Top 3 Analytical Applications for 2025

With 20 years of navigating the wild world of Business Intelligence systems, I’ve seen tech evolve faster than a caffeinated hamster on a sugar rush! In 2024, my top three platforms—Trino, Snowflake, and Databricks—each bring their own flair to the AI party. Trino is like the charming host who knows how to query every corner of the data room without breaking a sweat, making it perfect for those with a multi-cloud setup (because who doesn’t love a good data mingle?). Snowflake is the sophisticated guest who shows up with an endless supply of data—secure, easily sharable, and ready to scale like a pro, ensuring you never run out of analytics to chew on. Finally, Databricks is the overachiever with a Lakehouse that integrates data engineering and machine learning, making it the go-to for teams looking to whip up insights faster than you can say "data-driven decisions." Together, these platforms are the superheroes of modern analytics, ready to save the day in our AI-driven world!

?Lets get into a bit more detail around my top 3.

Databricks is quicker for big data processing, machine learning, and streaming workloads, particularly when handling massive datasets or combining analytics with data science workflows.

Snowflake is quicker for structured data analytics and SQL-based queries within a centralized data warehouse. Its cloud-native optimizations make it highly efficient for large-scale business intelligence and analytics.

Trino can be quicker for federated querying across multiple data sources. Performance is blistering quick and can be used for AI and data science applications. It does depend on the underlying data sources namely Hadoop and Iceberg file storage. Or cloud storage in Azure ?Blob, AWS S3 and GCS (Google Cloud Storage).

Each platform excels in different areas, and choosing the quickest one will depend on your specific use case, the type of data you're working with, and the required processing.

Architectural Overview:

Trino (formerly Presto)

Distributed SQL query engine.

• Key Components: Coordinator Node: Manages query planning and execution. Worker Nodes: Execute queries in parallel across multiple nodes. Connectors: Query data from various external sources (e.g., HDFS, S3, databases over 100+ connectors).
• Strength: Best for federated querying across multiple data sources. Storing data in a centralised database or storage system if required . Any file format can be used especially iceberg which is great for AI.

Snowflake

Cloud-native data warehouse.

• Key Components: Multi-cluster Compute (Virtual Warehouses): Elastic compute nodes that can scale independently of storage. Centralized Storage Layer: Data is stored centrally in cloud object storage. Metadata Services: Handles query optimisation, indexing, and caching.
• Strength: Optimized for SQL-based analytics with automatic scaling and query optimisation.

Databricks

Unified data analytics platform (built on Apache Spark).

• Key Components: Apache Spark Clusters: Distributed compute engine for large-scale processing. Delta Lake: Provides reliable data storage with ACID transactions, indexing, and caching. Workspace and Notebooks: Integrated environment for data engineering, analytics, and machine learning.
• Strength: Excellent for big data processing, machine learning, and real-time streaming.

Most frequently asked questions are :

Which Product is quickest to retrieve results ?

Trino is typically quicker for: Federated Queries Across Multiple Data Sources: If you need to run queries across data stored in different systems without moving it to a central warehouse, Trino’s federated query engine can provide good speed. For heavy analytical processing it can be faster than Databricks and Snowflake. Requires a specific custom setup that DELIVERBI can help with. Out of the box solutions include Starburst. Ad-hoc Data Exploration: For environments with disparate data sources, Trino allows for querying them without preprocessing or data movement, which can save time and make it quicker for exploration.

Databricks is typically quicker for: Big Data Processing: Databricks excels at large-scale ETL, machine learning, and streaming workloads. For any tasks involving massive datasets, especially unstructured or semi-structured data, Databricks will often be quicker due to Spark’s in-memory and distributed processing capabilities. Machine Learning and Data Science Workflows: If you're doing machine learning model training, feature engineering, or large-scale transformations, Databricks can be significantly faster because it handles the entire data pipeline efficiently. Real-time Data Processing: Spark Streaming, supported by Databricks, allows for real-time data analytics, making it faster in streaming contexts compared to Snowflake or Trino.

Snowflake is typically quicker for: SQL Analytics on Structured Data: Snowflake’s cloud-native architecture and automatic optimizations make it very fast for querying large, structured datasets using SQL. For most analytical queries, Snowflake will be quicker than Databricks due to its data warehousing optimizations. Interactive Analytics: For dashboards or repeated queries on the same data (especially if cached), Snowflake can provide near real-time performance. Ease of Scaling: Snowflake’s architecture allows for quick, on-demand scaling of compute resources, enabling fast processing of complex queries without manual tuning.

Which product is most cost effective ?

Trino is the cheapest option in terms of software since it is open-source, but the overall cost depends on the infrastructure. It's ideal if you already have existing data infrastructure (e.g., a data lake) and need a low-cost, federated querying engine with blistering performance.

Snowflake can be cost-effective for analytics workloads due to its pay-for-use model and the ability to scale compute separately from storage. However, for constant heavy processing or large datasets, costs can rise very quickly.

Databricks is often more expensive than Snowflake for simple SQL workloads but can be cheaper and more efficient for large-scale data processing and machine learning workloads because of Spark’s distributed processing model.

?Which product is easiest to support and maintain ?

• Snowflake is the easiest to maintain, ideal for companies that want a hands-off experience with minimal operational overhead.
• Databricks requires moderate effort, with automated features for cluster management but still some involvement in configuring workloads.
• Trino is the hardest to maintain, as it requires hands-on management of infrastructure, tuning, and scaling. This is where DELIVERBI can help setup a self healing and auto scaling architecture. Starburst which is a company that specialises in a Trino managed environment can also take the stress of deploying Trino and takes a hands off approach in terms of maintenance and support.

AI and roadmaps for product enhancements into 2025 and which product will excel.

Predicting the best platform for AI and data analytics in 2025 feels a bit like trying to pick the winner of a three-legged race—everyone’s got their strengths, but some might just stumble! Databricks is likely to steal the show with its impressive Lakehouse architecture, seamlessly blending data engineering, analytics, and machine learning like a master chef whipping up a gourmet dish. Its constant upgrades for machine learning, especially with features that support large language models, make it the darling of organizations looking to ride the AI wave. Snowflake the reliable in this trio, strutting around with its cloud-native data warehousing and data governance swagger, appealing to enterprises that prioritise low maintenance.

But let’s not forget about Trino—the quirky, fast-paced query engine that’s like the life of the party, effortlessly juggling connections to diverse data sources while serving up lightning-fast queries. In a world where data is scattered across every cloud and corner, Trino excels at making sense of it all, proving that sometimes it’s not just about having the flashiest features, but also being the one who can bring everyone together for a great time! So, for organisations seeking a comprehensive, scalable, and user-friendly solution for AI and analytics in 2025, Databricks may be the star, but Trino is the underdog you definitely want in your corner!

Which application do you prefer and why ?