TigerData Launches Tiger Lake to Bridge Postgres and the Open Lakehouse

Here’s a unique article for archyde.com based on the provided text, focusing on the “shift away from compromise” and the benefits of “developer experience without the glue code.”

---

Beyond the Patchwork: Tiger Lake Ushers in an Era of Seamless Data Integration

For too long, the digital realm has been a landscape of intricate, often fragile, data pipelines. Engineering teams have spent countless hours wrestling with a motley crew of tools – Kafka, Flink, custom scripts – simply to synchronize information between vital systems like PostgreSQL and the increasingly popular Iceberg data format.This complex “glue code” era, while functional, has been a constant source of instability and a drain on valuable progress resources. Now, a new architecture is emerging to liberate us from this compromise.

Enter Tiger Lake, a promising new platform that heralds a meaningful paradigm shift. Its core philosophy is rooted in unification, a direct challenge to the fragmented approaches that have become the norm. As articulated by the development team, Tiger Lake aims to “unify both, natively and without compromise.” This isn’t just a minor upgrade; it’s a basic rethinking of how we connect and manage data across different layers of our infrastructure.

The Developer’s Dream: Intelligence Without the Overhead

The most immediate impact of Tiger Lake is highly likely to be felt by the developers laboring behind the scenes. The burden of stitching together disparate systems is a notorious pain point. Consider the experience of Kevin Otten, Director of Technical Architecture at Speedcast. He describes their previous setup: a complex web of Kafka,Flink,and custom code to stream data from PostgreSQL to Iceberg. “It worked, but it was fragile and high-maintenance,” Otten admits. “Tiger Lake replaces all of that with native infrastructure. It’s not just simpler-it’s the architecture we wish we had from day one.”

This sentiment underscores a critical advantage: developer experience. By offering a natively integrated, real-time stack, Tiger Lake promises a dramatic uplift in reliability and maintainability. This means less time spent debugging brittle connections and more time focusing on innovation. Companies like Speedcast are already demonstrating the power of this approach, replacing their previous “fragile pipelines with a natively integrated, real-time stack that’s built to scale.”

The benefits extend to the very nature of how we access and utilize data. Tiger Lake is designed to provide low-latency access to both live operational events and the deep well of past insights. This effectively bridges the gap between immediate application context and the broader analytical landscape.Whether it’s feeding real-time dashboards or powering elegant agentic behaviors, the goal is to make intelligence readily available at the precise moment it’s needed – at the point of interaction.

Openness and Modularity: Power Without the Shackles

Crucially, Tiger Lake is not pushing a proprietary, all-encompassing solution. instead, its foundation is built on openness and composability. By connecting PostgreSQL to Iceberg thru open formats,it actively avoids the common pitfalls of vendor-controlled ecosystems and their restrictive metadata layers.

This commitment to openness means Tiger Lake is designed to integrate seamlessly with the broader cloud infrastructure that organizations already rely on. This includes effortless integration with various query engines, machine learning workflows, and observability platforms, all without the dreaded vendor lock-in. Developers retain the freedom to leverage their preferred tools, from cloud storage solutions like S3 to powerful data warehouses like Snowflake, all while preserving the modularity of their existing systems.

A leap Forward: Now in Public Beta

The promise of Tiger Lake is no longer a future vision; it’s a present reality. The platform is now available in public beta, fully managed through Tiger Cloud.The initial release focuses on the essential task of streaming data from PostgreSQL and TimescaleDB hypertables into AWS S3 Tables in Iceberg format, with the capability to sync data back from S3 into PostgreSQL.The roadmap ahead is even more enterprising, hinting at future functionalities like direct querying of Iceberg catalogs from within postgresql and enabling complete round-trip workflows. This includes seamlessly reintegrating analytical outputs,such as aggregates or machine learning results,back into PostgreSQL for immediate,real-time utilization.

In essence, Tiger Lake is offering a unified foundation for real-time applications. It’s a platform built to empower organizations without forcing them to sacrifice control, performance, or the flexibility they need to thrive in a dynamic digital world. The era of compromise in data integration may finally be drawing to a close.

---

How does Tiger Lake address the challenges of combining transactional consistency with the schema-on-read approach of data lakes?

understanding the Convergence: Postgres, Data Lakes, and Tiger Lake

The modern data landscape is increasingly hybrid. Organizations are realizing the limitations of siloed data systems and seeking ways to combine the strengths of traditional databases like PostgreSQL with the scalability and flexibility of data lakes and the emerging open lakehouse architecture. This is where TigerData’s Tiger Lake comes in. It’s designed to seamlessly integrate Postgres with the open lakehouse, unlocking new possibilities for data analysis and application progress.

The core challenge lies in bridging the gap between the transactional consistency of a relational database and the schema-on-read approach of a data lake. Traditional ETL (Extract, Transform, Load) processes are often slow, complex, and introduce latency. Tiger Lake aims to solve this with a more direct and efficient approach.

What is Tiger lake and How Does it Work?

Tiger Lake is a data virtualization layer built to connect PostgreSQL directly to open lakehouse storage formats like Apache Parquet and Apache Iceberg. It doesn’t require data movement, rather providing a unified query interface. Here’s a breakdown of it’s key components:

Virtualization Engine: This is the heart of Tiger Lake.It translates PostgreSQL queries into operations optimized for the underlying lakehouse storage.

Metadata Management: Tiger Lake maintains a metadata catalogue that maps PostgreSQL tables and columns to the corresponding data in the lakehouse.This allows for efficient data discovery and access.

query Optimization: The system intelligently optimizes queries to leverage the parallel processing capabilities of the lakehouse, resulting in considerably faster query performance.

Data Governance & Security: Tiger Lake inherits and extends the security and governance features of both PostgreSQL and the lakehouse platform, ensuring data compliance and protection.

Key Benefits of Using Tiger Lake

Integrating Postgres with an open lakehouse via Tiger Lake offers several compelling advantages:

reduced Data Movement: Eliminating ETL processes saves time,resources,and reduces the risk of data inconsistencies.

Real-time Analytics: Query data directly in the lakehouse without waiting for batch updates, enabling real-time insights.

Scalability & Cost Savings: Leverage the cost-effective storage and scalability of data lakes without sacrificing the reliability of PostgreSQL.

Unified Data Access: Provide a single point of access to all your data, irrespective of where it resides.

Enhanced Data Science Workflows: Empower data scientists with access to a broader range of data for machine learning and advanced analytics.

Modern Data Stack Integration: Seamlessly integrates with popular data lakehouse technologies like Delta Lake, Apache Hudi, and Apache Iceberg.

Tiger Lake vs. Traditional ETL: A Comparison

| Feature | Traditional ETL | Tiger Lake |

|—|—|—|

| Data Movement | Required | Eliminated |

| Latency | high | Low |

| Complexity | High | Low |

| Cost | High (infrastructure, maintenance) | Lower (reduced infrastructure, simplified management) |

| Real-time Analytics | Limited | Enabled |

| scalability | Can be challenging | Highly scalable |

Use Cases for Tiger Lake

Tiger Lake is applicable across a wide range of industries and use cases:

Financial Services: Risk management, fraud detection, and regulatory reporting by combining transactional data from PostgreSQL with market data in the lakehouse.

Retail: Personalized recommendations, inventory optimization, and customer segmentation by analyzing sales data from PostgreSQL alongside website activity and social media data.

Healthcare: Patient outcome analysis, clinical trial optimization, and population health management by integrating electronic health records (postgresql) with genomic data and research datasets.

Manufacturing: Predictive maintenance, quality control, and supply chain optimization by combining sensor data from IoT devices with production data from PostgreSQL.

Marketing: Customer 360 views, campaign performance analysis, and lead scoring by integrating CRM data (PostgreSQL) with marketing automation data and web analytics.

Practical tips for Implementing Tiger Lake

Start Small: Begin with a pilot project to test the integration and validate the benefits.

Metadata Management is Key: Invest in a robust metadata management strategy to ensure data discoverability and accuracy.

optimize Queries: Leverage Tiger Lake’s query optimization features to maximize performance.

Security First: Implement appropriate security measures to protect sensitive data.

Monitor Performance: Continuously monitor the performance of the integration and make adjustments as needed.

* Consider Data Governance: Establish clear data governance policies to ensure data quality and compliance.

The Future of Data Integration: Open Lakehouses and Postgres

Tiger Lake represents a significant step towards a more unified and efficient data architecture.By bridging the gap between PostgreSQL and the open lakehouse,