Enterprises face significant challenges with their Hadoop infrastructures, driving the need for modernization. Upgrading the SQL engine is a highly effective solution, providing data engineers and developers with immediate performance enhancements and cost savings without requiring a full system overhaul. This guide outlines how to upgrade your SQL engine to Starburst, harnessing the capabilities of Trino and Apache Iceberg, to transform your data architecture.
Why should you modernize? Traditional Hadoop infrastructures, while revolutionary in their time, now face several challenges:
These challenges make it imperative for organizations to seek modern, scalable, and cost-effective solutions. Upgrading your SQL engine from Hive to Trino represents a significant leap forward in terms of performance, scalability, and efficiency. Trino is designed to handle these challenges with its massively parallel processing (MPP) architecture, which allows for the rapid execution of queries across large datasets. This upgrade can drastically reduce query times, enabling faster decision-making and more responsive data-driven operations. Additionally, Trino’s ANSI SQL compatibility ensures that your existing SQL knowledge and queries can be easily adapted, minimizing the learning curve and streamlining the transition process.
Why upgrade to Starburst’s Trino-based SQL engine?
Starburst, built on the open-source Trino, offers a robust solution to these challenges. Trino is designed to process large-scale data at high speeds, making it ideal for enterprises dealing with extensive data workloads. By optimizing resource utilization and reducing the need for extensive hardware, Starburst can significantly lower operational costs. Additionally, Starburst supports a wide range of data sources and formats, enabling seamless integration with existing data ecosystems.
Starburst’s runtime replaces Hive’s runtime by utilizing the existing metastore metadata and files residing in storage. With Hadoop, scaling up requires paying for both storage and compute, leading to higher infrastructure costs. Starburst, on the other hand, connects to the storage and handles the compute process for reading files, allowing you to add more vCPU if needed. This separation of compute and storage ensures more efficient and cost-effective scaling compared to Hadoop.
Key features of Starburst’s SQL engine and lakehouse platform:
- Enhanced query performance: Trino’s MPP architecture allows for efficient query execution across large datasets, drastically improving performance over Hive and Impala.
- Comprehensive security and governance: With built-in access controls, data lineage, and schema monitoring, Starburst ensures robust data governance and security.
- Unified data access: Starburst provides a single point of access to various data sources, whether on-premises or in the cloud, supporting over 50 connectors to different data environments.
- Managed Iceberg tables: By integrating with Apache Iceberg, Starburst ensures efficient data management and optimization, supporting features like ACID transactions, time travel, and partition evolution.
Step-by-Step Guide to Upgrading Your SQL Engine
- Assess your current infrastructure: Begin by evaluating your existing Hadoop setup, identifying performance bottlenecks, and understanding your data governance requirements.
- Plan the transition: Define the scope of the upgrade. Determine whether you need a full migration of all workloads or a phased approach targeting the most critical workloads first.
- Set-up Starburst Enterprise: Deploy Starburst Enterprise in your environment. This can be done on-premises, in a cloud environment, or in a hybrid setup.
- Connect to your data sources: Configure Starburst to connect to your existing data sources, whether they are in HDFS, cloud storage, or other databases. Utilize Starburst’s connectors for seamless integration.
- Migrate your workloads: Begin migrating your workloads from Hive or Impala to Starburst. This involves translating your existing SQL queries to be compatible with Trino.
- Optimize and validate: Perform optimization tasks such as indexing, caching, and query tuning to ensure maximum performance. Validate the migrated workloads to ensure they meet performance and accuracy requirements.
- Monitor and manage: Use Starburst’s built-in monitoring tools to keep track of performance metrics, data access patterns, and overall system health.
Optimize Data Accessibility and Achieve 10-20x Faster Queries
Optum faced challenges with its data warehouse, which couldn’t support growing analytics demands, leading to poor user experience and restricted new workloads. By replacing its previous solution with Starburst for its Hadoop environment, Optum achieved 10x faster queries, a 30% reduction in infrastructure costs, and projected savings of $8 million
Summary: Modernize your data infrastructure
Upgrading your SQL engine to Starburst represents a pivotal step in modernizing your data infrastructure. It provides immediate ROI through improved performance, scalability, and cost-efficiency, setting the stage for further advancements in your data strategy. For data engineers and developers, this transition not only addresses current pain points but also opens up new possibilities for innovation and growth.
Embrace the future of data management with Starburst and experience the transformation firsthand. For more detailed information on upgrading to Starburst’s SQL engine and Hadoop modernization, visit our solution page, or explore our modern data lakehouse resources.
