Startup Stash • Tools and resources for entrepreneurs Integrations Directory • Directory of integrations for your no-code product. One Page Love • Find inspiration from one-page websites Do Things That Don’t Scale • Collection of unscalable startup hacks NoCodeList • Software for your projects Page Flows • User design flow inspiration Stackshare • Find software for your projects and business Side Hustle... Source: over 1 year ago

One of the things you will need to think about at this stage of the project lifecycle is the tools you will use to power your business. Startup Stash is a directory of tools (both free and paid-for) that you can utilize at the start of your business journey. In addition to that check our directory of tools, that we’ve checked and used during our startup journey. - Source: dev.to / about 2 years ago

"Startup Stash - A Curated Directory of Tools and Resources for Your Startup" https://startupstash.com. Source: almost 3 years ago

Also useful (but not a book): https://startupstash.com/. Source: about 3 years ago

I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / 13 days ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 27 days ago

Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 2 months ago

Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago

Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 6 months ago