python-fake-data-producer-for-apache-kafka
Weaviate
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python-fake-data-producer-for-apache-kafka | Weaviate | |
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32 | 76 | |
76 | 9,524 | |
- | 5.7% | |
4.5 | 10.0 | |
8 months ago | 3 days ago | |
Python | Go | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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python-fake-data-producer-for-apache-kafka
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ElephantSQL Is Shutting Down
I had good experience with Aiven in the past, we needed something located in the EU: https://aiven.io/
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Crossplane: Streamline your infrastructure provisioning & management
Access to Aiven
- Google Cloud Spanner is now half the cost of Amazon DynamoDB
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Scale up: a MySQL bug story, or why Aiven works
One of the hardest questions we answer for our large enterprise customers is why they should choose Aiven instead of managing their own database and streaming services. It can seem counterintuitive that paying extra for a managed service can save you money. However, when we factor in economies of scale - particularly in regards to access to specialized knowledge and tooling - the case for managed services becomes clear. This was certainly the case for some of our MySQL clients earlier this year, where their investments in Aiven paid off in the form of a quietly managed bug fix.
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Flink CDC / alternatives
And Kafka + Kafka Connect has https://www.confluent.io/ https://aiven.io/ https://upstash.com/ (and not quite Kafka, but protocol-compatible, https://redpanda.com/)
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What are your favorite tools or components in the Kafka ecosystem?
Fake data utility - https://github.com/aiven/python-fake-data-producer-for-apache-kafka
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Do we have such a thing as Postgres Atlas?
For PostgreSQL, similar offerings are: * Google Cloud SQL * AWS RDS * Digital Ocean Postgres * Azure Database for Postgres * Aiven, Instaclustr etc
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Good database solution
Aiven - https://aiven.io/
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Why are we paying these folks - a tale of DevRel
Majority of companies layer DevRel on top of marketing as a sort of afterthought, and that’s usually a recipe for failure. All four co-founders at Aiven (the company where I work at) have been long-time open-source maintainers/contributors and highly value the work of DevRel. Similar examples can be seen at HashiCorp, where co-founder Armon Dadgar has been doing DevRel on the whiteboard since the early days of the company. Technical founders know the value of DevRel and know when to form a DevRel team. This is very different from bringing your first DevRel hire onboard and making them convince the leadership why the company needs DevRel in the first place. If your developer advocate needs to explain to the technical leadership the need for DevRel, that's a red flag for that individual and the company.
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Hetzner continues its growth in the US with a new location
I wonder when Aiven https://aiven.io/ (or something similar) will start supporting hetzner.
Weaviate
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
- FLaNK Stack 29 Jan 2024
- Qdrant, the Vector Search Database, raised $28M in a Series A round
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How to use Weaviate to store and query vector embeddings
In this tutorial, I introduce Weaviate, an open-source vector database, with the thenlper/gte-base embedding model from Alibaba, through Hugging Face's transformers library.
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Choosing vector database: a side-by-side comparison
This will be solved in Weaviate https://github.com/weaviate/weaviate/issues/2424
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Who's hiring developer advocates? (October 2023)
Link to GitHub -->
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Do we think about vector dbs wrong?
Hey @rvrs, I work on Weaviate and we are doing some improvements around increasing write throughput:
1. gRPC. Using gRPC to write vectors has had a really nice performance boost. It is released in Weaviate core but here is still some work on do on the clients. Feel free to get in contact if you would like to try it out.
2. Parameter tuning. lowering `efConstruction` can speed up imports.
3. We are also working on async indexing https://github.com/weaviate/weaviate/issues/3463 which will further speed things up.
In comparison with pgvector, Weaviate has more flexible query options such as hybrid search and quantization to save memory on larger datasets.
- Weaviate vector database
- Weaviate 1.21: Support for ImageBind and GPT4all and more
- Weaviate Vector Database
What are some alternatives?
kafka-connect-opensky - Kafka Source Connector reading in from the OpenSky API
Milvus - A cloud-native vector database, storage for next generation AI applications
OpenKP - Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural m
faiss - A library for efficient similarity search and clustering of dense vectors.
fake-data-producer-for-apache-kafka-docker - Fake Data Producer for Aiven for Apache Kafka® in a Docker Image
pgvector - Open-source vector similarity search for Postgres
Grafana - The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
jina - ☁️ Build multimodal AI applications with cloud-native stack
demo-scene - 👾Scripts and samples to support Confluent Demos and Talks. ⚠️Might be rough around the edges ;-) 👉For automated tutorials and QA'd code, see https://github.com/confluentinc/examples/
vald - Vald. A Highly Scalable Distributed Vector Search Engine