why aren't all 14 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 0 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Prediction Not checkable as stated
Stoica predicts big data infrastructure will eventually unify around one system
“Now, I do think that looking forward you are going to see more and more of this unification. This happens in many other industries and technologies. I do think this will happen in big data because it's so much easier if you have only one system than if you nee…”
Assertion Supported
Stoica: Apache Spark won the TerraSort benchmark processing data out of memory
“Just October last year, we had this we won this kind of TerraSort benchmark. And in those, in that benchmark, the data, it's not in memory. Right? It's SSDs and so forth.”
Assertion Supported
Stoica: Apache Spark supports all major data workloads with one engine
“While we spark, You can use only one engine and only one API to support all these workloads.”
Assertion Supported
Stoica: Hadoop's HDFS read/write cycle crippled early iterative machine learning
“If you look at the machine learning, it's, fundamentally, it's an iterative algorithm, and every iteration is turned into a Hadoop job. So between the iteration, you write the data and read the data from HDFS, so that's why it's very slow.”
Opinion
Stoica: Hadoop remains a very great batch processing engine
“Hadoop is still a very great, ah, batch engine.”
Assertion Not checkable as stated
Stoica: Spark Streaming and Storm have roughly similar throughput
“They are roughly, you know, in terms of the throughput, they are roughly similar.”
Assertion Not checkable as stated
Stoica: Spark Streaming micro-batching is unfit for high-frequency trading latency
“It's very hard to, you know, if you want millisecond latency from the time you, ah, the data entered in the system until you get the result, it's very hard to get. You can get latencies of several hundreds of milliseconds, but milliseconds, very hard. Ah, you,…”
Assertion Supported
Stoica: Apache Spark has exceeded 500 active open-source contributors
“We exceeded 500 contributors. It is the most active big data project right now, Spark.”
Assertion Supported
Stoica: Databricks still provides the majority of Apache Spark open-source contributions
“A lot of contributions, still the majority of contributions comes from Databricks.”
Assertion Supported
Stoica: Early Hadoop was limited to batch processing
“So at that point, in big data space we there was Hadoop just started, but of course that was, by, back then it was mostly, you know, batch, computation, so you could do historical analysis, but not much more than that.”
Disclosure
Stoica: Apache Spark was created for iterative machine learning and interactive queries
“And Spark was, ah, you know, we targeted first some workloads which are not covered by Hadoop, and from all this experience I mentioned earlier, we look at iterative, iterative computations to support machine learning, as well as interactive computation, right…”
Disclosure
Stoica: Databricks offers only cloud services, not a custom Spark distribution
“We don't have our own distribution. We provide only the service.”
Assertion Supported
Stoica: Berkeley's Succinct enables query processing on compressed data
“There is a related project that, ah, Berkeley is called, ah, succinct, ah, which actually go even more than, you know, beyond that. It's, ah it's a project that allows you to, you know, provide allow you to have, you know, query processing on the compressed da…”
Assertion Supported
Stoica: Apache Spark originally ran on Mesos before adding YARN and standalone support
“As originally was built to run on top of Mesos. Today is working on Yarn, working, you know, standalone, and is working also in addition to HDFS, you know, imports and exports data to many other data sources.”