelerator should seamlessly connect and work alongside the organization’s existing databases, data warehouses and analytics tools. This compatibility ensures smooth operations without disruption to current data workflows and investments.

Choose solutions that offer connectors and APIs specifically designed for popular data platforms and BI tools. By ensuring compatibility, businesses can make the most of their current data investments while also improving query performance.

Scalability and Flexibility
As datasets continue to grow and the demand for real-time insights increases, it becomes crucial to ensure scalability. Selecting query accelerators that offer elastic scalability is important, as they effortlessly adapt to changing workload demands. This ensures that even with expanding data volumes, the accele­rator remains responsive and maintains smooth query performance.

With the power of cloud computing, Kyvos offers elastic scalability to easily adjust query processing resources as per changing business requirements. With auto-scaling, the platform efficiently handles the increased workload effortlessly.

Manage Security and Compliance
Ensuring data security and compliance with industry regulations is imperative. Organizations need to verify that the query accelerators offer robust features for data encryption and access control. This guarantees the protection of sensitive data while enabling the organization to meet regulatory requirements. Kyvos implements a robust three-tiered security architecture that includes row-level and column-level security measures to guarantee that enterprise data remains shielded from unauthorized access.

Conclusion

Leveraging query accelerators is not just an option; it has become a necessity for organizations to enhance the efficiency of processing and analyzing large datasets. For organizations struggling with vast datasets, the integration of cloud-based query accelerators into their data infrastructure holds the key to optimizing performance and maximizing the value of their data assets. These accelerators employ techniques such as smart aggregates, cloud-native solutions and advanced data profiling, empowering businesses to reduce response times and make quicker decisions.

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