How to Boost Query Speeds for Large Datasets

Technology has created a world of instant gratification as the base expectation. Imagine waiting more than a few seconds for a click to register, a webpage to load, or a video to begin. The horror!

But those near instantaneous response times rarely occur in enterprise software. It’s as if your workers should forget speedy and seamless technology experiences as soon as they log on to your enterprise systems. 

Behind the scenes, however, it’s a complex interchange of networks, systems, and ever-growing data volumes that lead to lackluster performance. And, while cloud-based software and systems have effectively addressed some of the speed issues, data volumes continue to grow and expand, which slows data query and analytics efforts whether the data is on-premises or in the cloud. 

Benefits of boosting query speeds for large data sets

Finding answers quickly is a competitive advantage that can translate directly into revenues, profits, and overall business success. As a busy data analyst, the ability to ask questions and find answers at the speed of thought empowers you to drive business results more efficiently. It can even allow for conversational analysis – and more sophisticated business strategies.

Here are some other key benefits of boosting query speeds.

Enhanced Decision-Making

Faster query speeds enable organizations to access and analyze data in real-time, leading to quicker and more informed decision-making. When queries that once took hours can be executed in minutes or even seconds, business leaders and analysts can rapidly respond to emerging trends, market changes, and operational challenges. This agility is particularly critical in fast-paced industries such as finance, healthcare, and retail, where timely decisions can significantly impact outcomes.

Improved Customer Experience

In sectors like e-commerce and customer service, the ability to retrieve and process customer data swiftly can vastly improve the customer experience. Faster query speeds mean that personalized recommendations, support responses, and transaction processing occur seamlessly, enhancing customer satisfaction and loyalty. When organizations can anticipate and meet customer needs promptly, they build stronger relationships and foster greater customer retention.

Increased Operational Efficiency

Operational efficiency is a cornerstone of successful business management. Boosting query speeds reduces the time employees spend waiting for data retrieval, allowing them to focus on more strategic tasks. This increased efficiency leads to higher productivity across the organization. For example, data scientists and analysts can run complex models and simulations more frequently, refining their analyses and delivering more accurate insights without being bogged down by slow data processing times.

This ability to run complex models more frequently is table stakes for impactful AI projects. Such projects almost always demand huge datasets and complex computations. Increasingly, organizations are leveraging cloud solutions like Snowflake with a data catalog to grow or shrink compute power on demand, which supports optimal performance for data processing, inference, and AI  model training.

Scalability and Flexibility

As organizations grow, their data needs expand, often exponentially. Enhanced query speeds facilitate the handling of larger datasets without compromising performance. This scalability ensures that businesses can continue to leverage their data assets effectively as they expand their operations and customer base. Furthermore, faster query speeds provide the flexibility to explore new data sources and integrate diverse datasets, supporting innovative projects and initiatives.

Cost Savings

Time is money, and faster query speeds translate directly into cost savings. By reducing the time required for data processing, organizations can lower their operational costs. Additionally, efficient data retrieval can minimize the need for extensive hardware investments and reduce the burden on IT infrastructure. Over time, these savings can be substantial, freeing up resources to be invested in other critical areas of the business.

Enhanced Competitive Advantage

In an increasingly competitive landscape, the ability to leverage data swiftly and effectively can be a significant differentiator. Organizations that can extract actionable insights faster are better positioned to innovate, optimize their strategies, and outperform competitors. Whether it's launching new products, entering new markets, or fine-tuning marketing campaigns, the ability to rapidly access and analyze data provides a decisive edge.

Most companies across most industries are inundated with data, but that data contains incredible business value. Insurance and financial services firms comb data to improve fraud detection, risk assessments, credit rankings, and more. Healthcare organizations use vast amounts of patient and pharmaceutical data for research, to develop new medicines, and to improve patient outcomes. Retailers analyze marketing, web, and transaction data to better predict future purchases and offer compelling ads.

So, the pressure is on to boost query speeds for large data sets. Unfortunately for data engineers, that pressure falls squarely on their shoulders.

How can data engineering teams improve data query performance?

Data engineering teams design and build systems for handling large-scale data, enable real-time insights by querying large datasets for valuable strategic information, and ensure stakeholders across an enterprise have easy and informed access to reliable and secure datasets to enable business decisions.

As data volumes and the business value locked within data continue to explode, the need to boost query speed for large datasets is crucial. Here are several strategies data engineering teams can employ to improve query performance:

Using data partitioning to increase data query speed

Data partitioning divides a large dataset into smaller, more manageable sets called partitions. This helps improve scalability, fault tolerance, and other concerns, but also helps improve query speeds. 

Partitioning your data based on certain criteria, such as date ranges or geographical regions, can significantly improve query performance. This allows the query engine to narrow down the search space, reducing the amount of data that needs to be scanned. 

Imagine sifting through your old photos. If they were sorted into boxes based on the year they were taken, it would be faster and easier to find your 2015 summer vacation photos than if all photos from the past 20 years were thrown into just a single box.

Data engineers can use partitioning techniques supported by your data storage system (e.g., Hive partitioning in Hadoop, partitioned tables in databases) to optimize data organization.

Boosting query performance on large data volumes with data indexing

Data indexing uses a data structure to create a sorted list of key information that points to corresponding data rows, similar to how an index in a book helps you find information quickly. To boost query speeds, the database can use the index to efficiently locate the required data. 

Creating indexes on columns frequently used in queries, especially those involved in filtering or joining operations, allows the query engine to quickly locate relevant rows, thereby speeding up query execution. However, indexes may incur overhead during data inserts and updates, so it’s important for data engineers to carefully consider which columns to index based on query patterns and performance requirements.

Using data compression to improve query speeds

Data compression uses algorithms to reduce the size of digital files that hold data, thereby increasing transfer and access speeds. One compression approach is to identify and eliminate redundant data by storing a single value and referencing that value in all other instances.

For example, say an equipment manufacturer’s database contains product and component images for hundreds of thousands of finished products. If many parts and products were assembled with a popular M8 bolt, the database would save storage space and data querying time by not having to process thousands of high-quality images during every query. 

Compressing data can reduce storage space and improve query performance by reducing the overhead required to input, process, transfer, and output data.

Data engineers must be mindful when choosing an appropriate compression algorithm based on the data characteristics and query patterns. Modern data storage systems often support various compression techniques (e.g., Snappy, Gzip, LZ4), so experiment with different options to find the most suitable compression method for your data.

Accelerating query performance through query optimization

Queries are the search strings, or commands databases use to locate corresponding data. Just as you might need to reword your search in Google or Bing search engines to find the answers you seek, optimizing database queries can help you find the right information in less time. 

Query optimization efforts typically begin with analyzing query performance and identifying bottlenecks. Query execution plan tools can then be used to identify inefficient operations, such as full table scans or unnecessary joins, and optimize query syntax and structure accordingly. AI can be extremely helpful in this area.

Data engineers should consider rewriting complex queries, breaking them down into smaller, more efficient steps, or leveraging query hints and directives to guide the query optimizer.

Using distributed processing for faster queries of large datasets

Teamwork makes the dream work, as they say. Distributed processing brings together multiple servers, systems, and computing resources to share the burden and speed up data processing. By dividing complex queries into smaller tasks, different resources can tackle these smaller tasks individually and simultaneously. 

When dealing with extremely large datasets, consider distributed processing frameworks like Apache Spark or Apache Flink. These frameworks can parallelize query execution across multiple nodes, enabling faster processing of large volumes of data.

Data engineers can also distribute data and workload evenly across nodes to maximize parallelism and avoid resource bottlenecks.

Optimizing query performance across massive data volumes with caching

Caching stores frequently accessed data in a temporary location to improve performance and efficiency when processing. This may be faster but more expensive storage technologies that will be used only temporarily specifically to speed query performance.

Data engineers can implement caching mechanisms to store frequently accessed query results or intermediate data. Caching can reduce query latency for repetitive queries and alleviate the load on underlying data storage systems.

In-memory caching solutions like Redis or Memcached can also be used for low-latency access to frequently accessed data.

Using hardware optimization to speed query performance

There’s no replacement for displacement, at least when it comes to performance muscle cars. Hardware optimization takes that same view by focusing on the robustness, speed, capabilities, and tuning of the equipment used to facilitate data storage and analysis. This can range from ensuring timely firmware updates to hardware acceleration to efficient resource utilization to preventative maintenance.

Data engineers can invest in hardware upgrades or optimizations to improve query performance. This may include upgrading CPUs, increasing memory capacity, or utilizing faster storage solutions (e.g., SSDs or NVMe drives) to reduce latency.

Consider leveraging cloud-based infrastructure, which offers elastic scalable compute and storage resources that can be dynamically provisioned based on workload demands.

Speeding data queries with data denormalization and materialized views

Normalization of data typically splits data into separate tables to decrease redundancy, but that can slow query speeds. Data denormalization brings back the redundancy specifically to increase query speeds. A materialized view is a stored query output, which saves time by not having to repeat an oft-used query. Since query results are typically temporary, a materialized view saves the results.

Getting more technical, denormalized data structures or created materialized views are both techniques to precompute and store aggregated or frequently queried data. This can eliminate the need for expensive join operations or complex calculations during query execution.

Data engineers can update materialized views incrementally to reflect changes in underlying data, either through periodic refreshes or real-time streaming updates.

Faster data-driven decisions from faster query speeds

Business runs on data. Being data-driven and building a robust data culture are strategic and competitive prerequisites to success. But, if your teams can’t quickly access the data they need to execute, your push for success may be too late.

By implementing these strategies, you can significantly improve query performance for large datasets in your company's data infrastructure, enabling faster data access and analysis for various analytical and operational use cases.

Curious to learn how Alation works “beneath the hood?” Explore this datasheet to learn how Query Log Ingestion (QLI) powers the catalog.