Singer Reviews

Google BigQuery Reviews

Singer mentions (7)

• Why do companies still build data ingestion tooling instead of using a third-party tool like Airbyte?

  Coincidently, I saw a presentation today on a nice half-way-house solution: using embeddable Python libraries like Sling and dlt - both open-source. See https://www.youtube.com/watch?v=gAqOLgG2iYY There is also singer.io which is more of a protocol than a library, but can also be installed although it looks like it is a true community effort and not so well maintained. Source: over 2 years ago

• Data sources episode 2: AWS S3 to Postgres Data Sync using Singer

  Singer is an open-source framework for data ingestion, which provides a standardized way to move data between various data sources and destinations (such as databases, APIs, and data warehouses). Singer offers a modular approach to data extraction and loading by leveraging two main components: Taps (data extractors) and Targets (data loaders). This design makes it an attractive option for data ingestion for... - Source: dev.to / about 3 years ago

• CDC (Change Data Capture) with 3rd party APIs

  Or you could build your own such system and run it on Airflow, Prefect, Dagster, etc. Check out the Singer project for a suite of Python packages designed for such a task. Quality varies greatly, though. Source: almost 4 years ago

• Looking to build a database for BI reports

  This is good advice and I think Airbyte created a great product here. I tried singer.io and pipewise but Airbyte is much better in my opinion and I love the UI. Source: almost 5 years ago

• Recommendation for approach for populating and refreshing new data lake

  Suspect my question should have been regarding FREE systems, rather than BUYING a system. Sounds like singer.io will do what I need. Source: about 5 years ago

Google BigQuery mentions (47)

• Ruby on Rails Performance: 7 Lessons from Scaling FirstPromoter

  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 — 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 / 4 months ago

• How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better

  Google BigQuery - For large-scale data processing and SQL-based analysis. - Source: dev.to / 5 months ago

• What if ML pipelines had a lock file?

  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 / 6 months ago

• Best SQL Courses with Certificates for 2026

  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 transferability—while dialects differ across PostgreSQL, SQL Server, and BigQuery, the fundamentals stay consistent. - Source: dev.to / 8 months ago

• Why Your Snowflake Bill is High and How to Fix It with a Hybrid Approach

  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 / 9 months ago