Data Processing

DuckDB36.9k★

DuckDB is SQLite for analytics — an embedded database that runs inside your process with zero setup, but instead of row-based storage, it uses columnar storage optimized for analytical queries. Run complex aggregations on millions of rows from a CSV, Parquet, or JSON file without spinning up a server. If you're building a data-heavy indie project — dashboards, reporting tools, local analytics — DuckDB is your first stop. It's 10-100x faster than SQLite for analytical queries and handles files larger than memory. ClickHouse is the server-based alternative when you need real-time ingestion at scale. BigQuery and Snowflake are the expensive cloud options. The catch: DuckDB is single-machine only. No replication, no clustering, no concurrent writes from multiple processes. It's for analysis, not your app's primary OLTP database. Use Postgres for that, DuckDB for the analytics layer.

Flink25.9k★

Flink is the gold standard for real-time stream processing — purpose-built for event-by-event processing with sub-second latency, exactly-once guarantees, and sophisticated windowing. When you need to process millions of events per second and every single one matters, Flink is what the big players use. Skip this for indie projects. Flink is enterprise infrastructure — complex to deploy, complex to operate, and overkill for anything under massive scale. Kafka Streams is simpler for Kafka-native event processing. Spark Structured Streaming handles batch-and-stream workloads in one engine. Benthos/Redpanda Connect is lightweight for simple pipelines. The catch: Flink requires dedicated infrastructure — JobManagers, TaskManagers, and state backends. The learning curve is significant (watermarks, event time, state management). Java/Scala-first APIs mean Python support is available but second-class. And managed Flink services (AWS Managed Flink, Confluent) are expensive. Unless you're processing millions of events per second, simpler tools will serve you better.

Trino12.7k★

Trino is the distributed SQL engine that queries data where it lives — S3, Postgres, MySQL, Kafka, Elasticsearch — without moving it. Born as PrestoSQL (the original Presto creators left Facebook and took the project with them), it's now the most actively developed query engine for data lakes, with 3x the development velocity of Presto. If you need interactive analytics across multiple data sources without building an ETL pipeline, Trino is the tool. Apache Spark handles batch processing and ML workloads better. PrestoDB (Meta's fork) is similar but slower-moving. DuckDB is the in-process alternative for single-machine analytics. Starburst is the commercial Trino distribution with enterprise support. The catch: Trino is an interactive query engine, not a batch processor. Large joins can OOM your cluster because it keeps intermediate data in memory. Fault-tolerant execution mode exists but is newer and slower. And running a Trino cluster is real infrastructure — coordinators, workers, catalogs, and memory tuning. For most indie projects, DuckDB on a single machine is all you need.

dbt12.5k★

dbt turned SQL transformations into software engineering — version control, testing, documentation, and modular design for your data warehouse. Write SELECT statements, dbt handles the DAG, materializations, and incremental builds. It's the tool that created "analytics engineering" as a discipline. If you're doing data transformation in any modern warehouse (Snowflake, BigQuery, Redshift, Postgres), dbt is the industry standard. SQLMesh is the open-source challenger with better environment isolation and incremental rebuilds — and it was recently acquired by Fivetran alongside dbt Labs, making the competitive landscape interesting. Dataform is Google's free alternative locked to BigQuery. Coalesce is the visual, no-code option. The catch: dbt Cloud costs about $100 per user per month, and the open-source CLI requires you to handle orchestration yourself (Airflow, Dagster, or cron). The Fivetran acquisition raises questions about dbt's independence — tool neutrality matters more than ever. And dbt's YAML-heavy configuration and Jinja templating in SQL creates a learning curve that pure SQL analysts find frustrating.

quix-streams1.5k★

Quix Streams is Kafka stream processing in pure Python — no JVM, no Scala, no cross-language debugging. A Streaming DataFrame API that feels like pandas but processes Kafka topics in real-time. Filter, transform, aggregate, window, and join directly in Python with native access to NumPy, scikit-learn, and PyTorch. If you're a Python developer building real-time data pipelines on Kafka, Quix Streams eliminates the language mismatch. Faust was the Python Kafka Streams alternative but it's abandoned and unreliable. kafka-python is a low-level client without processing primitives. Apache Flink's PyFlink works but wraps Java. Bytewax is the Rust-powered Python alternative. The catch: Quix Streams is focused on Kafka — no RabbitMQ, no Pulsar, no generic message broker support. At 1,500 stars, the community is small and Stack Overflow coverage is thin. The Streaming DataFrame API is convenient but less flexible than Flink's windowing for complex event processing. And for truly high-throughput workloads, the Python runtime is inherently slower than JVM-based Kafka Streams.

arc565★

Arc wraps DuckDB's SQL engine, Parquet storage, and Arrow format into a single Go binary that ingests 18M+ records per second and queries 6M+ rows per second. Your data lands in standard Parquet files on S3 or local disk — readable by ClickHouse, Snowflake, DuckDB, or anything else that speaks Parquet. No proprietary formats, no vendor lock-in. If you need a lightweight analytical database for observability, IoT, or product analytics without running a ClickHouse cluster, Arc is worth watching. QuestDB is the established time-series competitor (Arc claims 1.8x faster on ClickBench). TimescaleDB is the PostgreSQL-native option. ClickHouse is the heavyweight for production analytics at scale. The catch: Arc is brand new — 564 stars, AGPL-3.0 licensed, and from a startup nobody has heard of yet. Performance claims on benchmarks don't equal production reliability. The AGPL license means any modifications must be open-sourced, and network use triggers the copyleft. For anything production-critical, ClickHouse or QuestDB have years of battle-testing that Arc simply doesn't.