Snowflake
Google BigQuery
Amazon EMR
Databricks
Qubole
Amazon Redshift
Azure HDInsight
Google Cloud Dataflow
Apache Beam
Google Cloud Dataflow
Apache Airflow
Amazon EMR
Google BigQuery
Databricks
Qubole
Apache Spark
Snowflake| Categories |
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|---|---|
| Website | snowflake.com |
| Pricing URL | Official Snowflake Pricing |
| Details $ | - |
Apache Beam| Categories |
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|---|---|
| Website | beam.apache.org |
| Pricing URL | - |
| Details $ |
Based on our record, Apache Beam should be more popular than Snowflake. It has been mentiond 14 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Snowflake, a data warehousing company founded by ex-Oracle and ex-VectorWise experts, responded with a blog post that critically reviewed Databricks' findings, reported different results for the same benchmark, and claimed comparable price/performance to Databricks. - Source: dev.to / almost 2 years ago
Snowflake: Snowflake is fast, and works well as a product analytics database. - Source: dev.to / over 2 years ago
If you just go to snowflake.com you can sign up for a demo account for free for a month and I'm fairly certain you can get more than one of these accounts (I would recycle emails doing it all the time.) Once you have an account there's lots of docs and videos out there either using the Database via their UI or via python using their connector. They also have a pyspark connector but you might want to just learn... Source: over 2 years ago
Early stage funding & VCs clearly demarcate between tech companies and tech enabled companies. But, once the PE comes into the picture at the scale of BlackStone, the border between doordash.com and snowflake.com starts to blur. The motivation is to make some bucks by going to IPO and they know how to get it done. Source: over 2 years ago
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / 3 months ago
Apache Beam is one of many tools that you can use. Source: 4 months ago
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 1 year ago
Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / over 1 year ago
What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all. Source: over 1 year ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.
