How to Implement High-Performance Data Querying

Data infrastructure forms the backbone for AI and advanced analytics scenarios — and it’s been growing at a massive clip. According to AvePoint, 64% of enterprises manage more than one petabyte of data, and 41% deal with 500+ petabytes. 

This volume of historical records and newer streaming pipelines adds constant ingestion pressure. Queries that once took seconds now take ages. So business teams are stuck waiting for fresh insights, while data scientists running model training pipelines compete for the same compute resources as business intelligence workloads.

Query performance sits directly inside that problem space. When it’s slow, your company struggles to obtain real-time business intelligence and support advanced AI models that depend on fast access to large datasets. But it shouldn’t be the case, no matter how large or legacy-ridden your data infrastructure is today.  

In this guide, our data science team explains why and how to best approach high-performance data querying in modern data platforms to reduce latency, improve infrastructure costs, and bring more real-time data into customer-facing and back-office applications. 

Why Optimized Data Querying Should Be a Business Priority for 2026 

Query performance determines how reliably organizations can operationalize their data. When analytical queries slow down, the delay propagates across all the connected applications, and operational consequences appear quickly.

For instance, a predictive inventory management system may generate availability forecasts using stale records. Finance teams may wait longer for reports during closing periods when accurate numbers matter most. And customer-facing teams may only make decisions based on partial data. 

What’s more, over half of US brands report having large reserves of “dark data” — information that is stored but rarely analyzed or operationalized. In other words, they absorb storage and infrastructure costs without generating proportional analytical value.

Efficient querying improves how data systems respond to growing workloads and helps companies achieve several operational advantages:

• Real-time operational dashboards and monitoring systems. Live reporting becomes feasible when analytical queries complete fast enough to support frequent refresh cycles.

• Better performance for customer-facing products. Applications such as recommendation engines, financial analytics tools, and personalization services can retrieve the necessary data without introducing noticeable latency.

• Lower infrastructure costs. Efficient queries scan fewer rows and access fewer columns, which reduces compute consumption and limits unnecessary instance scaling in cloud environments.

• Greater reliability during peak demand periods. Analytical systems remain responsive during events such as financial reporting cycles, major product launches, or seasonal spikes in platform usage.

• More stable machine learning workflows. Model training pipelines, feature stores, and monitoring systems can retrieve large datasets efficiently when query latency remains predictable.

• Higher user trust in enterprise data systems. Teams rely more confidently on dashboards and reports when data loads quickly and behaves consistently across tools.

And the above advantages of high-performance data querying drive measurable business impacts.  

Our team helped a real estate platform create a data infrastructure capable of processing over 250 million record updates with a total size of 2.5 TB each month while maintaining sub-five-second query execution for the most complex analytical requests. The platform now supports near-real-time lead prediction models and market analysis built on the same data environment.

Similarly, AT&T reduced infrastructure costs by 84% while enabling most business users to retrieve self-service reports in under one second after consolidating several analytics systems into Snowflake’s Data Cloud. 

For organizations expanding their analytics and AI use cases, query optimization becomes part of the core data architecture. Without it, infrastructure costs tend to rise faster than the analytical value, while operational workloads begin to compete for the same platform resources.

When Implement Optimized Data Querying 

Allocating budgets toward optimized data querying makes the most sense when:

• Query latency begins affecting decision cycles. Reports take longer to generate, competitive intelligence arrives late, and confidence in data reliability declines.
  
• Data volumes outgrow the original system architecture. Tables expand into the millions or billions of rows, streaming pipelines increase ingestion pressure, and infrastructure costs begin rising faster than analytical ROI.
  
• Concurrency increases across the organization. More teams and tools query the same datasets simultaneously, leading to queue backlogs and frequent resource contention.
  
• Customer-facing data products enter production. Systems such as gen AI assistants, live inventory platforms, recommendation or personalization engines require low-latency data retrieval consistently.
  
• AI and advanced analytics workloads scale. Feature stores, training pipelines, and experimentation environments repeatedly access large historical datasets, increasing demand on the underlying data platform.

6 Steps to Implement High-Performance Data Querying

Before we jump in, a couple of important clarifications. Query optimization does not follow a single universal playbook. The mechanisms that improve performance depend heavily on the type of data platform involved, from transactional databases to analytical warehouses and indexing systems. 

In this guide, we’re focusing on query optimization in modern data lakehouse environments such as Amazon Redshift, alongside search layers like Elasticsearch.

1. Evaluate Current Query Patterns and Latency Pain Points

Many data platforms are built around assumed workloads. But in practice, real usage patterns tend to look very different. A handful of queries often generate most of the system load. Some tables receive constant lookups from dashboards and APIs, while others are rarely touched. 

Understanding these patterns is the starting point for meaningful performance improvements. We recommend analyzing

• Query logs show which queries run most often, consume the most compute, or generate the largest data scans.

• Execution plans reveal how the query engine processes each request, exposing expensive joins and other sources of latency.

• Warehouse telemetry shows how infrastructure resources are used to detect queue delays, compute saturation, and other system-level bottlenecks.

This analysis reveals where optimization efforts deliver the greatest return and helps prioritize accordingly.  In most environments, addressing a small number of high-cost queries can significantly improve overall platform performance.

Several questions that typically guide this diagnostic phase at Edvantis: 

• Which queries generate the highest scan volumes? Large scans often indicate missing filters, inefficient joins, or poorly structured data layouts.
  
• Which datasets receive the most frequent access? High-traffic tables supporting dashboards, APIs, or customer-facing products may require different optimization strategies than archival datasets.
  
• Where do queues or resource bottlenecks appear? Query wait times often signal concurrency limits or uneven resource allocation within the warehouse.
  
• Which workloads run most frequently? Scheduled reports, automated pipelines, and monitoring systems can repeatedly execute the same queries throughout the day.

Answering these questions clarifies where the most-used data is hosted and whether it requires data migration to architectures better suited for its access patterns. The answers also help our teams suggest better data layouts and serving layers for high-demand workloads. 

2. Decide on the Optimal Data Layout

When you know where the most-accessed data sits and how it’s currently stored, you can see the most common access paths and ways to optimize them to minimize scan volume. 

Modern analytical warehouses rely on distributed execution models. This means that data is split across nodes so that multiple processors can work in parallel. For instance, Amazon Redshift uses a massively parallel processing (MPP) architecture, so rows are distributed across node slices in the cluster. Query performance, therefore, depends on whether related data resides on the same node or has to be transferred across the network during execution.

To optimize Redshift data layout and its querying, several technical best practices are thus essential. 

1. Choose the appropriate distribution style

Distribution style determines how rows are placed across nodes.

• KEY distribution works well for large tables frequently joined on the same column because rows sharing the same key are stored on the same node slice.
  
• ALL distribution replicates small dimension tables across every node, allowing joins to occur locally without redistributing the table.
  
• EVEN or AUTO distribution spreads rows evenly when no dominant join key exists. AUTO allows Redshift to adjust distribution strategies based on observed workloads.
  

2. Align distribution keys with join paths

Fact tables and their most frequently joined dimension tables should share the same distribution key. When related rows reside on the same node slice, joins can be executed locally instead of triggering expensive network redistribution. This significantly reduces query latency in large analytical workloads.

3. Minimize the volume of scanned data

Queries should retrieve only the columns and partitions required for the analysis. Selecting unnecessary fields or applying filters late in the query plan increases I/O and memory usage across the cluster. Filtering early allows Redshift to skip irrelevant data blocks and limits the amount of information that must pass through later stages of the execution plan.

4. Leverage Automatic Table Optimization (ATO)

Redshift’s Automatic Table Optimization feature allows the platform to adjust distribution and sort strategies based on real workload behavior. Using DISTSTYLE AUTO and SORTKEY AUTO allows auto-refining table layouts as query patterns evolve. Automation helps maintain performance as datasets grow and analytical workloads change.

3. Separate Analytical Workloads From Real-Time Search Workloads

In some cases, one data platform may be used to serve both analytical queries and real-time data streaming to applications. Over time, this often introduces competing workload requirements and latency spikes. 

This happens because most data warehouses are optimized for large analytical scans and scheduled transformations. But interactive search workloads behave differently. They require fast point lookups, predictable response times, and stable performance under high concurrency. These two access patterns rarely coexist efficiently, so, in many cases, it may be better to separate analytical processing from real-time data retrieval.

Introducing a dedicated search layer with Elasticsearch serves interactive queries, for example, running on top of a data warehouse allows each system to operate within its strengths.

In practice, this architecture can significantly improve response times for dashboards, APIs, and customer-facing applications. For instance, when we replicated selected datasets into an Elasticsearch cluster, our client’s query rate improved to under 3 sec in 99% of cases.

Technical best practices for optimizing Elasticsearch deployments

Once a search layer is introduced, cluster configuration becomes a critical performance factor.  Several design choices typically determine how well Elasticsearch performs under production workloads: 

• Design a shard strategy early. Hard shard sizes and document limits prevent query latency from oversized shards or cluster overhead from excessive shard counts. A good practice is 10-50 GB shards and fewer than ~200 million documents per shard.

• Plan primary and replica shards carefully. Primary shards determine how data scales across the cluster. Replica shards add fault tolerance and increase read throughput because they can also serve search queries.

• Optimize index mappings. Define field mappings explicitly instead of relying on dynamic mapping. Use appropriate field types (keyword, date, numeric) and disable indexing for fields that are never queried.

• Leverage caching mechanisms. Enable query and result caching for frequently executed filters and aggregations. Caching improves performance for dashboards and repeated API queries.

• Separate indexing and search workloads. High ingestion rates can degrade search performance if both share the same resources. Use dedicated ingest nodes or separate indexing pipelines when throughput is high.

Lastly, monitor cluster health continuously to avoid performance issues. We usually set a dashboard to track shard allocation, query latency, heap usage, and indexing throughput to identify emerging bottlenecks and adjust cluster configuration as workloads evolve.

4. Precompute Frequently Used Analytical Results

Even highly scalable warehouses incur significant cost when the same complex query runs repeatedly. Aggregations across large datasets, rolling metrics, and multi-table joins can be expensive to compute on demand. For example, daily revenue summaries, inventory counts, or customer segmentation analysis often rely on identical query logic executed many times throughout the day. 

Precomputation helps streamline these workloads. Instead of calculating results during each query, frequently used aggregations can be calculated in advance and stored in dedicated tables or views. So analytical queries then read from these precomputed datasets instead of scanning the underlying raw tables.

Several effective precompute techniques include: 

• Materialized views store the result of a query physically in the warehouse and refresh it periodically. Dashboards and analytical tools can query the precomputed results directly.

• Aggregation tables precompute commonly used metrics such as daily or hourly summaries. Queries then operate on these smaller aggregated datasets instead of scanning the full event history.

• Incremental refresh pipelines update precomputed tables only for newly arriving data rather than recomputing the entire dataset. This approach reduces compute consumption and keeps results close to real time.

Precomputation doesn’t fully eliminate raw data queries, but it reduces the number of times heavy calculations must be redone. This significantly improves query latency while lowering warehouse compute costs.

For example, German retailer Home24 implemented Amazon Redshift materialized views for unifying analytics across its data estate, and this feature reduced dashboard load times from 8 minutes to just 500 ms. 

5. Optimize Query Design for Distributed Execution

As mentioned previously, modern data warehouses use massively parallel processing. Thus, query performance heavily depends on how efficiently the query plan distributes work across the cluster.

Large joins, repeated sorting operations, or poorly structured aggregations can force the system to redistribute data between nodes before computation begins. So network transfer then becomes a dominant part of the execution time. 

In order to prevent that, try to reduce the volume of data processed at each stage of the query plan while allowing the engine to parallelize the work.

Here are several best practices to maximize query performance on Amazon Redshift: 

• Select only required columns. Avoid SELECT * in analytical queries. Retrieving unnecessary columns increases scan volume and memory usage across the cluster.

• Reduce dataset size before joins. Pre-aggregate or filter large tables before joining them to other datasets. Smaller intermediate results reduce network transfer between nodes.

• Use approximate aggregations when exact precision isn’t critical. Functions such as approximate distinct counts can provide statistically accurate estimates while avoiding expensive full dataset scans.

With these changes to query design, distributed analytical systems become more efficient at executing workloads.

6. Implement Continuous Query Performance Monitoring 

Even with all of the above optimizations in place, query performance will be variable in production, reflecting further data growth in your organization. Continuous monitoring is thus essential to detect latency issues and performance degradation before it affects end-users. 

To troubleshoot issues early on, we recommend configuring the following monitoring views in Redshift:

• System tables and monitoring views. System views such as STL, SVL, SVV, and SYS expose execution metadata and resource usage across the cluster, such as query runtime, execution across nodes, CPU usage, disk I/O, and more. So you know when and where issues start piling up. 

• Workload Management (WLM) metrics. WLM queues monitoring shows how queries compete for cluster resources. Important signals include queue wait times, query execution duration, concurrency levels within queues, and memory allocation per query.
  
• Infrastructure telemetry from Amazon CloudWatch. Cluster-level and node-level metrics such as CPU utilization, disk I/O activity, and maintenance status help distinguish inefficient queries from underlying resource constraints.
  

Together, these signals help you identify emerging bottlenecks early on and tune controls as usage patterns evolve.

Conclusion

The real competitive advantage today isn’t how much data you can collect and store, but how fast you can operationalize it. Data-driven organizations have 70% higher revenue per employee and 22% higher overall profitability. They continuously evaluate the state of their data infrastructure, improve data layouts, and aim to separate analytical and real-time workloads. 

If your data architecture is struggling to keep pace with analytics workloads and new AI app demands, query performance optimization is an essential next step. Get in touch with Edvantis data science team to discuss how you could run a more reliable and scalable analytics infrastructure.