(Credit: TigerData.com)
TigerData as we speak launched Tiger Lake, an structure it calls “the lacking layer between Postgres and the lakehouse for the age of brokers.” The discharge is a part of the corporate’s effort to make it simpler for builders to work with each real-time and historic knowledge with out counting on fragile pipelines or delayed batch syncing.
Tiger Lake is constructed instantly into Tiger Postgres, the corporate’s personalized model of PostgreSQL designed for real-time and analytical workloads. In keeping with TigerData, the brand new structure permits Postgres to sync with Iceberg-backed lakehouses like AWS S3 in each instructions. The structure at present affords native assist for AWS S3 Tables, with integration for different lakehouse codecs anticipated in future updates.
The purpose with Tiger Lake is to offer builders an easier approach to construct purposes, dashboards, and AI brokers that depend upon each recent operational knowledge and long-term analytical insights. TigerData says the structure is supposed to scale back complexity whereas maintaining knowledge methods versatile and open.
As co-founder and CTO Mike Freedman explains, Postgres has change into the operational coronary heart of many fashionable purposes, but it surely has remained remoted from the lakehouse layer. “With Tiger Lake, we’ve constructed a local, bidirectional bridge between Postgres and the lakehouse,” he mentioned. “It’s the structure we imagine the business has been ready for.”
Many groups at present depend on a patchwork of instruments to maneuver knowledge between methods, usually utilizing Kafka, Flink, or customized scripts. That form of setup might be fragile and costly to keep up. TigerData says Tiger Lake replaces that complexity with built-in, real-time sync throughout Postgres and Iceberg.
(Credit:TigerData.com)
That was a key motivator for Speedcast. “We stitched collectively Kafka, Flink, and customized code to stream knowledge from Postgres to Iceberg—it labored, but it surely was fragile and high-maintenance,” mentioned Kevin Otten, Director of Technical Structure at Speedcast. “Tiger Lake replaces all of that with native infrastructure. It’s not simply easier—it’s the structure we want we had from day one.”
Past syncing tables, a key function of Tiger Lake is its skill to assist a two-way stream of knowledge. Operational knowledge strikes into the lakehouse for long-term storage or evaluation, whereas outcomes, like aggregates, ML options, or historic summaries, might be pushed again into Postgres to be used in dwell purposes.
TigerData emphasizes that one of many major benefits of utilizing Tiger Lake is that it permits customers to keep away from vendor lock-in. It makes use of open codecs like Iceberg, runs on AWS S3, and connects with present ecosystems for machine studying, monitoring, and analytics. Builders don’t must rebuild their stack or swap platforms to make use of it.
This launch additionally displays broader shifts within the knowledge world. Postgres continues to rise in recognition for operational workloads, whereas Iceberg is gaining floor as the usual for open lakehouses. With extra AI-driven apps needing entry to each latest context and deep historic perception, Tiger Lake positions itself because the connective tissue between these layers.
For ML groups that want recent options, analytics teams working with long-range developments, or builders constructing AI brokers and dashboards, Tiger Lake may make it simpler to attach totally different knowledge methods with out having to handle advanced integrations.
If we zoom out a bit, the introduction of Tiger Lake aligns with TigerData’s broader focus. The corporate initially launched as Timescale in 2017, gaining recognition for its time-series extensions to PostgreSQL. However because it expanded into vector search, real-time analytics, and AI-native workloads, the crew rebranded to TigerData earlier this yr to higher mirror its wider ambitions.
(Shutterstock AI Picture)
“Fashionable purposes don’t match neatly into conventional database classes. They seize huge streams of information, energy real-time analytics, and more and more depend on clever brokers that motive and act. These workloads—transactional, analytic, and agentic—require a brand new form of operational database,” mentioned Ajay Kulkarni, Co-founder and CEO. “That’s precisely what we’ve constructed at TigerData: a system that delivers pace with out sacrifice.”
TigerData has grown to now serve greater than 2,000 organizations, together with Mistral, HuggingFace, Nvidia, Toyota, Tesla, NASA, JP Morgan Chase, Schneider Electrical, Palo Alto Networks, and Caterpillar. Additionally it is an AWS Associate with options obtainable on AWS Market. The corporate has raised over $180 million from backers like Benchmark, NEA, Redpoint, and Tiger World.
The corporate says Tiger Lake is just the start. Future updates will embody the power to question Iceberg catalogs instantly, ship analytical outcomes again into Postgres, and broaden assist for Iceberg-based workflows. TigerData can also be engaged on efficiency upgrades, together with sooner large-scale inserts and a brand new storage structure designed to make forks and replicas extra environment friendly.
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