@vishalmisra Yep. If others seeing their email. it’s ok. I will try again. Will send my email in DM if i cant get in. Dont want add more work for you. Thanks.
@vishalmisra@harshjain85 Love your work, both academic and with cricket. I didn’t know you were associated with dream11. Isn’t it essentially gambling (I have seen multiple people close to me losing lot of money in sports betting, before Dream11 though). Curious what your work involves with them.
@ryanssking1@venkmurthy I guess more data from the above database could validate a lot of theories here. E.g. how are the admission rates in STEM or humanities.
Happy to see Real-Time mode (ultra low latency mode) in Apache Spark in production having direct impact on customers. All this started as hackathon in Fall 2023 (with @entersudonym).
Real-Time Mode for Apache Spark Structured Streaming on Databricks is now generally available.
For ultra-low latency workloads, teams have historically needed to run separate, specialized engines like Apache Flink alongside Spark, duplicating codebases, governance, and operational overhead. RTM eliminates that by bringing millisecond-level latency to the Spark APIs you already use.
Industry-leading companies are already seeing results:
- @coinbase cut end-to-end latency by 80%+ while computing 250+ ML features on a unified Spark engine
- @DraftKings rebuilt fraud detection pipelines for live sports betting with latencies that weren't previously possible
- @makemytrip hit sub-50ms P50 latencies and saw a 7% lift in click-through rates
If you're already on Structured Streaming, a single config update is all it takes to get started. https://t.co/zp0F2HwshW
Customers are getting some amazing performance with Apache Spark Real-Time Mode! Try it out, just turn a SQL or DataFrame query to a streaming job! This was our-term vision for streaming of Spark since we first designed the API over Spark SQL, and it’s great to see it in action.
Still maintaining two engines for streaming? It's time to simplify.
Now you can get millisecond-level latency using the same Apache Spark APIs you already know.
Using Real-Time Mode (RTM) in Spark Structured Streaming, you get the best of both worlds:
• Ultra-low latency (millisecond-level p99s) on Spark
• A unified codebase using the familiar Spark APIs for all your streaming use cases
• Lower ops overhead by eliminating second-engine complexity
Whether it's fraud detection, instant personalization, or generating real-time context for your AI agents, using RTM to keep your stack unified is the ultimate productivity hack for data teams. https://t.co/1FjYfWYAPN
Magistral from @MistralAI gives a clue in its thoughts why these are all 27. Not sure whether to believe it but it might be based on the number of characters in the prompt. cc @karpathy
https://t.co/GTpbPEtwM9
1/24 I’m thrilled to share what my co-founder @gilad and I’ve been cooking up over the past year. Check out @yupp_ai – a fun and easy way for anyone to discover, compare and get the best answers across the latest AIs, all for free! Yes, even the most powerful pro models.
@AravSrinivas Recent form guide of a player, even more useful if we can see home and away forms separately. We can also ask specific questions to get to this, but will be cool to see by default.
Hey @confluentinc can you help delete my account from my ex-employer? I don’t have access to that email. I got a huge charge on credit for tiny cluster. Please contact me. Email to support didn’t help.