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Memgraph 2.2. is released – graph database that supports streaming

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Memgraph 2.2. is released – graph database that supports streaming

A few days ago we have released a new version of Memgraph. Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications. It is powered by an in-memory graph database built to handle real-time use-cases at enterprise scale. Memgraph supports strongly-consistent ACID transactions and uses the standardized Cypher query language for structuring, manipulating, and exploring data. The main new things in Memgraph 2.2 are: - you can run Memgraph now on Apple M1 based computers - you can now use WebSocket support to push the logs - you can now configure Kafka streams using CONFIG and CREDENTIALS clauses - we have added a bunch of new built-in procedures You can take a look at our code on github [1] or you can go to our site[1] and download Memgraph. Please let us know what you like and what you don't like about it. Are there any features that you are missing? [1] https://github.com/memgraph/ [2] https://memgraph.com/

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, computer · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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