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Automation takes the lead — jobs, ingestion, and scale

July 27 – August 2, 2026

DagsterTinybirdClickHouse
Dagster, Tinybird, ClickHouse

Dagster brings declarative automation to entire jobs, Tinybird finalizes its v1 local file ingestion, and ClickHouse publishes a trio of benchmarks and guides covering observability, Postgres memory management, and an espresso machine instrumented with OpenTelemetry.

Declarative automation reaches Dagster jobs

Dagster 1.13.16 extends its Declarative Automation system from individual assets to full jobs — a preview feature that closes a long-standing gap. You can now pass an automation_condition to define_asset_job, wrapping asset-level conditions with AutomationCondition.any_job_root_assets_match or AutomationCondition.all_job_root_assets_match. When the condition turns true, Dagster launches a single run of the entire job, and the evaluation history is visible in a new Automation tab on job pages. This is the kind of change that quietly reorients how teams think about scheduling: instead of daisy-chaining sensors and schedules, you write a condition once and let Dagster figure out the timing.

Tinybird streamlines local ingestion

Tinybird's v1 local file ingestion is now the default on both Forward and Classic CLIs, removing the --experimental=use_v1 flag that was required for CSV, NDJSON, or Parquet file uploads. The CLI release also tweaks job-return behavior: Forward CLI now returns the queued job ID by default (use --wait to block), while Classic CLI waits for completion by default (use --no-wait to background). File ingestion is a gateway workflow — every new pipeline starts with a local file — so making this path frictionless matters more than a headline feature.

ClickHouse: observability, Postgres, and espresso

ClickHouse published a dense week of content. The clickhousectl v0.4.0 release adds horizontal autoscaling for Cloud services, ClickPipe schema discovery, and opt-out anonymous telemetry — practical ops improvements for teams running ClickHouse at scale. Two blog posts tackle real-world comparisons: Benchmarking NVMe-backed Managed Postgres against PlanetScale on identical hardware shows ClickHouse delivering up to 54% higher throughput with lower latency, while Choosing Between ClickStack and Grafana for ClickHouse observability walks through the trade-offs. On the lighter side, instrumenting an espresso machine with OpenTelemetry is a delightful case study: an ESP32 streams shots and house sensors into ClickHouse Cloud, with ClickStack and an LLM agent turning every pull into a queryable event. Finally, a customer story from Fetch shows how they built FAST, a conversational BI agent on ClickHouse Cloud that lets non-technical teams query 150 billion rows a day in plain language. The strict memory overcommit post for Managed Postgres is a crisp technical explainer: turning an instance-wide OOM restart into a single failed query, with no measurable throughput cost.

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