Explore >> Select a destination
|
You are here 
|
www.analyticsvidhya.com | ||
| | | | |
www.onehouse.ai
|
|
| | | | | Learn how Apache Flink?, Apache Kafka? Streams, and Apache Spark? Structured Streaming stack up against each other in terms of engine design, development experience, and more. | |
| | | | |
dagshub.com
|
|
| | | | | Explore the top ML workflow and pipeline tools, including tools from Netflix, to enhance your data science projects' efficiency and impact. | |
| | | | |
timilearning.com
|
|
| | | | | In the first lecture of this series, I wrote about MapReduce as a distributed computation framework. MapReduce partitions the input data across worker nodes, which process data in two stages: map and reduce. While MapReduce was innovative, it was inefficient for iterative and more complex computations. Researchers at UC Berkeley invented Spark to deal with these limitations. | |
| | | | |
www.fivetran.com
|
|
| | | Automated data integration, centralized data storage, and governance underpin your data fabric. | ||
