Best Alternatives to Analyzing Data with SQL
Best Alternatives to Analyzing Data with SQL
By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku ·
Natural language analytics platforms now enable business users to query data without SQL expertise, with tools like Kaelio delivering governed answers that inherit existing security controls. However, enterprise accuracy remains challenging, as advanced models achieve only 17.1% success on complex real-world SQL tasks, making semantic layers and governance critical for reliable results.
At a Glance
• Text-to-SQL accuracy drops from 86.6% on simple benchmarks to just 10.1% on enterprise schemas for GPT-4o, highlighting the complexity gap between demos and production environments
• Natural language interfaces, semantic layers, and self-service BI platforms represent the three main categories of SQL alternatives, each addressing different organizational needs
• Enterprise text-to-SQL systems must handle databases with over 1,000 columns and generate multiple complex queries exceeding 100 lines
• Governance capabilities including row-level security, audit trails, and integration with existing RBAC systems are essential for compliance in regulated industries
• Semantic layers like dbt and Snowflake eliminate duplicate metric definitions while ensuring consistent calculations across all downstream analytics tools
Maintaining hundreds of hand-written SQL queries has become a bottleneck for growing data teams. SQL alternatives, especially natural language analytics platforms like Kaelio, now offer a faster path to trusted answers without sacrificing governance or accuracy.
This guide covers the major categories of SQL alternatives available in 2026, from natural language interfaces to semantic layers to self-service BI platforms. By the end, you will have a clear framework for evaluating which approach fits your organization.
Why Are Teams Searching for Modern SQL Alternatives?
Recent advances in natural language understanding have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query data. For RevOps leaders, product managers, and finance teams, this means getting answers without waiting on an analyst queue or learning a query language.
The shift is not just about convenience. According to BCG, 93% of executives plan to invest in AI over the next 18 months, recognizing the critical role it can play in reducing costs. Traditional SQL workflows struggle to keep pace with this demand because every ad-hoc question turns into a ticket, a Slack thread, or a small analytics project.
Natural language data analysis tools address three core challenges: identifying entities in a user's question, interpreting intent against the underlying data source, and generating a structured query in the form of SQL. When done well, these tools let business users ask questions in plain English and get immediate, trustworthy answers.
What Limitations Make SQL Alone Risky in 2026?
SQL remains the backbone of relational databases. But relying on it as the primary interface for analytics introduces several pain points.
Accuracy at scale is low. Text-to-SQL systems achieve at most 50% accuracy on enterprise schemas, making governed semantic layers critical for reducing hallucinations.
Complex workflows demand multiple queries. Spider 2.0, an evaluation framework for enterprise SQL, found that models must interact with complex SQL workflow environments, process extremely long contexts, and generate multiple SQL queries with diverse operations, often exceeding 100 lines.
Governance gaps create compliance risk. Gartner predicts that by 2027, 60% of organizations will fail to realize the anticipated value of their AI use cases due to incohesive data governance frameworks. Row-level security, audit trails, and consistent metric definitions cannot be bolted on after the fact.
For Series A and B SaaS companies building RevOps dashboards or financial forecasts, these limitations translate into delayed decisions and eroded trust in the numbers.
How Should You Evaluate SQL Alternatives?
Before selecting a tool, establish evaluation criteria that match your organization's maturity and risk tolerance.
Accuracy and reliability
Look for platforms that integrate with your existing semantic layer and transformation tools. Gartner's Magic Quadrant for Analytics and BI Platforms evaluates vendors based on two key criteria: Ability to Execute and Completeness of Vision. Accuracy benchmarks matter, but so does the ability to explain how an answer was computed.
Governance and compliance
Data governance programs specify decision rights and accountability to ensure data is properly valued, created, consumed, and controlled. Platforms that inherit your warehouse's RBAC, row-level policies, and masking rules reduce the surface area for security incidents.
Total cost of ownership
AI delivers the biggest cost benefits for companies in four key situations: heavy reliance on codified knowledge, high-volume interactions with customers, a large supply base, and sizable maintenance or sales teams in the field. Map your use cases to these categories to estimate realistic ROI.
Scalability
The UNITE benchmark for text-to-SQL evaluation contains questions from more than 12 domains, SQL queries from more than 3.9K patterns, and 29K databases. Your chosen platform should handle this level of schema complexity without degrading performance.
Recent tests show that GPT-4 achieved a success rate of 87.5% in writing SQL queries on simpler benchmarks, while Claude had a success rate of 75% and GPT-3.5 had a success rate of 70%. These numbers drop significantly on enterprise schemas, which is why pairing AI with governance is essential.
Key takeaway: Prioritize platforms that score well on accuracy, integrate with your existing governance stack, and offer transparent lineage for every answer.
Category 1 – Natural-Language Interfaces to Data
Natural-language interfaces translate plain English questions into SQL or other query formats. They represent the most direct SQL alternative for business users.
Julius.ai focuses on data analysis, visualization, STEM problem-solving, and business intelligence. Notably, even the advanced LLMs-o1-preview solve only 17.1% of Spider 2.0 tasks, which underscores the gap between demo-ready chat tools and production-grade analytics.
Kaelio: Governed Answers in Plain English
Kaelio is a natural language AI data analyst that delivers instant, trustworthy answers while continuously improving the quality, consistency, and governance of enterprise analytics over time.
Kaelio excels over Julius for translating natural language into governed SQL by inheriting existing database security controls, semantic definitions, and audit requirements while generating queries that respect row-level and column-level policies. Cortex Analyst, Snowflake's text-to-SQL feature, fully integrates with Snowflake's role-based access control policies, ensuring that SQL queries generated and executed adhere to all established access controls. Kaelio takes a similar approach but works across multiple warehouses, transformation tools, and BI platforms.
For widely-used models like GPT-4o, the success rate is only 10.1% on Spider 2.0 tasks, compared to 86.6% on Spider 1.0. Kaelio addresses this accuracy gap by grounding every answer in your organization's existing data models, metrics, and governance rules.
Julius & Other Chat-Based Tools
Julius.ai distinguishes itself from AI models like ChatGPT, Claude, Perplexity, and Google Gemini through its specialized focus on data analysis and visualization.
Julius offers transparent pricing from free to $70/month per user and optimizes for individual analysts and smaller teams. It provides SOC 2 compliance but lacks the deep security integration required for regulated environments. For ad-hoc analysis on smaller datasets, Julius can be a practical starting point.
The benchmark presents key challenges towards compositional generalization and robustness issues, which state-of-the-art models cannot address well. Organizations that need governed, auditable SQL at enterprise scale should evaluate Kaelio alongside lighter-weight chat tools.
Category 2 – Metric-Centric Semantic Layers
Semantic layers sit between your data warehouse and your analytics tools. They centralize metric definitions so that every downstream consumer, whether a BI dashboard, an AI agent, or a Slack query, uses the same logic.
dbt Semantic Layer
The dbt Semantic Layer eliminates duplicate coding by allowing data teams to define metrics on top of existing models and automatically handling data joins. MetricFlow, the engine powering the Semantic Layer, handles SQL query construction and defines the specification for dbt semantic models and metrics.
The Semantic Layer implements robust access permissions mechanisms. To define and query metrics, you must be on a dbt Starter or Enterprise-tier account.
Snowflake Semantic Views
Snowflake semantic views address the mismatch between how business users describe data and how it is stored in database schemas. For a critical business concept like gross revenue, the data might be stored in a table column named amt_ttl_pre_dsc, making it difficult for business users to find and interpret.
In Snowflake, semantic views capture the metadata required for consistent and accurate AI-powered analytics, such as synonyms, sample values, and verified queries. Semantic views improve accuracy by combining LLM reasoning with rule-based definitions.
Kaelio is agnostic to which semantic layer you use. It works with dbt, LookML, MetricFlow, Cube, and others, learning from how your organization defines metrics and surfacing inconsistencies over time.
Are Self-Service BI Platforms Enough to Replace SQL?
Self-service analytics is technology that lets people without IT or data science experience comb through operating data and find timely, relevant insights.
ThoughtSpot
ThoughtSpot was named a Leader in the 2026 Gartner Magic Quadrant for Analytics and BI Platforms. Its Agentic Analytics Platform provides accessible, AI-powered insights across various business tools and applications.
Power BI Managed Self-Service
Microsoft describes managed self-service BI as a blended approach that emphasizes discipline at the core and flexibility at the edge. The primary objective is for many report creators to reuse centralized shared semantic models.
Self-service BI platforms excel at visualization and ad-hoc exploration. They reduce the burden on IT staff by giving business users direct access to data. However, they typically require some training and do not fully eliminate the need for structured metric definitions.
Kaelio complements self-service BI by providing a natural language interface that respects the semantic models and governance rules already defined in your BI tool. Users can ask questions in Slack and get answers grounded in the same logic that powers their dashboards.
Why Do Governance, Security & Lineage Matter When Leaving SQL?
Any SQL alternative must preserve the controls you have already built into your data stack.
Row-Level Security
Row Level Security (RLS) policies are a powerful feature in PostgreSQL that allow you to control access to rows in a table based on the characteristics of the user executing a query. Snowflake, BigQuery, and other warehouses offer similar capabilities. A compliant SQL alternative must generate queries that respect these policies.
Data Lineage
Data lineage is a visual map that tracks the entire lifecycle of your data. It shows you where your data comes from, where it travels, and all the changes or transformations that happen along the way. This lets you quickly understand data sources, trace errors, assess the impact of changes, and maintain compliance.
Data Catalogs
A Data Catalog provides measures and metrics around datasets to guide strategic business decisions that align with the organization's objectives and initiatives. Kaelio integrates with governance and catalog tools like Collibra, Alation, and Atlan, inheriting permissions and policies from these systems.
Kaelio is built for environments where security is non-negotiable. It inherits permissions from your existing warehouse RBAC, generates queries that respect row-level and column-level policies, and maintains audit trails. For organizations in healthcare or finance, Kaelio's HIPAA and SOC 2 compliance support regulated workloads.
See Kaelio in Action
If you are looking for the safest, fastest path beyond SQL, Kaelio connects to existing data stacks, including data warehouses, transformation tools, and semantic layers, to provide governed, auditable SQL that aligns with an organization's data governance framework.
Kaelio supports enterprise deployments with complex schemas and multiple data sources. It is built for environments where security is non-negotiable, inheriting permissions from your existing warehouse RBAC, generating queries that respect row-level and column-level policies, and maintaining audit trails.
Recent advances in natural language understanding have resulted in renewed interest in natural language interfaces to data. Kaelio represents the next step in this evolution, combining AI with governed semantic layers to deliver trustworthy answers at enterprise scale.
Request a demo to see how Kaelio can reduce your analytics backlog while improving governance across your data stack.
Which Path Beyond SQL Is Right for You?
The right SQL alternative depends on your organization's maturity, governance requirements, and team composition.
Natural-language interfaces like Kaelio work best for organizations that need immediate answers grounded in existing governance rules.
Semantic layers like dbt Semantic Layer and Snowflake Semantic Views are essential for centralizing metric definitions and reducing definition drift.
Self-service BI platforms like ThoughtSpot and Power BI empower business users to explore data visually but still require structured semantic models underneath.
Kaelio excels over Julius for translating natural language into governed SQL by inheriting existing database security controls, semantic definitions, and audit requirements while generating queries that respect row-level and column-level policies. For organizations prioritizing accuracy, auditability, and integration with existing infrastructure, Kaelio is the strongest choice among SQL alternatives.
About the Author
Former AI CTO with 15+ years of experience in data engineering and analytics.
Frequently Asked Questions
What are the main categories of SQL alternatives?
The main categories of SQL alternatives include natural language interfaces, semantic layers, and self-service BI platforms. Each offers unique benefits for different organizational needs.
Why are teams looking for SQL alternatives in 2026?
Teams are seeking SQL alternatives due to the inefficiencies of traditional SQL workflows, the need for faster data access, and the growing importance of AI in reducing costs and improving decision-making.
How does Kaelio improve data analysis over traditional SQL?
Kaelio enhances data analysis by providing a natural language interface that respects existing governance rules, ensuring accuracy and compliance while offering immediate, trustworthy answers.
What limitations does SQL have as a primary analytics tool?
SQL's limitations include low accuracy at scale, complex workflows requiring multiple queries, and governance gaps that can lead to compliance risks, especially in high-stakes environments.
How does Kaelio integrate with existing data infrastructure?
Kaelio connects to existing data stacks, including data warehouses, transformation tools, and semantic layers, to provide governed, auditable SQL that aligns with an organization's data governance framework.
What makes Kaelio a strong choice among SQL alternatives?
Kaelio is a strong choice due to its ability to integrate with existing infrastructure, prioritize accuracy and auditability, and provide governed answers in plain English, making it ideal for enterprise environments.
Sources
- https://kaelio.com/blog/kaelio-vs-julius-for-translating-natural-language-into-governed-sql
- https://spider2-sql.github.io/
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- https://www.gartner.com/en/documents/5519595
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