Google BigQuery
Databricks
Looker
Jupyter
Presto DB
Amazon EMR
Google Cloud Dataflow
Rakam
Temporal
Trigger.dev
n8n.io
Pipedream
Amazon AWS
Apache Airflow
Aditya Protocol
Molted
Google BigQuery TemporalNo features have been listed yet.
Based on our record, Google BigQuery should be more popular than Temporal. It has been mentiond 47 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
We migrated the analytics layer to Google BigQuery. Same queries that timed out in PostgreSQL now run in under 2 seconds. But not everything belongs in BigQuery โ we initially moved too aggressively and actually reverted some queries back when the added complexity wasn't justified. Our rule of thumb: if a query scans hundreds of thousands of rows or involves complex time-series aggregations, BigQuery. Everything... - Source: dev.to / 3 months ago
Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 4 months ago
Data Pipelines usually read from tables that change over time. Most of these tables are stored in a data warehouse like Amazon Redshift or Google BigQuery. Rows are added or removed. Backfills happen. A column gets renamed or its meaning changes. Even when teams snapshot data, those snapshots are often implicit, not recorded as part of the pipeline run itself. - Source: dev.to / 5 months ago
SQL endures because it's the non-negotiable interface for relational data. Enterprise data storage still relies heavily on relational databases despite new alternatives. What makes SQL valuable for learners is transferabilityโwhile dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 7 months ago
Within classic cloud data warehouses, Google BigQuery presents a different pricing model. Its on-demand, per-terabyte-scanned pricing can be cost-effective for sporadic forensic queries. But it carries the risk of a runaway query where a single mistake leads to a massive bill. - Source: dev.to / 8 months ago
Durable execution โ the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows โ treats waiting as a continuation rather than a loop:. - Source: dev.to / 2 days ago
Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal โ Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / 29 days ago
We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of โ a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines โ an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate โ plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 2 months ago
The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago
A typical production stack for teams using Claude or Gemini as the reasoning layer includes an LLM provider API, an orchestration layer (n8n, Temporal, or a custom Python service), application infrastructure (a server running the orchestration code), and a data layer (a database for storing results). Each boundary introduces a failure point. When the LLM provider changes its rate limits, as OpenAI did repeatedly... - Source: dev.to / 3 months ago
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.โWhat is Apache Spark?
Trigger.dev - Trigger workflows from APIs, on a schedule, or on demand. API calls are easy with authentication handled for you. Add durable delays that survive server restarts.
Looker - Looker makes it easy for analysts to create and curate custom data experiencesโso everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Pipedream - Integration platform for developers
