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Based on our record, OctoSQL seems to be a lot more popular than Confluent. While we know about 23 links to OctoSQL, we've tracked only 1 mention of Confluent. 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.
Weโre going to setup a Kafka cluster using confluent.io, create a producer and consumer as well as enhance our behavior driven tests to include the new interface. Weโre going to update our helm chart so that the updates are seamless to Kubernetes and weโre going to leverage our observability stack to propagate the traces in the published messages. Source: over 3 years ago
This looks extremely cool. This is basically incremental view maintenance in databases, a problem that almost everybody (I think) has when using SQL databases and wanting to do some derived views for more performant access patterns. Importantly, they seem to support a wide breath of SQL operators, and it's open-source! There's already a bunch of tools in this area: 1. Materialize[0], which afaik is more... - Source: Hacker News / about 1 year ago
OctoSQL[0] or DuckDB[1] will most likely be much simpler, while going through 10 GB of JSON in a couple seconds at most. Disclaimer: author of OctoSQL [0]: https://github.com/cube2222/octosql. - Source: Hacker News / over 2 years ago
This is really cool! With their Postgres scanner[0] you can now easily query multiple datasources using SQL and join between them (i.e. Postgres table with JSON file). Something I strived to build with OctoSQL[1] before. It's amazing to see how quickly DuckDB is adding new features. Not a huge fan of C++, which is right now used for authoring extensions, it'd be really cool if somebody implemented a Rust extension... - Source: Hacker News / over 2 years ago
Congrats on the Show HN! It's great to see more tools in this area (querying data from various sources in-place) and the Lambda use case is a really cool idea! I've recently done a bunch of benchmarking, including ClickHouse Local and the usage was straightforward, with everything working as it's supposed to. Just to comment on the performance area though, one area I think ClickHouse could still possibly improve... - Source: Hacker News / over 2 years ago
SPyQL is really cool and its design is very smart, with it being able to leverage normal Python functions! As far as similar tools go, I recommend taking a look at DataFusion[0], dsq[1], and OctoSQL[2]. DataFusion is a very (very very) fast command-line SQL engine but with limited support for data formats. Dsq is based on SQLite which means it has to load data into SQLite first, but then gives you the whole breath... - Source: Hacker News / almost 3 years ago
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Materialize - A Streaming Database for Real-Time Applications
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
LNAV - The Log File Navigator (lnav) is an advanced log file viewer for the console.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Steampipe - Steampipe: select * from cloud; The extensible SQL interface to your favorite cloud APIs select * from AWS, Azure, GCP, Github, Slack etc.