Datalake and Lakehouse: Comparison of Apache Kylin and Trino for Business Intelligence Analytics
- Claude Paugh
- Jul 23
- 6 min read
In today's dynamic business landscape, having the right tools for data analysis and business intelligence can make all the difference. With the vast amount of data available, businesses need efficient ways to process and analyze it for better decision-making. Two powerful platforms that stand out in this area are Apache Kylin and Trino, also known as Presto. While both serve important functions in analytics, understanding how they differ is key for data professionals looking to leverage these technologies effectively.
This article provides a comparison of Apache Kylin and Trino, focusing on their query capabilities and aggregation methods to determine which is best suited for your analytics needs.
Understanding Apache Kylin
Apache Kylin is an open-source analytics engine designed for fast OLAP (Online Analytical Processing) on big data platforms. Relying on Spark and Hadoop, Kylin lets users create data Cube data models for speedy query responses. According to Apache, its pre-aggregation features can improve query speeds by up to 100x compared to traditional methods.
Kylin is ideal for business intelligence applications that require quick, reliable insights, particularly when dealing with large data sets—something businesses often struggle with. Kylin provides drivers for connections to BI tools, such as Tableau and Power BI. Kylin's competitive peers in the market place would be Microsoft Analysis Services & Cloud equivalent, IBM Cognos, SAP Business Objects, Looker, Qlick, etc.
Understanding Trino (Presto)
Trino, originally known as Presto, is an open-source SQL query engine that allows analytics professionals to query data from various sources in real-time. It excels in scenarios where data analysts need to run complex queries across multiple data lakes and relational databases.
With Trino, users can perform integrated data analysis without preparing a single data warehouse, making it incredibly flexible for modern analytical challenges. According to its creators, Trino can query petabytes of data in just seconds, making it an attractive alternative for real-time analytics. Trino's competitive peers would be AWS Glue, Databricks, Google BigQuery, AWS Redshift Spectrum, Apache Drill, and Clickhouse, to name a few.
Comparing Key Features
Both Kylin and Trino, and their various peers products have overlap with key features and functions. All of the products are attempting to capture the core OLAP (data Cube) functionality and multi-source/multi-format data with ad-hoc and batch queries.
Many of them are attempting to query incoming data in every/any format and aggregate that data in multiple-dimensions in "real" time. No tool set does that with a simple deployment, and without metadata stores to catalog data, it's not possible.
The aggregate and drill through to detail data in "real" time still requires additional engineering and configuration to get close to that goal. That's before you get to anomaly and quality scrubbing, which ideally should be done before seeding data for training AI/ML models.
To provide clarity on how Apache Kylin and Trino stack up against each other, let’s look at their key features side by side.
Performance and Speed
Feature | Apache Kylin | Trino (Presto) |
Query Performance | Pre-aggregated data results in faster responses | Queries may experience latency depending on data source complexity and federation of sources |
Data Size Handling | Optimized for handling massive datasets with data Cube technology | Efficiently manages both small and large datasets |
Apache Kylin’s pre-aggregation boosts query performance dramatically. In contrast, while Trino can handle large datasets efficiently, its real-time queries can sometimes introduce latency, especially with larger, more complex data sources.
Data Modeling
Apache Kylin | Trino (Presto) |
Requires structured data Cube models for optimization | Schema-less; direct querying from data sources without a model. Uses source data model if available. |
Kylin’s need for data Cube models can make it less flexible, but it significantly enhances query speed. Trino’s schema-less nature allows users to explore various data sources instantly, adding to its adaptability, but it does not fit every scenario.
SQL Capabilities
Feature | Apache Kylin | Trino (Presto) |
Federated Queries | No --> potential using Hive and pushdown | Yes --> RDBMS, noSQL, DataLakes |
SQL Standard | Kylin 5.0 supports ANSI SQL 2003 | Full ANSI SQL capabilities available |
Ad-hoc and Batch Queries | No --> requires additional engineering | Yes --> feature built-in |
Trino stands out with its full ANSI SQL support, making it easy to execute complex queries. In contrast, Kylin's strict cube structure imposes some limitations, but the new 5.0 release offers ANSI SQL 2003 compliance as well.
Compatibility and Ecosystem
Feature | Apache Kylin | Trino (Presto) |
Data Sources | Hadoop, Hive and its underlying data sources (Iceberg, Parquet, MySQL,PostgresSQL, etc.) | Data sources including MySQL, PostgreSQL, Parquet, MongoDB, etc. |
Trino’s ability to interface with various data sources allows for greater flexibility, while Kylin, although efficient in its Hadoop-centric ecosystem, may struggle when adapting to varied data environments. Expanding to include Hive and it's connected sources (Iceberg, Parquet, ORC, RDBMS, JDBC sources, etc.) enhances Kylin expansion to additional data.
Business Intelligence: Query Execution
Query execution techniques are crucial in differentiating these two platforms. Here’s how both handle it:
Query Execution in Apache Kylin
Data Modeling: Users define metrics and dimensions within a data Cube, setting the stage for optimized queries. It implements a multi-dimensional data model using dimensions and measures.
Pre-aggregation: Kylin pre-aggregates data based on these definitions, ensuring rapid access to metrics. When using Hive, the additional sources can be included in this aggregation step.
Instant Execution: When a query runs, Kylin retrieves these pre-aggregated results, significantly reducing processing time.
The pre-aggregation method is especially helpful when generating reports that must be refreshed quickly or user on-demand queries from BI tools.
Query Execution in Trino
Live Querying: Users can execute SQL directly against diverse data sources, tapping into live data.
Data Federation: Trino seamlessly queries across systems without needing to map data beforehand.
Optimized Performance: Trino leverages optimization techniques to reduce latency and enhance query speed.
While Trino does not match Kylin's pre-aggregation speed, its approach offers flexibility that is vital for real-time analytics.
Aggregation Mechanics
Aggregation is vital for deciphering data insights. Here’s how Kylin and Trino manage this:
Apache Kylin Aggregation
Cube Aggregation: Kylin conducts aggregation during data Cube creation, focusing on defined metrics or measures.
Pre-computation: This enables users to access pre-computed metrics quickly during queries.
Granularity Control: Users can set detail levels for the aggregates, allowing for flexible insights for drill-though, roll-up and roll-down.
Trino Aggregation
Dynamic Aggregation: Trino performs real-time aggregations on-the-fly, allowing for rapid data compilation.
SQL Functions: Analysts can utilize built-in SQL aggregation functions for complex calculations.
Distributing Resource Load: Trino uses distributed resources effectively to manage large data operations during aggregation.
Trino provides real-time insights at a cost of greater resource usage, unlike Kylin, which relies on pre-computed results for efficiency.
Ideal Use Cases
Choosing between Apache Kylin and Trino depends on specific business scenarios as outlined below:
Optimal Scenarios for Apache Kylin
Fast Performance with Huge Data: For organizations handling extensive datasets needing swift query results, Kylin is a top choice.
Structured Reporting: If regular reporting involves stable metrics, Kylin’s pre-aggregation optimizes these occurrences.
Heavy OLAP Workloads: Kylin thrives in environments that leverage comprehensive OLAP capabilities.
Optimal Scenarios for Trino
Multiple Data Sources: When analytics require integration across several data systems, Trino offers great flexibility.
Real-Time Decision Making: In cases needing immediate data insights, Trino’s ability for on-the-fly querying is invaluable.
Complex SQL Needs: If your team requires complex SQL capabilities, Trino ensures no question answered by SQL is beyond your reach.
Final Thoughts
In summary, both Apache Kylin and Trino offer unique strengths in the domain of business queries and intelligence analytics. Kylin excels when performance is needed, especially with pre-aggregated data and Cube technology. Trino, however, shines in flexibility and real-time querying capabilities, accommodating various data sources effectively.
For data professionals, recognizing each platform's strengths and weaknesses is crucial for choosing the right tool. Prioritize understanding your organization’s data architecture, performance needs, and analytics goals to enhance your overall strategy.
There is no single tools for real-time data queries and comprehensive analytics, that can also provide data quality intervention to feed AI/ML models for training. Apache Spark is certainly pervasive in conjunction with, many open-source tools as a processing engine. So if you're adopting open-source driven analytics, Spark skills are a must. By aligning the capabilities of either tool with your business requirements, you can significantly enhance your data-driven decision-making.
