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BigQuery Ingestor

An example of how to ingest data from Big Query to harper efficiently.

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JavaScript

BigQuery Ingestor

Ivan R. Judson, Ph.D.

Distinguished Solution Architect

at Harper

November 13, 2025

Ivan R. Judson, Ph.D.

Distinguished Solution Architect

at Harper

November 13, 2025

Ivan R. Judson, Ph.D.

Distinguished Solution Architect

at Harper

November 13, 2025

November 13, 2025

An example of how to ingest data from Big Query to harper efficiently.

Ivan R. Judson, Ph.D.

Distinguished Solution Architect

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Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.

An example of how to ingest data from Big Query to harper efficiently.

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An example of how to ingest data from Big Query to harper efficiently.

Download

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An example of how to ingest data from Big Query to harper efficiently.

Download

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Explore Recent Resources

Faster Vector Search in Harper 5.3 with Native HNSW

Explore vector index in Harper 5.3’s with benchmarks against pgvector and Harper 5.2, and the open-source Node.js package @harperfast/hnsw.

Comparison

Explore vector index in Harper 5.3’s with benchmarks against pgvector and Harper 5.2, and the open-source Node.js package @harperfast/hnsw.

Kris Zyp

SVP of Engineering

Faster Vector Search in Harper 5.3 with Native HNSW

Explore vector index in Harper 5.3’s with benchmarks against pgvector and Harper 5.2, and the open-source Node.js package @harperfast/hnsw.

Faster Vector Search in Harper 5.3 with Native HNSW

Explore vector index in Harper 5.3’s with benchmarks against pgvector and Harper 5.2, and the open-source Node.js package @harperfast/hnsw.

Faster Vector Search in Harper 5.3 with Native HNSW

Explore vector index in Harper 5.3’s with benchmarks against pgvector and Harper 5.2, and the open-source Node.js package @harperfast/hnsw.

What If Your AI App Didn’t Need All Those Services?

Explore how Harper brings search, embeddings, model access, and controlled AI decisions into one runtime, reducing the services around your AI application.

Blog

Explore how Harper brings search, embeddings, model access, and controlled AI decisions into one runtime, reducing the services around your AI application.

Aleks Haugom

Senior Manager of GTM

What If Your AI App Didn’t Need All Those Services?

Explore how Harper brings search, embeddings, model access, and controlled AI decisions into one runtime, reducing the services around your AI application.

What If Your AI App Didn’t Need All Those Services?

Explore how Harper brings search, embeddings, model access, and controlled AI decisions into one runtime, reducing the services around your AI application.

What If Your AI App Didn’t Need All Those Services?

Explore how Harper brings search, embeddings, model access, and controlled AI decisions into one runtime, reducing the services around your AI application.

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

Blog

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

Austin Akers

Head of Developer Relations

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Blog

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Austin Akers

Head of Developer Relations

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Media Coverage

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Renato Losio

InfoQ Staff Editor

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Blog

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Kris Zyp

SVP of Engineering

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

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