The inner join between sets of people who build databases, write, and draw? Low cardinality. I'm in that set, so I'm starting a blog!
Here's my first post: www.bitsxpages.com/p/frameworks...
Correct. The new site is a temporary one, more details will follow as we firm up our new plans.
wow. that's surprising!
Today marks SlateDB’s one year anniversary! It’s been a lot of fun. Thanks to @rohanpd.bsky.social @flaneur2024.bsky.social @almog.ai @vigneshc.bsky.social @paulbutler.org Jason Gustafson, David Moravek, and many others for joining the project. 😀
That looks super interesting. There seems to be a burst of innovation in this space of better developer abstractions on top of data systems recently.
I’m a sucker for a good cost calculator. This one is pretty slick. Lots of components contribute to cost in Kafka Streams.
Looking forward to it!
SlateDB 0.5.0 is out!
Features:
- Checkpoints
- Clones
- Read only client
- Split/merge database foundation
- TTL filtering on reads
- Last version with breaking byte format changes
By the numbers:
- 62 commits
- 2 new contributors
- 10 total contributors
github.com/slatedb/slat...
Related: super interesting discussion on stateful stream processing vs OLTP with @apurvamehta.com from @responsive.dev open.spotify.com/episode/1hsH... (start at 18:46)
I'm always excited to chat with @apurvamehta.com about what @responsive.dev is building.
Streaming and real time as terms are being constantly reinvented as the market needs change rapidly, and Apurva is one of the best to talk about that.
Check the conversation here: techontherocks.show/15
If, on the other hand, IDEs can build proprietary adjacent tooling that integrates into developer workflows, they may become the integration point, and consequently an aggregator.
Not sure what you mean by aggregator, but for me it's something that has a 2-sided network effect. I don't know if that applies to the IDEs: If 2 IDEs use the same foundation model, what's the structural barrier for one to copy the other over the medium term?
I’m excited to listen to this one. Some of my favorite folks all in one podcast 😀
This week, our 101 series has @ableegoldman.bsky.social diving deep into the world of performance optimization for Kafka Streams.
This isn't just theory: she shows how we realized 10x throughput improvements by implementing these techniques in prod!
www.responsive.dev/blog/perform...
New episode: Reinventing Stream Processing with @apurvamehta.com from @responsive.dev
We cover:
Real-time vs low latency in streaming
Why stateful streaming is hard at scale
RocksDB, Postgres limitations
SQL vs flexible APIs
Decoupling state & compute with SlateDB.
🎧 👉
Next entry in the Kafka Streams 101 series: application lifecycle.
Very beginner friendly, but even the pros might find a useful tip or two -- for example: have you heard of the new standby task listener?
Check it out: www.responsive.dev/blog/app-lif...
We're keeping the ball rolling on the Kafka Streams 101 series with yet another blog post! This time the focus is on configuration, and breaking down the most important configs to focus on for various needs (such as correctness, resiliency, etc)
Check it out here: www.responsive.dev/blog/importa...
We are happy to announce that we have completed another SOC2 Type 2 audit along with completing another successful penetration test against our cloud services.
You can find the latest reports on our trust center: trust.responsive.dev
wow. that's so cool. I never thought to use ChatGPT like that. It makes so much sense!
What’s a query example that ChatGPT helped with?
Most banks export your credit card transactions as CSV, with categorizations. I just load that into sheets and then make a chart out of it. Since most of the spending is on CC, it works pretty well.
(3/3) Application upgrades were by far the #1 requested topic when we asked what the community would like to learn about earlier this year.
We hope you find this latest post in our Kafka Streams 101 series helpful!
www.responsive.dev/blog/topolog...
(2/3) @ableegoldman.bsky.social has been helping the @kafkastreams.bsky.social community with application upgrades since what feels like the beginning of time. She has now put all of that practical knowledge in one blog post to help everyone reason about their upgrades and do them correctly.
(1/3) One of the hardest problems in the world of stream processing is upgrading stateful applications. Why? Because the events never stop, and your application has to handle the events correctly before and after a upgrade and if rewind your position in a topic.
Had two people ask me for guidance about open source foundations this week. Lots of trepidation around Apache, not much familiarity with CNCF, and curiousity about why I chose Commonhaus for SlateDB.
Here are my thoughts:
We mostly use those two terms interchangeably. However, a remote storage solution could also have a disaggregated storage architecture with multiple layers: in-memory, on disk, object store, etc.
#StreamProcessing with @kafkastreams.bsky.social
Definitely latency is the biggest issue. Our Async processor alleviates that concern completely though.
Managed RS3 is practical with and without BYOC because RS3 nodes are very light weight: Kafka is the WAL and S3 is the persistence layer.
www.responsive.dev/blog/async-p...
Should you use Kafka? If so, when? And what are the tradeoffs presented by the dizzying variety of Kafka-adjacent technologies? I hope that my latest blog post provides unique and useful answers to these questions.
Let me know what you all think!
👉 www.responsive.dev/blog/why-whe...
We are going to write more, but here is how RS3 works. Essentially, from Kafka Streams point of view, it's no different from using MongoDB or Cassandra as state, which is something our SDK already supports in production: docs.responsive.dev/storage/rs3
